Name: Critical Thinking
Price: 395 GBP
Availability: InStock

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			<p><span>Want to master basic algebra? Engineering, physics, pharmaceuticals and many other industries require excellent numerical skills, so it's important to know your algebra if you want to work in these fields. This Build Your Algebra Fundamentals (New version) Course will help you gain fundamental practical skills and help you reach a higher level of learning, whether you're a student or professional.</span></p>
<p><span>This essential algebra course will train you to develop your critical thinking skills, so you can become a master at problem-solving and logical reasoning. Even if you have little or no knowledge of the subject, in just a few hours, you'll be able to tackle more advanced algebra equations and simplify equations with ease. You'll explore all levels of algebra, including common algebraic terminology, and will get the chance to tackle beginner and advanced problems.</span></p>
<p><span>On course completion, you'll have the confidence to solve simple and more complex algebraic equations, with the ability to apply your newfound skills in the workplace.</span></p>
<h3><strong>Highlights of this Build Your Algebra Fundamentals (New version) Course</strong></h3>
<ul>
<li><span>Familiarise with basic algebraic expressions and concepts</span></li>
<li><span>Learn how to multiply and divide algebraic expressions</span></li>
<li><span>Understand how to expand and simplify brackets</span></li>
<li><span>Solve linear equations and inequalities with ease</span></li>
<li><span>Expand your knowledge of algebraic identities</span></li>
<li><span>Get an overview of polynomials in abstract algebra </span></li>
<li><span>Familiarise with the coordinate plane and the axis of symmetry</span></li>
</ul>
<h3><strong>What you'll learn</strong></h3>
<ul>
<li><span>Higher Indices - Laws of Indices (Exponent)</span></li>
<li><span>Formula - Change the subject of formula</span></li>
<li><span>Rational Expressions - Simplification of Algebraic Fractions to its lowest form</span></li>
<li><span>BODMAS - Adding and removing brackets in algebraic expressions</span></li>
<li><span>Graphs - Coordinate Axis, Points and Line Graph</span></li>
<li><span>Linear equations in one variable and word problems</span></li>
<li><span>Linear Inequalities</span></li>
<li><span>Simultaneous linear equations- Graphical method, Substitution method, Equating coefficient &amp; cross multiplication method</span></li>
<li><span>Graphical method of solving simultaneous linear equations</span></li>
<li><span>Word problems with the help of simultaneous linear equations</span></li>
<li><span>Quadratic equations using Factorization method and Quadratic Formula</span></li>
<li><span>Quadratic equations using squaring complete method</span></li>
<li><span>Equations reducible to quadratic equations</span></li>
<li><span>Word problems of Quadratic equations</span></li>
<li><span>Quadratic polynomials</span></li>
<li><span>Knowledge of nature of roots of quadratic equations</span></li>
<li><span>Zeros of polynomials Î±, Î² &amp; Î³</span></li>
<li><span>Addition, Subtraction,Multiplication and Division of Algebraic Expressions</span></li>
<li><span>Remainder Theorem &amp; Factor Theorem</span></li>
<li><span>Directed Numbers (Integers)</span></li>
<li><span>Finding Numerical Value of Algebraic Expressions</span></li>
<li><span>Factorization Techniques like common factors, regrouping , splitting the middle term and using identities</span></li>
<li><span>Algebraic Identities like ( a - b ) Â² , ( a + b ) Â³ , a Â³ - b Â³ , ( a + b + c ) Â² etc</span></li>
</ul>
<h3><strong>Requirements</strong></h3>
<div>
<ul>
<li>Knowledge of Mathematics till 5th grade</li>
</ul>
</div><table>				<tbody><tr>
					<td colspan="4">Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 1 Introduction</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Fundamental concepts on Algebraic Expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 2 What is Algebra</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 3 Simple Equations</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 4 What are Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 5 Terms in Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 6 Degree of Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 7 Writing statements to algebraic form</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Operations on Algebraic Expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 8 Integers and common mistakes in solving integers</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 9 Arrangement of Terms</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 10 Powers on integers</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture11 Simplification using BODMAS</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 12 Distributive Properties in Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 13 Simplify Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 14 Additions of Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 15 Subtractions of Polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Indices ( Exponents)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 16 The rules of Indices in algebra</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 17 Fractional indices</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 18 Understanding indices (practice questions)</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 19 Problems from IGCSE Last year papers</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Multiplication and Division of Algebraic expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 20 Multiplication of monomial to Polynomial</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 21 Multiplication of Polynomial by Polynomial</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 22 Division of algebraic expression by a monomial</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 23 Division of algebraic expression by another polynomial</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 24 Division of a polynomial by another polynomial with remainder</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Brackets in Algebra</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 25 Rules of brackets</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 26 Simplification by removing brackets</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Linear equations in one variable</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 27 Simplification of algebraic fractions</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 28 Rules to solve linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 29 Solving linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 30 Solving complex linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 31 Word problems on linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Algebraic Identities</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 32 What are Identities?</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 33 Identity ( a + b ) Â²</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 34 Identity ( a - b ) Â² new</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 35 Identity aÂ² - bÂ² = (a-b) (a +b ) new</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 36 -- Standard Identities ( a + b + c ) Â² = a Â² + b Â² + c Â² + 2 a b + 2 a c +2 b c old</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 37 Identity (x + a) (x + b) Identity Derivation &amp; Application new</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 38 Pascal's Triangle _ Identity ( a + b ) Â³ new</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 39 Identities( a - b ) Â³, ( a Â³ + b Â³) and (a Â³ - b Â³) new</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 40 - Standard Identities a Â³ + b Â³ + c Â³ - 3 a b c</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Formula : Change of subject of formula</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 41 -Changing the subject of formula</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Linear Inequalities</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 42 - Linear Inequalities</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Resolve into factors</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 43 - Factorization by taking out common factor</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 44 - Factorization by grouping the terms</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 45 - factorize using identity a Â² - b Â²</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 46 - factorize using identity (a + b )Â² and (a - b )Â² (2)</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 47 - factorize using identity ( a + b + c ) Â²</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 48 - factorization by middle term split</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Algebraic Fractions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 49 -Simplification of algebraic fractions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Coordinate axis - points and Line graph</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 50 All that you need to know about co ordinate axis</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 51 Some important facts needed to draw line graph</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 52 - How to draw a line graph on coordinate plane</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 53 Drawing line graphs</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">System of simultaneous linear equations in two variables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 54 Simultaneous Linear Equations in two variables- intro</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 55 Graphical method of solving linear equations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 56 Graphical method - more problems</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 57 Method of Elimination by substitution</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 58 Method of Elimination by Equating coefficients</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 59 Method of Elimination by cross multiplication</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 60 Equations reducible to simultaneous linear equations</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 61 Word Problems on Linear equations</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Polynomials</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 62 Polynomials and Zeros of polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 63 Remainder Theorem</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 64 Factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 65 Practice problems on Remainder and Factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 66 Factorization using factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Quadratic Polynomials</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 67 Zeros of polynomials Î±, Î² &amp; Î³</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 68 Relation between zeros and coefficients of a polynomials</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 69 Finding polynomials if zeros are known</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 70 Practice problems on zeros of polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 71Problems solving with Î± and Î² (part 1)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 72 Problems solving with Î± and Î² (part 2)</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Quadratic Equations</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture73 what are Quadratic equations</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 74 Solutions by factorization method</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 75 Solutions by completing square formula</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 76 Deriving Quadratic formula</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 77 Practice problems by Quadratic formula</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 78 Solving complex quadratic equations by Quadratic Formula</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 79 Solutions of reducible to Quadratic Formula</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 80 Skilled problems on Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 81 Exponential problems reducible to Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 82 Nature of Roots of Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 83 Word problems on quadratic Equations Part 1</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 84 Word problems on quadratic Equations Part 2</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
				</tbody></table>

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Build Your Algebra Fundamentals (New version)

General Admission

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(52, 58, 64); box-sizing: border-box;" id="isPasted" fr-original-style="color: rgb(52, 58, 64);">The APM Project Fundamentals Qualification is for everyone wishing to demonstrate a fundamental awareness of project management and become familiar with knowledge from the APM Body of Knowledge 7th edition.</span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(52, 58, 64); box-sizing: border-box;" fr-original-style="color: rgb(52, 58, 64);"><br fr-original-style="" style="box-sizing: border-box;"></span></p><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h3 style="color: rgb(108, 117, 125); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(108, 117, 125);">About this course</h3><hr style="color: rgb(108, 117, 125); border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;" fr-original-style=" color: rgb(108, 117, 125);"><ul style="color: rgb(108, 117, 125); box-sizing: border-box;" fr-original-style=" color: rgb(108, 117, 125);"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;">&pound;295.00</li><li fr-original-style="" style="box-sizing: border-box;">95 lessons</li><li fr-original-style="" style="box-sizing: border-box;">Accredited training</li><li fr-original-style="" style="box-sizing: border-box;">Certificate of completion included</li></ul><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course curriculum</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;">Introduction<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Course Introduction</li><li fr-original-style="" style="box-sizing: border-box;">APM Tutor Bot</li></ul></li><li fr-original-style="" style="box-sizing: border-box;">Module 1<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 1 - What is a Project?</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 2 - Projects and Business-as-usual</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 3 - The Iron Triangle</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 4 - The Context of Project Management</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 5 - Projects, Programmes and Portfolios</li><li fr-original-style="" style="box-sizing: border-box;">Test Your Knowledge</li></ul></li><li fr-original-style="" style="box-sizing: border-box;">Module 2<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 1 - What is a Project Life Cycle</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 2 - Linear Project Life Cycles</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 3 - Iterative Project Life Cycles</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 4 - Hybrid Project Life Cycles</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 5 - Extended Project Life Cycles</li><li fr-original-style="" style="box-sizing: border-box;">Test Your Knowledge Quiz</li></ul></li><li fr-original-style="" style="box-sizing: border-box;">Module 3<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Assessment Criteria</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 1 - Why do we need to define roles and responsibilities in a project?</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 2 - The Project Sponsor</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 3 - Temporary Structures</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 4 - Project Governance</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 5 - Project Support</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 6 - The Project Team</li><li fr-original-style="" style="box-sizing: border-box;">End of Session</li><li fr-original-style="" style="box-sizing: border-box;">Test Your Knowledge Quiz</li></ul></li><li fr-original-style="" style="box-sizing: border-box;">Module 4<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Assessment Criteria</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 1 - Project Management Planning</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 2 - Integrated Planning</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 3 - Redeployment Baseline</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 4 - Deployment Baseline Reviews and Approvals</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 5 - Deployment Baselines in Linear and Iterative Projects</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 6 - The Business Case</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 7 - Stakeholder Engagement</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 8 - Stakeholder Identification and Analysis</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 9 - Success and Benefits</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 10 - Project Estimation</li><li fr-original-style="" style="box-sizing: border-box;">Lesson 11 - Progress Reporting</li><li fr-original-style="" style="box-sizing: border-box;">Lesson - End of Session</li><li fr-original-style="" style="box-sizing: border-box;">Test Your Knowledge Quiz</li></ul></li><li fr-original-style="" style="box-sizing: border-box;">Module 5<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Assessment Criteria</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 1 - What is Scope Management?</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 2 - How do we Scope a Project?</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 3 - Scoping in Linear and Iterative Projects</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 4 - Project Breakdown Structures</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 5 - Governance Principles</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 6 - Change Control</li></ul><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lesson 7 - Configuration Management</li></ul></li><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Test Your Knowledge Quiz</li></ul></ul><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div><div fr-original-style="" style="box-sizing: border-box;">Career Smarter acknowledges that APM owns all intellectual property rights in &lsquo;APM&rsquo; and &lsquo;Association for Project Management&rsquo; and logos used in connection with APM&rsquo;s activities. All rights reserved.</div>

Career Smarter

APM Project Fundamentals Qualification (PFQ)

<div>
					<div>
								<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
							</div>
				</div>
			<p><span>Algebra is one of the most common and malleable types of mathematics, and it is also one of the most significant since primary algebra used by electricians, engineers, and nearly everyone in between. </span></p>
<p><span>This </span><b>Pefect your Algebra Fundamentals </b><span>is intended for individuals with no prior knowledge of Algebra. This course includes all the fundamental concepts of Algebra, and each step-by-step arranged modules will explain topics in a mild and an approachable manner. You will understand the basic terminology of Algebra, following with finding the numerical value of Algebraic expressions, addition, subtraction, multiplication and division of Algebraic expressions, directed numbers, higher indices, use of brackets in Algebra and many more.</span></p>
<p><span>To sum up, theories explained in an interactive and practical format and then further demonstrated with questions to ensure you have a good understanding of the topics by the end of this course.</span></p>
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<h3><strong>What Will I Learn?</strong></h3>
<ul>
<li><span>Apply laws of Indices ( Exponents) on algebraic expressions.</span></li>
<li>Algebraic Identities used in algebra and their application like ( a - b ) Â² , ( a + b ) Â³ , a Â³ - b Â³ , ( a + b + c ) Â² etc</li>
<li>Factorize using common factors, regrouping , splitting the middle term, using identity aÂ² - bÂ² , (a+b)Â² , (a+b)Â² ,a Â³ + b Â³ + c Â³ - 3 a b c etc</li>
<li><span>Solve all types of Linear equations in one variable</span></li>
<li><span>Word problems based on linear equations</span></li>
<li><span>Knows about adding and removing brackets in algebraic expressions</span></li>
<li><span>Change the subject of formula</span></li>
<li><span>simplify fractions with denominators algebraic expression and bring them to its lowest form</span></li>
<li><span>Add , subtract , multiply and divide any algebraic expression</span></li>
<li><span>Divide one polynomial by another by long division method</span></li>
<li><span>Find value of any algebraic expression when value of variable is known</span></li>
<li><span>Fully familiar with rarely used identity a Â³ + b Â³ + c Â³ - 3 a b c</span></li>
<li><span>Learn to draw line graph</span></li>
<li><span>Solve Linear Inequalities</span></li>
<li><span>Able to solve all the problems of simultaneous linear equations by applying different methods</span></li>
<li><span>Able to solve linear equations with 1/2 variables graphically</span></li>
<li><span>Able to solve real world problems with the help of simultaneous linear equations</span></li>
<li><span>Solve Quadratic equations using Factorization method and Quadratic Formula</span></li>
<li><span>Solve Quadratic using squaring complete method</span></li>
<li><span>Solve all types of complex Quadratic equations and reducible to quadratic equation</span></li>
<li><span>Knowledge of nature of roots of quadratic equations</span></li>
<li><span>Learn to solve different types of word problems on Quadratic equations</span></li>
</ul>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div>
<div>
<div>
<div>
<h3><strong>Requirements</strong></h3>
<div>
<ul>
<li>Knowledge of Mathematics till 5th grade</li>
</ul>
<h3><strong>Who is the target audience?</strong></h3>
<ul>
<li>GMAT , GRE and MBA entrance exams students looking for revision of Algebra fundamentals</li>
<li>Wants to brush up basics of algebra in Mathematics</li>
<li>Current IGCSE students because course is designed to cover topics of Algebra</li>
<li>Current Algebra students of CBSE , ICSE board .</li>
<li>Middle school, High school or early college level students</li>
<li>If Algebra is always trouble for you then this course is specially for you as it will teach from very basics to in depth knowledge giving lots of practice through solving problems</li>
<li>Students who wants to learn all types of factorisation especially middle term split</li>
<li>High school students who have gaps in their knowledge and would like to fill them with basics</li>
</ul>
</div>
</div>
</div>
</div>
</div><table>				<tbody><tr>
					<td colspan="4">Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 1 Intro video Algebra Introduction final</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Fundamental concepts on Algebraic Expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 2 Terminology used in Algebra</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 3 Language of Algebra</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 4 Practice Questions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 5 Finding numerical value of an algebraic expression</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Operations on Algebraic Expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 6 Revision of Directed number ( integers</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 7 Addition and subtraction of monomial expressions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 8 Addition of algebraic expressions with many terms</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 9 Subtraction of algebraic expressions</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Indices ( Exponents)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 10 The rules of Indices in algebra</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 11 Fractional indices</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 12 Understanding indices (practice questions)</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 13 Problems from IGCSE Last year papers</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Multiplication and Division of Algebraic expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 14 Multiplication of monomial algebraic expressions</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 15 Multiplication of monomial with binomials and trinomials</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 16 Division of algebraic expression by a monomial</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 17 Division of algebraic expression by another polynomial</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 18 Division of a polynomial by another polynomial with remainder</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Brackets in Algebra</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 19 Rules of brackets</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 20 Simplification by removing brackets</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Linear equations in one variable</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 21 Simplification of algebraic fractions</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 22 Rules to solve linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 23 Solving linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Pefect your Algebra Fundamentals</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 25 Word problems on linear equations in one variable</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Algebraic Identities</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 26 Standard Identities (a + b )Â² and (a - b )Â² identities</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 27 Standard Identity ( a - b ) ( a + b) = a Â² - b Â²</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 28 Standard Identities ( a + b + c ) Â² = a Â² + b Â² + c Â² + 2 a b + 2 a c +2 b c</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 29 Standard Identities ( a + b ) Â³ and ( a - b ) Â³</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 30 Standard Identities a Â³ + b Â³ and a Â³ - b Â³</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 31 Standard Identities a Â³ + b Â³ + c Â³ - 3 a b c</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Formula : Change of subject of formula</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 32 -Changing the subject of formula</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Linear Inequalities</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 33 Linear Inequalities</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Resolve into factors</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 34 Factorization by taking out common factor</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 35 Factorization by grouping the terms</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 36 Factorize using identity a Â² - b Â²</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 37 Factorize using identity (a + b )Â² and (a - b )Â²</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 38 Factorize using identity ( a + b + c ) Â²</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 39 Factorization by middle term split</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Algebraic Fractions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 40 Simplification of algebraic fractions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Coordinate axis - points and Line graph</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 41 All that you need to know about co ordinate axis</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 42 Some important facts needed to draw line graph</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 43 How to draw a line graph on coordinate plane</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 44 Drawing line graphs</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">System of simultaneous linear equations in two variables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 45 Simultaneous Linear Equations in two variables- intro</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 46 Graphical method of solving linear equations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 47 Graphical method - more sums</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 48 Method of Elimination by substitution</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 49 Method of Elimination by Equating coefficients</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 50 Method of Elimination by cross multiplication</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 51 Equations reducible to simultaneous linear equations</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 52 Word Problems on Linear equations</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Polynomials</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 53 Polynomials and Zeros of polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 54 Remainder Theorem</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 55 Factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 56 Practice problems on Remainder and Factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 57 Factorization using factor Theorem</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Quadratic Polynomials</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 58 Zeros of polynomials Î±, Î² &amp; Î³</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 59 Relation between zeros and coefficients of a polynomials</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 60 Writing polynomials if zeros are given</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 61 Practice problems on zeros of polynomials</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 62 Problems solving with Î± and Î² (part 1)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 63 Problems solving with Î± and Î² (part 2)</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Quadratic Equations</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 64 what are Quadratic equations</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 65 Solutions by factorization method</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 66 Solutions by completing square formula</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 67 Deriving Quadratic formula</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 68 Practice problems by Quadratic formula</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 69 Solving complex quadratic equations by Quadratic Formula</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 70 Solutions of reducible to Quadratic Formula</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 71 Skilled problems on Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 72 Exponential problems reducible to Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 73 Nature of Roots of Quadratic Equations</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 74 Word problems on quadratic Equations Part 1</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lecture 75 Word problems on quadratic Equations Part 2</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>lecture 76 word problems on Quadratic</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Mock Exam</td>
				</tr>
								<tr>
					<td colspan="4">Final Exam</td>
				</tr>
				</tbody></table>

Arithmetic

Pefect your Algebra Fundamentals

<p>As you walk into a world full of data, your mind starts to churn, exploring the infinite possibilities of extracting insights from these vast amounts of information. You can feel the excitement building within you as you picture yourself becoming a data scientist, solving real-world problems using machine learning algorithms. With the "Learn Machine Learning with R" course, you'll be taking the first step towards becoming a master of machine learning.</p><p>The course comprises two sections, each meticulously crafted to help you understand the concepts of machine learning, starting from the very basics. You will learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. You'll also explore multiple linear regression, dummy variable concepts, and predictions over the year. Furthermore, you'll dive into the workings of the model and the backward elimination approach. You will be able to use R to analyse datasets, develop predictive models, and extract valuable insights from them.</p><p>This course is suitable for anyone who wants to understand the basics of machine learning and its implementation using R. Whether you are a beginner or an experienced programmer, this course will help you improve your skills and gain an in-depth understanding of machine learning concepts.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the basics of machine learning and its implementation using R.</li><li>Develop the skills to build simple and multiple linear regression models.</li><li>Learn how to use R to analyse datasets and develop predictive models.</li><li>Understand the concept of dummy variables and the backward elimination approach.</li><li>Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data.</li></ul><p>If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R.</p><p><strong>â±â± Learn Machine Learning with R</strong><strong> Course Curriculum </strong></p><p><strong>Section 01: Linear Regression and Logistic Regression</strong></p><ul><li>Working on Linear Regression</li><li>Equation</li><li>Making the Regression of the Algorithm</li><li>Basic Types of Algorithms</li><li>predicting the Salary of the Employee</li><li>Making of Simple Linear Regression Model</li><li>Plotting Training Set and Work</li></ul><p><strong>Section 02: Understanding Dataset</strong></p><ul><li>Multiple Linear Regression</li><li>Dummy Variable Concept</li><li>Predictions Over Year</li><li>Difference Between Reference Elimination</li><li>Working of the Model</li><li>Working on Another Dataset</li><li>Backward Elimination Approach</li><li>Making of the Model with Full and Null</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Students or professionals looking to develop their data analysis and machine learning skills.</li><li>Individuals interested in pursuing a career in data science or machine learning.</li><li>Anyone interested in understanding how to extract insights from data.</li><li>Programmers looking to learn machine learning implementation using R.</li><li>Beginners interested in learning the basics of machine learning.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data analyst: £30,000 to £50,000</li><li>Machine learning engineer: £45,000 to £85,000</li><li>Data scientist: £40,000 to £80,000</li><li>Business analyst: £30,000 to £55,000</li><li>Research analyst: £25,000 to £45,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Learn Machine Learning with R Course

Packt

Python

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

Natural Language Processing with Real-World Projects

<p>algebraic concepts. This course covers a range of topics, including operations on algebraic expressions, linear equations, quadratic polynomials, algebraic fractions, and more. With a focus on real-world applications, this course is perfect for students who want to apply algebraic concepts in their everyday lives.</p><p>In this course, you will learn fundamental concepts of algebraic expressions and how to perform operations on them. You will also learn about indices, multiplication and division of algebraic expressions, and brackets in algebra. Additionally, you will study linear equations in one variable, algebraic identities, formulas, and linear inequalities. You will also learn how to resolve algebraic expressions into factors, work with algebraic fractions, and plot points and line graphs on a coordinate axis. Finally, you will explore quadratic polynomials, quadratic equations, and systems of simultaneous linear equations in two variables.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Apply algebraic concepts in real-world scenarios.</li><li>Perform operations on algebraic expressions and solve linear equations.</li><li>Work with quadratic polynomials and quadratic equations.</li><li>Plot points and line graphs on a coordinate axis.</li><li>Understand and work with algebraic fractions.</li><li>Solve systems of simultaneous linear equations in two variables</li></ul><p>The Algebra Fundamentals course is an excellent opportunity for students to develop their algebraic skills and gain a deep understanding of fundamental concepts. By taking this course, you will learn how to perform operations on algebraic expressions and solve linear equations, quadratic polynomials, and systems of simultaneous linear equations in two variables. You will also develop your ability to plot points and line graphs on a coordinate axis and work with algebraic fractions.</p><p>This course is ideal for students who want to pursue careers in engineering, science, economics, or any other field that requires a strong foundation in algebraic concepts. By taking this course, you will gain the necessary knowledge and skills to succeed in these fields and excel in your future academic and professional pursuits. With engaging lessons and real-world applications, this course is perfect for students of all backgrounds and skill levels.</p><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The course is ideal for highly motivated individuals or teams who want to enhance their professional skills and efficiently skilled employees.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The Algebra Fundamentals course provides you with the ability to explore different job opportunities across various industries with positions and pay scales such as upon completion of the course:</p><ul><li>Financial Analyst - £35,000-£65,000</li><li>Data Analyst - £25,000-£45,000</li><li>Market Research Analyst - £20,000-£40,000</li><li>Mechanical Engineer - £26,000-£50,000</li><li>Economist - £30,000-£70,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Algebra Fundamentals Course

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.</strong>
        <p>TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning.

In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization.

By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning.</p>
        <h2>What You Will Learn</h2><p>Understand what machine learning is<br>Build linear models with TensorFlow 2<br>Learn how to build deep neural networks with TensorFlow 2<br>Learn how to perform image classification and regression with ANN<br>Learn loss functions such as mean-squared error and cross-entropy loss<br>Learn about stochastic gradient descent, momentum, and Adam optimization</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers.<br><br>One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build artificial neural networks. The course is well-balanced with theory that explains the ANN concepts and hands-on coding exercises for practical understanding.</p>
        <h2>Key Features</h2><p>Understand the utilization of TensorFlow 2 to construct artificial neural networks * The course covers the basics of machine learning, classification, and regression * Explore the connection between artificial neural networks and biological neural networks</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Machine Learning and Neurons</h4><p>In this section, we will cover a crash course on machine learning and neurons. You will learn about classification and regression, and their TensorFlow implementations.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Machine Learning?</p><p style='margin: 0'>In this video, we will understand what machine learning is and get a clear idea about ML (machine learning).</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (Classification Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear classification for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification Notebook</p><p style='margin: 0'>In this video, we will understand linear classification with the help of breast cancer dataset example.</p></td></tr><tr><td style='padding: 15px;'><p>4. Code Preparation (Regression Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear regression for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression Notebook</p><p style='margin: 0'>In this video, we will understand linear regression by proving the Mosse law true.</p></td></tr><tr><td style='padding: 15px;'><p>6. The Neuron</p><p style='margin: 0'>In this video, we will understand the neuron.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Does a Model 'Learn'?</p><p style='margin: 0'>In this video, we will understand how a model 'learns'.</p></td></tr><tr><td style='padding: 15px;'><p>8. Making Predictions</p><p style='margin: 0'>In this video, we will be talking about another important part of creating a model, which is making predictions.</p></td></tr><tr><td style='padding: 15px;'><p>9. Saving and Loading a Model</p><p style='margin: 0'>In this video, you will learn how to save a model and then load it later.</p></td></tr><tr><td style='padding: 15px;'><p>10. Why Keras?</p><p style='margin: 0'>In this video, we will understand why we use Keras in this course and not TensorFlow 2.</p></td></tr><tr><td style='padding: 15px;'><p>11. Suggestion Box</p><p style='margin: 0'>In this video, we will have a look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Feedforward Artificial Neural Networks</h4><p>In this section, we will cover feedforward artificial neural networks.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Artificial Neural Networks Section Introduction</p><p style='margin: 0'>In this video, we will get introduced to artificial neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forward Propagation</p><p style='margin: 0'>In this video, you will learn about forward propagation as it is related to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Geometrical Picture</p><p style='margin: 0'>In this video, we will understand the geometrical picture by understanding why neural networks are so important instead of a single neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Activation Functions</p><p style='margin: 0'>In this video, you will learn about activation functions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Multiclass Classification</p><p style='margin: 0'>In this video, you will learn about multiclass classification.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Represent Images</p><p style='margin: 0'>In this video, we will discuss data and understand how to represent images.</p></td></tr><tr><td style='padding: 15px;'><p>7. Code Preparation (Artificial Neural Networks)</p><p style='margin: 0'>In this video, we will work with the MNIST dataset and ANN code preparation.</p></td></tr><tr><td style='padding: 15px;'><p>8. ANN for Image Classification</p><p style='margin: 0'>In this video, we will work on image classification with the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>9. ANN for Regression</p><p style='margin: 0'>In this video, we will understand regression in ANN.</p></td></tr><tr><td style='padding: 15px;'><p>10. How to Choose Hyperparameters</p><p style='margin: 0'>In this video, we will understand how to choose hyperparameters.</p></td></tr></tbody></table><br><h4>4. In-Depth: Loss Functions</h4><p>In this optional section, we will dive deeper into loss functions.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mean Squared Error</p><p style='margin: 0'>In this video, we will understand MSE (Mean Squared Error) from a probabilistic perspective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Binary Cross Entropy</p><p style='margin: 0'>In this video, you will learn about binary cross entropy, which is the correct loss function to use for binary classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Categorical Cross Entropy</p><p style='margin: 0'>In this video, you will learn about categorical cross entropy, which is used in multiclass classification.</p></td></tr></tbody></table><br><h4>5. In-Depth: Gradient Descent</h4><p>In this optional section, we will dive deeper into gradient descent.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Gradient Descent</p><p style='margin: 0'>In this video, we will get introduced to gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>2. Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about stochastic gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>3. Momentum</p><p style='margin: 0'>In this video, you will learn about momentum.</p></td></tr><tr><td style='padding: 15px;'><p>4. Variable and Adaptive Learning Rates</p><p style='margin: 0'>In this video, we will discuss variable and adaptive learning rates.</p></td></tr><tr><td style='padding: 15px;'><p>5. Adam Optimization (Part 1)</p><p style='margin: 0'>In this video, we will talk about Adam optimization, get introduced to Adam, and understand its basics.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adam Optimization (Part 2)</p><p style='margin: 0'>In this video, we will understand more about Adam optimization.</p></td></tr></tbody></table>

Data Science

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Deep Learning - Artificial Neural Networks with TensorFlow

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for individuals who want to become more rational and disciplined thinkers.</p>Overview<p>Upon successful completion of this course, students will boost their performance with an increase in their decision-making ability.</p><p>In this course, students will learn develop their ability to analyze and evaluate information.</p></div><div id="course-pills-2"><h3>1 - GETTING STARTED</h3> <ul> <li>Icebreaker</li> <li>Housekeeping Items</li> <li>The Parking Lot</li> <li>Workshop Objectives</li> </ul> <h3>2 - COMPONENTS OF CRITICAL THINKING</h3> <ul> <li>Applying Reason</li> <li>Open Mindedness</li> <li>Analysis</li> <li>Logic</li> <li>Case Study</li> </ul> <h3>3 - NON-LINEAR THINKING</h3> <ul> <li>Step Out of Your Comfort Zone</li> <li>Don't Jump to Conclusions</li> <li>Expect and Initiate Change</li> <li>Being Ready to Adapt</li> <li>Case Study</li> </ul> <h3>4 - LOGICAL THINKING</h3> <ul> <li>Ask the Right Questions</li> <li>Organize the Data</li> <li>Evaluate the Information</li> <li>Draw Conclusions</li> <li>Case Study</li> </ul> <h3>5 - CRITICAL THINKERS (I)</h3> <ul> <li>Active Listening</li> <li>Be Curious</li> <li>Be Disciplined</li> <li>Be Humble</li> <li>Case Study</li> </ul> <h3>6 - CRITICAL THINKERS (II)</h3> <ul> <li>Seeing the Big Picture</li> <li>Objectivity</li> <li>Using Your Emotions</li> <li>Being Self-Aware</li> <li>Case Study</li> </ul> <h3>7 - EVALUATE INFORMATION</h3> <ul> <li>Making Assumptions</li> <li>Watch out for Bias</li> <li>Ask Clarifying Questions</li> <li>SWOT Analysis</li> <li>Case Study</li> </ul> <h3>8 - BENEFITS OF CRITICAL THINKING</h3> <ul> <li>Being More Persuasive</li> <li>Better Communication</li> <li>Better Problem Solving</li> <li>Increased Emotional Intelligence</li> <li>Case Study</li> </ul> <h3>9 - CHANGING YOUR PERSPECTIVE</h3> <ul> <li>Limitations of Your Point of View</li> <li>Considering Others Viewpoint</li> <li>Influences on Bias</li> <li>When New Information Arrives</li> <li>Case Study</li> </ul> <h3>10 - PROBLEM SOLVING</h3> <ul> <li>Identify Inconsistencies</li> <li>Trust Your Instincts</li> <li>Asking Why?</li> <li>Evaluate the Solution(s)</li> <li>Case Study</li> </ul> <h3>11 - PUTTING IT ALL TOGETHER</h3> <ul> <li>Retaining Your New Skills</li> <li>Reflect and Learn From Mistakes</li> <li>Always Ask Questions</li> <li>Practicing Critical Thinking</li> <li>Case Study</li> </ul> <h3>12 - WRAPPING UP</h3> <ul> <li>Words from the Wise</li> <li>Review of Parking Lot</li> <li>Lessons Learned</li> <li>Completion of Action Plans and Evaluations</li> </ul></div>

Nexus Human

Creative Thinking

Study Skills

Critical Thinking

Analytical Skills

Strategic Thinking

Continuous Professional Development (CPD)

<p>Did you know that businesses that use linear programming techniques experience an average increase in productivity of 10-15%? With statistics like these, it's no wonder that linear programming is becoming an increasingly important skill in the world of business.</p> <p>Our Level 3, 5 &amp; 7 Linear Programming course is designed to give learners a comprehensive understanding of linear programming, from the basics to advanced techniques. With this course, you will be equipped with the knowledge and skills to help businesses improve their productivity and profitability.</p> <p>What sets our course apart is that it is endorsed by the Quality Licence Scheme (QLS), an independent accrediting body that ensures the quality of our courses. Upon completion, learners will receive a<strong> Level 7 Endorsed certificate</strong> hardcopy free of charge, which is recognised by employers worldwide.</p> <p>But that's not all - we are also offering five career-guided courses for free with this QLS course, which will help you to boost your employability and take your career to the next level. These courses include Career Development Plan Fundamentals, Networking Skills for Personal Success, Boost Your Confidence and Self-Esteem, CV Writing and Job Searching, and Learn to Reduce and Manage Stress.</p> <p>With our course, you will receive top-quality education and valuable guidance, enabling you to gain the practical skills and knowledge you need to succeed in the competitive world of business. Enrol in our Level 3, 5 &amp; 7 Linear Programming course today and take the first step towards a successful career in business.</p> <p><strong>Learning Outcomes:</strong></p> <ul> <li>Understand the basic principles and concepts of linear programming, including the use of constraints and objective functions.</li><li>Learn to solve problems with mixed-type constraints, including those with multiple solutions.</li><li>Gain knowledge of the different types of constraints, including those of the '=', '&gt;=' and '&lt;=' types.</li><li>Develop the ability to simplify complex problems and identify degeneracy in linear programming.</li><li>Master the two-phase method and sensitivity analysis to evaluate solutions and identify the impact of changes in parameters.</li><li>Understand the concept of duality in linear programming and its applications.</li><li>Learn to use Solver tools and mobile apps for linear programming problems and find online solutions.</li></ul> <p><strong>Please Note: Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</strong></p><p>Diploma in Level 3, 5 &amp; 7 Linear Programming at QLS at QLS's Description:</p> <p><strong>Level 3:</strong></p> <p>In this level, you will be introduced to the basics of linear programming, including the use of constraints and objective functions. You will learn how to solve problems with mixed-type constraints, including those with multiple solutions. We will also discuss the different types of constraints, including those of the '=', '&gt;=' and '&lt;=' types. You will have the opportunity to practice these concepts through various problem sets and examples.</p> <p><strong>Level 5:</strong></p> <p>In this level, you will learn how to simplify complex problems and identify degeneracy in linear programming. We will introduce you to the two-phase method and sensitivity analysis, which will help you evaluate solutions and identify the impact of changes in parameters. We will also discuss the concept of duality in linear programming and its applications. You will have the opportunity to practice these concepts through case studies and problem sets.</p> <p><strong>Level 7:</strong></p> <p>In this level, we will take a deeper dive into sensitivity analysis and duality in linear programming. You will learn how to use Solver tools and mobile apps to find online solutions for linear programming problems. We will present a production scheduling problem for formulation and an infeasible problem as case studies to help you apply these concepts in a practical context.</p> <p><strong>This bundle comprises the following courses:</strong><strong><br /></strong><strong>QLS Endorsed Course:</strong></p> <ul> <li><strong>Course 01:</strong> Advanced Diploma in Linear Programming at QLS Level 7</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul> <p><strong>Certificate</strong></p> <p>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</p> <p>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Upon completion, learners will receive an Advanced Diploma in Linear Programming at QLS Level 7 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <ul> <li>Business professionals who want to improve their decision-making skills.</li><li>Students who want to gain a competitive advantage in the job market.</li><li>Data analysts who want to improve their knowledge of linear programming.</li><li>Operations managers who want to optimise their business processes.</li><li>Anyone interested in improving their problem-solving skills.</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Operations Research Analyst - £30,000 to £60,000</li><li>Data Analyst - £25,000 to £50,000</li><li>Business Analyst - £25,000 to £45,000</li><li>Financial Analyst - £25,000 to £50,000</li><li>Logistics Manager - £30,000 to £60,000</li><li>Supply Chain Analyst - £25,000 to £50,000</li><li>Management Consultant - £40,000 to £80,000</li></ul> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 7 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Level 3, 5 & 7 Linear Programming at QLS

<strong>Get started with using linear algebra in your data science projects</strong>
        <p>Vectorizing your code is an essential skill to make your calculations faster and take advantage of the capabilities of modern machine and deep learning packages. This course will get you up and running with linear algebra fundamentals for data science in Python. 

In this course, you will learn about scalars, vectors, and matrices and the geometrical meaning of these objects. You will also learn how you should use linear algebra in your Python code. In addition to this, you'll be able to perform operations such as addition, subtraction and dot product. As you cover further sections, you'll focus on the different syntactical errors you can encounter while vectorizing your code.  

By the end of this course, you will have gained the skills you need to use linear algebra confidently in your data science projects. 

All code and supporting files for this course are available at - https://github.com/PacktPublishing/Linear-Algebra-for-Data-Science-in-Python</p>
        <h2>What You Will Learn</h2><p>Focus on the addition and subtraction of Matrix <br>Understand errors when adding matrices <br>Learn why linear algebra is useful</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists or anyone who wants to learn linear algebra in Python.</p>
        <h2>Approach</h2><p>This course takes you through linear algebra in a systematic manner. It is even packed with step-by-step instructions and working examples to enhance your learning experience.</p>
        <h2>Key Features</h2><p>Learn linear algebra for data science and understand the essential concepts  * Understand matrix, scalars, and vectors and learn how to use them</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/linear-algebra-for-data-science-in-python</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Linear Algebra Fundamentals</h4><p></p>

Linear Algebra

Get started with using linear algebra in your data science projects

Linear Algebra for Data Science in Python

<div>
        <div>
            <h2>
                Recognised Accreditation
            </h2>
            <p>This course is accredited by continuing professional development (CPD). CPD UK is globally
                recognised by employers, professional organisations, and academic institutions, thus a
                certificate from CPD Certification Service creates value towards your professional goal
                and achievement.</p>

                

                <p>The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries.</p>
                
    </div>
    <div>
        <div>
            <div>
                
                <h3>What is CPD?</h3>
                <p>Employers, professional organisations, and academic institutions all recognise CPD,
                    therefore a credential from CPD Certification Service adds value to your professional
                    goals and achievements.</p>
                <h4>Benefits of CPD</h4>
                <ul>
                    <li>Improve your employment prospects </li>
                    <li>Boost your job satisfaction </li>
                    <li>Promotes career advancement </li>
                    <li>Enhances your CV </li>
                    <li>Provides you with a competitive edge in the job market </li>
                    <li>Demonstrate your dedication </li>
                    <li>Showcases your professional capabilities</li>
                </ul>
            </div>
        </div>
        <div>
            <div>
                
                <h3>What is IPHM? </h3>
                <p>The IPHM is an Accreditation Board that provides Training Providers with international
                    and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation
                    is a guarantee of quality and skill. </p>
                <h4>Benefits of IPHM </h4>
                <ul>
                    <li>It will help you establish a positive reputation in your chosen field </li>
                    <li>You can join a network and community of successful therapists that are dedicated to
                    providing excellent care to their client </li>
                    <li>You can flaunt this accreditation in your CV </li>
                    <li>It is a worldwide recognised accreditation </li>
                </ul>
            </div>
        </div>
        <div>
            <div>
                
                <h3>What is Quality Licence Scheme?</h3>
                <p>This course is endorsed by the Quality Licence Scheme for its high-quality,
                    non-regulated provision and training programmes. The Quality Licence Scheme is a brand
                    of the Skills and Education Group, a leading national awarding organisation for
                    providing high-quality vocational qualifications across a wide range of industries.</p>
                <h4> Benefits of Quality License Scheme </h4>
                <ul>
                    <li>Certificate is valuable</li>
                    <li>Provides a competitive edge in your career</li>
                    <li>It will make your CV stand out</li>
                </ul>
            </div>
        </div>
    </div>
                    </div><div>
				
<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table>				<tbody><tr>
					<td colspan="4">Getting Data Ready for Regression Model</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Transportation Problem in Excel using Goal Seek					</td>

					
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Gathering Business Knowledge					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Data Exploration					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The Data and the Data Dictionary					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Univariate analysis and EDD					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Discriptive Data Analytics in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Outlier Treatment					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Identifying and Treating Outliers in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Missing Value Imputation					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Identifying and Treating missing values in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Variable Transformation in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Dummy variable creation: Handling qualitative data					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Dummy Variable Creation in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Correlation Analysis					</td>

					
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Correlation Matrix in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Regression Model</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The Problem Statement					</td>

					
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Basic Equations and Ordinary Least Squares (OLS) method					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Assessing accuracy of predicted coefficients					</td>

					
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Assessing Model Accuracy: RSE and R squared					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Simple Linear Regression model					</td>

					
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Multiple Linear Regression					</td>

					
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The F - statistic					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Interpreting results of Categorical variables					</td>

					
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Multiple Linear Regression model					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">What-if analysis</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Excel: Running Linear Regression using Solver					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assessment</td>
				</tr>
								<tr>
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Study Plex

Linear Regression Analysis in Microsoft Excel

<strong>This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.</strong>
        <p>Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and non-linear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss-Markov assumptions and zero conditional mean. Moving ahead, you will cover non-linear regression, exploring how it works, what the different non-linear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be well-versed with linear and non-linear regression and the basic principles of the statistical methodology.

All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear and non-linear regression terminologies<br>Distinguish between different types of regression methods<br>Analyze and integrate complicated regression output from ordinary least squares (OLS)<br>Find out the difference between logit and probit transformation<br>Model non-linear relationships in a linear regression</p>
        <h2>Audience</h2><p>If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.</p>
        <h2>Key Features</h2><p>Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Linear Regression?</p><p style='margin: 0'>This video explains the concept of linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. Learning Outcomes</p><p style='margin: 0'>This video explains the various learning outcomes.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video explains the various prerequisites of the course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video shows how to use Stata for linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Regression Analysis?</p><p style='margin: 0'>This video explains the concept of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. Get to Know About Linear Regression</p><p style='margin: 0'>This video explains the concept of linear regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Regression Analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. What Types of Regression Analysis Exist?</p><p style='margin: 0'>This video explains the various types of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>11. Explaining Regression</p><p style='margin: 0'>This video explains the concept of regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lines of Best Fit</p><p style='margin: 0'>This video explains the lines of best fit concept.</p></td></tr><tr><td style='padding: 15px;'><p>13. Causality vs. Correlation</p><p style='margin: 0'>This video illustrates the difference between causality and correlation.</p></td></tr><tr><td style='padding: 15px;'><p>14. What is Ordinary Least Squares (OLS)?</p><p style='margin: 0'>This video explains the concept of OLS.</p></td></tr><tr><td style='padding: 15px;'><p>15. Ordinary Least Squares (OLS) Visual - Part 1</p><p style='margin: 0'>This video is the first part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>16. Ordinary Least Squares (OLS) Visual - Part 2</p><p style='margin: 0'>This video is the second part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>17. Sum of Squares</p><p style='margin: 0'>This video explains how to calculate the sum of squares using linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Best Linear Unbiased Estimator</p><p style='margin: 0'>This video explains the best linear unbiased estimator.</p></td></tr><tr><td style='padding: 15px;'><p>19. The Gauss-Markov Assumptions</p><p style='margin: 0'>This video discusses the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>20. Homoskedasticity</p><p style='margin: 0'>This video explains the concept of homoskedasticity.</p></td></tr><tr><td style='padding: 15px;'><p>21. No Perfect Collinearity</p><p style='margin: 0'>This video explains the concept of no perfect collinearity.</p></td></tr><tr><td style='padding: 15px;'><p>22. Linear in Parameters</p><p style='margin: 0'>This video focuses on linearity in parameters.</p></td></tr><tr><td style='padding: 15px;'><p>23. Zero Conditional Mean</p><p style='margin: 0'>This video explains what is zero conditional mean.</p></td></tr><tr><td style='padding: 15px;'><p>24. How to Test and Correct for Endogeneity?</p><p style='margin: 0'>This video explains how to test and correct for endogeneity.</p></td></tr><tr><td style='padding: 15px;'><p>25. The Gauss-Markov Assumptions - Recap</p><p style='margin: 0'>This video presents a recap of the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>26. Stata - Applied Examples</p><p style='margin: 0'>This video explains with an example how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>27. Final Thoughts and Tips</p><p style='margin: 0'>This video provides some final thoughts and tips with respect to linear regression.</p></td></tr></tbody></table><br><h4>2. Non-Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. What are the Main Learning Outcomes?</p><p style='margin: 0'>This video presents the learning outcome of this course.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video discusses the various prerequisites for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video explains how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Non-Linear Regression Analysis?</p><p style='margin: 0'>This video explains the concept of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. How does Non-Linear Regression Work?</p><p style='margin: 0'>This video explains the working of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Non-Linear Regression analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. Types of Non-Linear Regression models</p><p style='margin: 0'>This video explains the various types of non-linear regression models.</p></td></tr><tr><td style='padding: 15px;'><p>11. Maximum Likelihood</p><p style='margin: 0'>This video explains the maximum likelihood estimation technique.</p></td></tr><tr><td style='padding: 15px;'><p>12. Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>13. The Logit and Probit Transformation</p><p style='margin: 0'>This video explains the logit and probit transformation model.</p></td></tr><tr><td style='padding: 15px;'><p>14. Latent Variables</p><p style='margin: 0'>This video focuses on latent variables.</p></td></tr><tr><td style='padding: 15px;'><p>15. What are Marginal Effects?</p><p style='margin: 0'>This video explains the concept of marginal effects.</p></td></tr><tr><td style='padding: 15px;'><p>16. Dummy Explanatory Variables</p><p style='margin: 0'>This video focuses on the dummy explanatory variables.</p></td></tr><tr><td style='padding: 15px;'><p>17. Multiple Non-Linear Regression</p><p style='margin: 0'>This video explains multiple non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Goodness-of-Fit</p><p style='margin: 0'>This video discusses the concept of goodness-of-fit.</p></td></tr><tr><td style='padding: 15px;'><p>19. A Note about Logit Coefficients</p><p style='margin: 0'>This video provides a note about logit coefficients.</p></td></tr><tr><td style='padding: 15px;'><p>20. Tips for Logit and Probit Regression</p><p style='margin: 0'>This video provides some tips for logit and probit regression.</p></td></tr><tr><td style='padding: 15px;'><p>21. Back to the Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>22. Stata - Applied Logit and Probit Examples</p><p style='margin: 0'>This video provides examples of applied logit and probit using Stata.</p></td></tr></tbody></table><br><h4>3. Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to regression modelling.</p></td></tr><tr><td style='padding: 15px;'><p>2. Regression Modelling - Don't Rush it</p><p style='margin: 0'>This video explains the concept of regression modelling in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Non-Linear Shapes in Regression</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Non-Linear Shapes in Regression - Practical Examples</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>5. How to use and Interpret Interaction Effects?</p><p style='margin: 0'>This video explains how to use and interpret interaction effects.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to use and Interpret Interaction Effects? - Practical Examples</p><p style='margin: 0'>This video explains how to use and interpret interaction effects with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>7. Using Time in Regression</p><p style='margin: 0'>This video explains how to use time in regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. Using Time in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to use time in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>9. Categorical Explanatory Variables in Regression</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression.</p></td></tr><tr><td style='padding: 15px;'><p>10. Categorical Explanatory Variables in Regression - Practical Examples</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>11. Dealing with Multicollinearity in Regression</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Dealing with Multicollinearity in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>13. Dealing with Missing Data in Regression</p><p style='margin: 0'>This video explains how to deal with missing data in regression.</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Missing Data in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with missing data in regression with the help of examples.</p></td></tr></tbody></table>

Stata

Statistics

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

Easy Statistics: Linear and Non-Linear Regression

<strong>This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.</strong>
        <p>To work with statistics and quantitative reports, you need to have a good understanding of the fundamentals and techniques of statistics. However, learning and applying new statistical techniques can often be a daunting experience and this is where this video course comes in handy. This course is designed to guide you through the basic principles of statistical methodology in a step-by-step way.
You'll start with an introduction to the course along with an overview of the learning objectives. Next, you'll explore the types of regression analysis that exist, and find out how ordinary least squares (OLS) work. To get a deeper understanding of linear regression and OLS, you'll interpret and analyze complicated regression output from OLS. Finally, you'll discover essential tips and techniques that'll help you to confidently perform regression analysis.
By the end of this video, you'll be well-versed with linear regression techniques and the basic principles of statistical methodology.
The code and resources for this course is available at https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear regression terminologies<br>Distinguish between different kinds of regression methods<br>Find out the difference between correlation and causation<br>Analyze and integrate complicated regression output from OLS<br>Explore tips and tricks relating to regression analysis</p>
        <h2>Audience</h2><p>If you are a student at any level, an experienced professional, a manager, or a government worker who wants to delve into statistical regression or work with quantitative reports and policy analysis, then this course is for you. As the course is for beginners, it doesn't require you to have any knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of animated graphics and interesting examples, this course will teach you the fundamentals of statistical regression terminologies.</p>
        <h2>Key Features</h2><p>Explore the basic statistical fundamentals of ordinary least squares (OLS) * Understand linear regression and its application * Gain the confidence to interpret complicated regression output</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. What is Regression?</h4><p></p><br><h4>3. What is Ordinary Least Squares?</h4><p></p><br><h4>4. Ordinary Least Squares Regression in Practice</h4><p></p><br><h4>5. Tips and Final Thoughts </h4><p></p>

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.

Easy Statistics: Linear Regression

SkillWise

Advanced Linear Programming

<p><strong></strong><strong> Our Aim Is Your Satisfaction! Offer Ends Soon; Hurry Up!! </strong><strong></strong></p><p><strong>Are you looking to improve your current abilities or make a career move? Our unique</strong><strong> Statistical Concepts in R</strong><strong> course might help you get there! Expand your expertise with high-quality training - study the</strong><strong> Statistical Concepts in R </strong><strong>course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through the</strong><strong> Statistical Concepts in R </strong><strong>online training course.</strong></p><p>The Statistical Concepts in R course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Statistical Concepts in R course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.</p><p>This Statistical Concepts in R training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence!</p><p><strong>Why Prefer Us? </strong></p><ul><li>Opportunity to earn a certificate accredited by <strong>CPDQS.</strong></li><li><strong>Get a free student ID </strong>card! (£10 postal charge will be applicable for international delivery)</li><li>Innovative and Engaging Content.</li><li>Free Assessments</li><li>24/7 Tutor Support.</li></ul> <p>Take a step toward a brighter future!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong>Module 01: Introduction to the Course</strong></p><ul><li>Introduction</li></ul><p><strong>Module 02: Simple Linear Regression</strong></p><ul><li>Install R, RStudio and Basic Functionality</li><li>Basics of Linear Regression</li><li>Basics of Linear Regression continued</li></ul><p><strong>Module 03: Linear Regression Analysis</strong></p><ul><li>Linear Relationships</li><li>Line of Best Fit, SSE and MSE</li><li>Linear Regression Analysis Continued</li><li>Regression Results and Interpretation</li><li>Predicting Future Profits</li><li>Statistical Validity Tests</li><li>Statistical Validity Discussion</li></ul><p><strong>Module 04: Multiple Linear Regression</strong></p><ul><li>Multiple Linear Regression</li><li>Importing the data</li><li>Correlation Matrix and MLR</li><li>MLR Results and ANOVA</li><li>The Best Model?</li><li>Interaction Terms and Validity Testing</li><li>ANOVA and Predictions</li></ul><p><strong>Module 05: Non-linear Regression</strong></p><ul><li>Non-linear Regression (and Recap)</li><li>Logistic Regression Overview</li><li>Logistic Regression: Odds, Logs and Poisson</li><li>Logistic Regression: Fitting the Models in R</li></ul><p><strong>Assessment Process</strong></p><p>Your skills and knowledge will be tested with an <strong>automated multiple-choice assessment</strong>. You will then receive instant results to let you know if you have successfully passed the Statistical Concepts in R course.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this Statistical Concepts in R course. This course is open to everybody.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise to enrol in this course.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After completing this course, you are to start your career or begin the next phase of your career.</p>
                        </div>

 Our Aim Is Your Satisfaction! Offer Ends Soon; Hurry Up!! 
Are you looking to improve your current abilities or make a career move? Our unique Statistical Concepts in R course might help you get there! Expand your expertise with high-quality training - study the Statistical Concepts in R course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through the Statistical Concepts in R online training course.
The Statistical Concepts in R course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Statistical Concepts in R course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.
This Statistical Concepts in R training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence!
Why Prefer Us? 
Opportunity to earn a certificate accredited by CPDQS.
Get a free student ID card! (£10 postal charge will be applicable for international delivery)
Innovative and Engaging Content.
Free Assessments
24/7 Tutor Support.
Take a step toward a brighter future!
*** Course Curriculum ***
Module 01: Introduction to the Course
Introduction
Module 02: Simple Linear Regression
Install R, RStudio and Basic Functionality
Basics of Linear Regression
Basics of Linear Regression continued
Module 03: Linear Regression Analysis
Linear Relationships
Line of Best Fit, SSE and MSE
Linear Regression Analysis Continued
Regression Results and Interpretation
Predicting Future Profits
Statistical Validity Tests
Statistical Validity Discussion
Module 04: Multiple Linear Regression
Multiple Linear Regression
Importing the data
Correlation Matrix and MLR
MLR Results and ANOVA
The Best Model?
Interaction Terms and Validity Testing
ANOVA and Predictions
Module 05: Non-linear Regression
Non-linear Regression (and Recap)
Logistic Regression Overview
Logistic Regression: Odds, Logs and Poisson
Logistic Regression: Fitting the Models in R
Assessment Process
Your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the Statistical Concepts in R course.
 CPD 
10 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Anyone interested in learning more about the topic is advised to take this Statistical Concepts in R course. This course is open to everybody.
 Requirements 
You will not need any prior background or expertise to enrol in this course.
 Career path 
After completing this course, you are to start your career or begin the next phase of your career.

Academy for Health and Fitness

Statistical Computing

Trainers Course

Statistical Concepts in R

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">In this workshop we address Bereavement.&nbsp;Grief is a complex emotion that stems from various losses, not just death. It encompasses feelings of loss from relationships, dreams, health, and more. Losing a partner or a family member can profoundly impact one&#39;s life and support system. Children face unique challenges in grieving, which require careful support. The grieving process is unpredictable and non-linear, involving stages like denial, anger, and acceptance. Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.</p>

In this workshop we address Bereavement. Grief is a complex emotion that stems from various losses, not just death. It encompasses feelings of loss from relationships, dreams, health, and more. Losing a partner or a family member can profoundly impact one's life and support system. Children face unique challenges in grieving, which require careful support. The grieving process is unpredictable and non-linear, involving stages like denial, anger, and acceptance. Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.

The Northern College Of Clinical Hypnotherapy

Grief Healing

Bereavement

Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.

Module 7 Part 2 Bereavement & working with loss 

Probability

Pandas

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

Data Science 101: Methodology, Python, and Essential Math

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			<div>
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				<div>
			<h2>Overview
</h2>		</div>
				</div>
				<div>
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					<p><span>This comprehensive course on <strong>Algebra Fundamentals </strong></span>will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This <strong>Algebra Fundamentals</strong></span> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <strong>Algebra Fundamentals</strong></span>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our <strong>Algebra Fundamentals</strong></span><strong> </strong>is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</span></p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>17 sections • 83 lectures • 11:02:00 total length</p></div><div><div><div><div><div><div><div><p>•Lecture 1 Introduction: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 2 What is Algebra: 00:02:00</p></div><div><div><p>•Lecture 3 Simple Equations: 00:05:00</p></div><div><div><p>•Lecture 4 What are Polynomials: 00:04:00</p></div><div><div><p>•Lecture 5 Terms in Polynomials: 00:03:00</p></div><div><div><p>•Lecture 6 Degree of Polynomials: 00:05:00</p></div><div><div><p>•Lecture 7 Writing statements to algebraic form: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 8 Integers and common mistakes in solving integers: 00:13:00</p></div><div><div><p>•Lecture 9 Arrangement of Terms: 00:07:00</p></div><div><div><p>•Lecture 10 Powers on integers: 00:04:00</p></div><div><div><p>•Lecture11 Simplification using BODMAS: 00:08:00</p></div><div><div><p>•Lecture 12 Distributive Properties in Polynomials: 00:04:00</p></div><div><div><p>•Lecture 13 Simplify Polynomials: 00:10:00</p></div><div><div><p>•Lecture 14 Additions of Polynomials: 00:06:00</p></div><div><div><p>•Lecture 15 Subtractions of Polynomials: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 16 The rules of Indices in algebra: 00:11:00</p></div><div><div><p>•Lecture 17 Fractional indices: 00:10:00</p></div><div><div><p>•Lecture 18 Understanding indices (practice questions): 00:07:00</p></div><div><div><p>•Lecture 19 Problems from IGCSE Last year papers: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 20 Multiplication of monomial to Polynomial: 00:09:00</p></div><div><div><p>•Lecture 21 Multiplication of Polynomial by Polynomial: 00:06:00</p></div><div><div><p>•Lecture 22 Division of algebraic expression by a monomial: 00:08:00</p></div><div><div><p>•Lecture 23 Division of algebraic expression by another polynomial: 00:09:00</p></div><div><div><p>•Lecture 24 Division of a polynomial by another polynomial with remainder: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 25 Rules of brackets: 00:04:00</p></div><div><div><p>•Lecture 26 Simplification by removing brackets: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 27 Simplification of algebraic fractions: 00:07:00</p></div><div><div><p>•Lecture 28 Rules to solve linear equations in one variable: 00:03:00</p></div><div><div><p>•Lecture 29 Solving linear equations in one variable: 00:07:00</p></div><div><div><p>•Lecture 30 Solving complex linear equations in one variable: 00:10:00</p></div><div><div><p>•Lecture 31 Word problems on linear equations in one variable: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 32 What are Identities?: 00:05:00</p></div><div><div><p>•Lecture 33 Identity ( a + b ) Â²: 00:13:00</p></div><div><div><p>•Lecture 35 Identity aÂ² - bÂ² = (a-b) (a +b ) new: 00:07:00</p></div><div><div><p>•Lecture 36 -- Standard Identities ( a + b + c ) Â² = a Â² + b Â² + c Â² + 2 a b + 2 a c +2 b c old: 00:07:00</p></div><div><div><p>•Lecture 37 Identity (x + a) (x + b) Identity Derivation &amp; Application new: 00:08:00</p></div><div><div><p>•Lecture 38 Pascal's Triangle _ Identity ( a + b ) Â³ new: 00:07:00</p></div><div><div><p>•Lecture 39 Identities( a - b ) Â³, ( a Â³ + b Â³) and (a Â³ - b Â³) new: 00:13:00</p></div><div><div><p>•Lecture 40 - Standard Identities a Â³ + b Â³ + c Â³ - 3 a b c: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 41 -Changing the subject of formula: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 42 - Linear Inequalities: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 43 - Factorization by taking out common factor: 00:10:00</p></div><div><div><p>•Lecture 44 - Factorization by grouping the terms: 00:09:00</p></div><div><div><p>•Lecture 45 - factorize using identity a Â² - b Â²: 00:07:00</p></div><div><div><p>•Lecture 46 - factorize using identity (a + b )Â² and (a - b )Â² (2): 00:08:00</p></div><div><div><p>•Lecture 47 - factorize using identity ( a + b + c ) Â²: 00:05:00</p></div><div><div><p>•Lecture 48 - factorization by middle term split: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 49 -Simplification of algebraic fractions: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 50 All that you need to know about co ordinate axis: 00:04:00</p></div><div><div><p>•Lecture 51 Some important facts needed to draw line graph: 00:03:00</p></div><div><div><p>•Lecture 52 - How to draw a line graph on coordinate plane: 00:03:00</p></div><div><div><p>•Lecture 53 Drawing line graphs: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 54 Simultaneous Linear Equations in two variables- intro: 00:03:00</p></div><div><div><p>•Lecture 55 Graphical method of solving linear equations: 00:06:00</p></div><div><div><p>•Lecture 56 Graphical method - more problems: 00:10:00</p></div><div><div><p>•Lecture 57 Method of Elimination by substitution: 00:09:00</p></div><div><div><p>•Lecture 58 Method of Elimination by Equating coefficients: 00:11:00</p></div><div><div><p>•Lecture 59 Method of Elimination by cross multiplication: 00:07:00</p></div><div><div><p>•Lecture 60 Equations reducible to simultaneous linear equations: 00:12:00</p></div><div><div><p>•Lecture 61 Word Problems on Linear equations: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 62 Polynomials and Zeros of polynomials: 00:10:00</p></div><div><div><p>•Lecture 63 Remainder Theorem: 00:04:00</p></div><div><div><p>•Lecture 64 Factor Theorem: 00:08:00</p></div><div><div><p>•Lecture 65 Practice problems on Remainder and Factor Theorem: 00:09:00</p></div><div><div><p>•Lecture 66 Factorization using factor Theorem: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 67 Zeros of polynomials Î±, Î² &amp; Î³: 00:10:00</p></div><div><div><p>•Lecture 68 Relation between zeros and coefficients of a polynomials: 00:13:00</p></div><div><div><p>•Lecture 69 Finding polynomials if zeros are known: 00:06:00</p></div><div><div><p>•Lecture 70 Practice problems on zeros of polynomials: 00:10:00</p></div><div><div><p>•Lecture 71Problems solving with Î± and Î² (part 1): 00:11:00</p></div><div><div><p>•Lecture 72 Problems solving with Î± and Î² (part 2): 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture73 what are Quadratic equations: 00:03:00</p></div><div><div><p>•Lecture 74 Solutions by factorization method: 00:12:00</p></div><div><div><p>•Lecture 75 Solutions by completing square formula: 00:06:00</p></div><div><div><p>•Lecture 76 Deriving Quadratic formula: 00:05:00</p></div><div><div><p>•Lecture 77 Practice problems by Quadratic formula: 00:07:00</p></div><div><div><p>•Lecture 78 Solving complex quadratic equations by Quadratic Formula: 00:11:00</p></div><div><div><p>•Lecture 79 Solutions of reducible to Quadratic Formula: 00:09:00</p></div><div><div><p>•Lecture 80 Skilled problems on Quadratic Equations: 00:07:00</p></div><div><div><p>•Lecture 81 Exponential problems reducible to Quadratic Equations: 00:06:00</p></div><div><div><p>•Lecture 82 Nature of Roots of Quadratic Equations: 00:09:00</p></div><div><div><p>•Lecture 83 Word problems on quadratic Equations Part 1: 00:13:00</p></div><div><div><p>•Lecture 84 Word problems on quadratic Equations Part 2: 00:11:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

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