242 Probability courses delivered Online

You do not need any lectures regarding the importance of Maths and English, do you? To suffice, one is the language of science, and the other is the international language. Unfortunately, we often struggle in both, thanks to the lack of a solid base. Our Learning Maths & English GCSE bundle can give you a firm foundation or help you to pass your GCSE exam with flying colours! This Learning Maths & English GCSE bundle consists of 12 individual courses. This bundle can be primarily divided into two parts. The first part, consisting of six courses, covers fundamentals and advanced mathematics. You will learn the basic concepts of Algebra, high school maths, as well as advanced maths and calculus. The other six courses deal with English language essentials. You will gain an understanding of GCSE English, TESOL, spelling, punctuation, and basics of grammar etc. Moreover, you will learn 200 common English idioms and phrases along with essay writing in English. Be the master of numbers and letters, then? Enrol immediately in this Learning Maths & English GCSE and firm your base. Happy learning! Along with this Learning Maths & English GCSE Course - CPD Accredited course, you will get 11 premium courses, an originalhardcopy, 12 PDF certificates (Main Course + Additional Courses) Student ID card as gifts. This Learning Maths & English GCSE Course - CPD Accredited Bundle Consists of the following Premium courses: Course 01: Functional Skills Maths Level 2 Course 02: Mathematics Fundamentals Course 03: Algebra Fundamentals Course 04: Calculus Level 1 - Learn Differentiation Course 05: Advanced Mathematics Course 06: High School Math Course 07: GCSE English Course 08: Diploma in TEFL/TESOL Course 09: Functional Skills: English Course 10: English Spelling, Punctuation, and Grammar Basic Course Course 11: 200 Common English Idioms and Phrases Course 12: Essay Writing in English Learning Outcome Upon completion of this Learning Maths & English GCSE bundle, you will be able to - Learn about major mathematical concepts such as Decimal numbers, Rational Numbers, Approximation etc Understand the BODMAS Simplification Rule Consolidate your understanding of fundamental mathematics Learn differential calculus Understand advanced mathematical ideas such as Matrices, Trigonometric Functions and Trigonometric Functions Comprehend the rudiments of English Grasp sentence structure, verb usage, capitalisation and punctuation rules in detail Learn 200 common English idioms and phrases Enrol now in Learning Maths & English GCSE Course - CPD Accredited to advance your career, and use the premium study materials from Apex Learning. Curriculum of the Bundle Course 01: Functional Skills Maths Level 2 Module 1: Numbers and Negative Numbers Module 2: Multiples Factors Module 3: Fractions and Power Module 4: Percentages Module 5: Expressions Module 6: Decimals Module 7: Ratio and Proportion Module 8: Exponents and Radicals Module 9: Graphs Module 10: The Profit and Loss Module 11: Perimeter and Area Module 12: Averages Module 13: Probability Course 02: Mathematics Fundamentals Integers (Directed Numbers) Factors and Multiples Fractions Simplification Rule : BODMAS Decimal numbers Rational Numbers Approximation Estimation and Scientific Notation or Standard form Percentage Ratio and Proportion Unitary Method and its Applications Profit , Loss, discount and Tax Course 03: Algebra Fundamentals Introduction Fundamental concepts on Algebraic Expressions Operations on Algebraic Expressions Indices ( Exponents) Multiplication and Division of Algebraic expressions Brackets in Algebra Linear equations in one variable Algebraic Identities Formula : Change of subject of formula Linear Inequalities Resolve into factors Algebraic Fractions Coordinate axis - points and Line graph System of simultaneous linear equations in two variables Polynomials Quadratic Polynomials Quadratic Equations Course 04: Calculus Level 1 - Learn Differentiation Section 01: Introduction Section 02: Fundamental Rules for Differentiation Section 03: Differentiation of Trigonometric Functions Section 04: Differentiation of Exponential Functions Section 05: Differentiation of Logarithmic Functions Section 06: Revision Section 0on Chain Rule Section 07: Differentiation of inverse Trig Function Section 08: Differentiation of Hyperbolic Trig Functions Section 09: Differentiation of Implicit functions Section 10: Logarithmic Differentiation Section 11: Differentiation of Parametric Functions Section 12: Differentiation of Higher order derivatives Course 05: Advanced Mathematics Unit 01: Introduction Unit 02: Mathematical Logic Unit 03: Matrices Unit 04: Trigonometric Functions Unit 05: Pair of Straight Line Unit 06: Lines & Planes Unit 07: Linear Programming Course 06: High School Math Introduction Functions Quadratic Equations Co-ordinate Geometry Sequence and series Binomial Theorem Differentiation Tangents and Normals Stationary Points & Curve Sketching Second Derivative Test (Maximum & Minimum Points) Simultaneous Linear equations Essential Revision Course 07: GCSE English Module 01: Introduction to GCSE English Module 02: Edexcel English Section A Part 01 Module 03: Edexcel English Section A Part 02 Module 04: Edexcel English Section B Module 05: Edexcel English Section C Module 06: Cambridge English Exercise 1-4 Module 07: Cambridge English Exercise 5 Module 08: Cambridge English Exercise 6 Course 08: Diploma in TEFL/TESOL Module 01: Introduction to English Language Teaching & Learning Module 02: Linguistics Module 03: English Pronunciation Module 04: Teaching Grammar and Vocabulary Module 05: Language Teaching Methodologies Module 06: Teaching Receptive Skills: Reading Module 07: Teaching Productive Skills: Writing Module 08: Teaching Receptive Skills: Listening Module 09: Teaching Productive Skills: Speaking Module 10: Lesson Planning and Creating Materials Module 11: Use of Games and Activities Module 12: Technology in Second Language Teaching & Learning Module 13: Classroom Management and Student Motivation Module 14: Teaching English: Situational Approach Module 15: Professional Development and Employment Course 09: Functional Skills: English Module 1: Introduction to the Course Module 2: Basics of Grammar Module 3: The Basics of Sentence Module 4: Structure of Sentence Module 5: Question Module 6: Punctuation & Capitalisation Module 7: Spelling Module 8: Common Mistakes & Ways to Improve Course 10: English Spelling, Punctuation, and Grammar Basic Course Module 1: Introduction to the Course Module 2: The Basics of Grammar Module 3: The Basics of Sentence Module 4: Structure of Sentence Module 5: Question Module 6: Punctuation and Capitalisation Module 7: Spelling Module 8: Common Mistakes & Ways to Improve Course 11: 200 Common English Idioms and Phrases Section 01: Introduction Section 02: Idioms and Phrases Unit 1 Section 03: Idioms and Phrases Unit 2 Section 04: Idioms and Phrases Unit 3 Section 05: Idioms and Phrases Unit 4 Section 06: Idioms and Phrases Unit 5 Section 07: Idioms and Phrases Unit 6 Section 08: Idioms and Phrases Unit 7 Section 09: Idioms and Phrases Unit 8 Section 10: Idioms and Phrases Unit 9 Section 11: Idioms and Phrases Unit 10 Course 12: 200 Common English Idioms and Phrases Module 01: Introduction Module 02: The Narrative Essay Module 03: The Narrative Essay / part 2 Module 04: Introduction to the Essay 'Shooting an Elephant' Module 05: A reading of 'Shooting an Elephant' Module 06: Discussion on 'Shooting an Elephant' Module 07: Introduction to 2nd Essay - 'Most Important Day' Module 08: A reading - 'Most Important Day' Module 09: Discussion - 'Most Important Day' Module 10: Prompt for your own Narrative Essay CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Learning Maths & English GCSE Course - CPD Accredited bundle. Requirements This Learning Maths & English GCSE Course - CPD Accredited course has been designed to be fully compatible with tablets and smartphones. Career path Building a solid foundation in maths and English will benefit you in every career prospect. Specifically, This Learning Maths & English GCSE course will lead you to some particular career opportunities, such as; Teacher - £30,000 - £41,000 Annually Private Tutor - £20,00 - £25.00 Annually Finance Manager - £50,000 - £60,000 Annually Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Functional Skills Maths Level 2) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Register on the Overcoming OCD with Mindfulness & CBT today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as proof of your course completion. The Overcoming OCD with Mindfulness & CBT is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Overcoming OCD with Mindfulness & CBT Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Online study materials Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £59. An extra £10 postage charge will be required for students leaving overseas.  CPD Accredited Certificate After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Overcoming OCD with Mindfulness & CBT, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Overcoming OCD with Mindfulness & CBT Introduction 00:01:00 Course Outline 00:03:00 What is Mindfulness? 00:03:00 Basic Concept of Mindfulness 00:07:00 Cognitive Therapy 00:04:00 Behavioral Therapy 00:05:00 Obsessive Cycle 00:02:00 Acceptance 00:02:00 Assessment 00:03:00 Action 00:01:00 Nature of Thoughts 00:03:00 Cognitive Theory of Emotional Problems 00:03:00 Compulsion & Impairment 00:03:00 How did you develop OCD? 00:03:00 Psychological Vulnerabilities 00:05:00 Triggering Events 00:03:00 Probability & Awfulness 00:03:00 Superstition & Magical Thinking 00:02:00 Not trusting your sense 00:04:00 The Vicious Flower 00:02:00 Understanding your problem 00:04:00 Rumination & Religion 00:04:00 Starting to tackle your problem 00:03:00 Break Free from OCD 00:03:00 Choosing to Change 00:03:00 Mental Disorders 00:04:00 Family & Friends 00:05:00 Life After OCD 00:03:00 Conclusion 00:02:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Learn how to use this innovative tool to analyse and validate your schedule, to add and model uncertainty and risk and to work with updated plans to compare project progress. Course overview Duration: 1 day (6.5 hours) This course looks at the powerful features of Nodes and Links. It looks at analysing and validating your schedule, adding uncertainty and risk and working with updated plans to compare project progress. Hands on practice will be gained throughout the course to ensure you can confidentially put your new skills into practice back in the workplace. This course is designed for new users of Nodes and links, no previous experience is required. You should however be familiar with risk management processes and terminology. Objectives  By the end of the course you will be able to: Import and validate plans Analyse and review plans Add and model uncertainty Add and model risk Load updated schedules Re run analysis on updated schedules Content Validating your plan Importing a baseline plan Running a health check Analysing the results Reviewing the plan Analysing critical paths Reviewing activities Reviewing resources Adding Uncertainty Setting uncertainty templates Distributions Adding uncertainty Using Inherit Using AI Reviewing activity distributions Modelling Uncertainty Running the Analysis Reviewing the results Reviewing activity results Risk Drivers Filtering for activities Setting up the Risk Register Setting Risk Templates Adding Risks to the Risk Register Independent vs Dependant Events Setting Probability and Impact Modelling Uncertainty and Risk Mapping risks to activities Running the Analysis Reviewing the results Updated Plans Importing a new plan version Comparing plans Tracking progress Trend analysis Analysing Updated Plans Using updated plans Synchronising uncertainly and risk Rerunning analysis

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Recognised Accreditation 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. 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. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? 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. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? 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. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Unit 1-Number Lesson 1.1: Number Lesson 1.2: Fractions and Decimals. Lesson 1.3: Percentages. Lesson 1.4: Ratios Lesson 1.5: Formulas Mock Assessment 1 Mock Assessment 1 Unit 2-Measures, Shapes and Space Lesson 2.1: Money. Lesson 2.2: Length, Weight Capacity. Lesson 2.3: Time. Lesson 2.4: Perimeter. Lesson 2.5: Area and Volume. Lesson 2.6: 2D Shapes. Lesson 2.7: Nets Plans and Elevations Lesson 2.8: Angles, Bearings, Maps and Map Scales Mock Assessment 2 Mock Assessment 2 Unit 3-Handling Data and Information Lesson 3.1: Tables Lesson 3.2: Charts and Graphs Lesson 3.3: Grouped Data Lesson 3.4: Mean Range Lesson 3.5: Probability Mock Assessment 3 Mock Assessment 3 Formula Sheet FS Maths Formula Sheet Final Assessment Final Assessment Important Exam Information Important Exam Information – Functional Skills Maths Level 1 Course  

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!

Overview This comprehensive course on Functional Skills Maths - Level 1 (Updated 2022) will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Functional Skills Maths - Level 1 (Updated 2022) comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? 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. Who is This course for? There is no experience or previous qualifications required for enrolment on this Functional Skills Maths - Level 1 (Updated 2022). It is available to all students, of all academic backgrounds. Requirements Our Functional Skills Maths - Level 1 (Updated 2022) 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. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path 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- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 24 sections • 114 lectures • 11:37:00 total length •Lecture 1: Introduction Video: 00:02:00 •Lecture 2: Quick Guide: 00:01:00 •Lecture 1: Read & Write Large Numbers Part 1: 00:08:00 •Lecture 2: Read & Write Large Numbers Part 2: 00:13:00 •Lecture 3: Compare and Order Large Numbers: 00:12:00 •Downloadable Resources: 00:00:00 •Lecture 1: Why do you Round off a Number?: 00:03:00 •Lecture 2: Rounding off techniques: 00:07:00 •Lecture 3: Practice Problems Part 1: 00:09:00 •Lecture 4: Practice Problems Part 2: 00:08:00 •Lecture 5: Rounding to Significant Figures: 00:15:00 •Downloadable Resources: 00:00:00 •Lecture 1: Number System: 00:07:00 •Lecture 2: Integers and Ordering of Integers: 00:08:00 •Lecture 3: Addition and Subtractions of Integers: 00:10:00 •Lecture 4: Operations on Integers: 00:06:00 •Lecture 5: Multiplication and Division of Integers: 00:07:00 •Downloadable Resources: 00:00:00 •Lecture 1: Factors and Multiples Basics: 00:07:00 •Lecture 2: Divisibility tests for 2,3,4,5,6: 00:07:00 •Lecture 3: Divisibility tests for 7,8,9,10,11: 00:11:00 •Lecture 4: Prime Factorisation: 00:13:00 •Lecture 5: Highest Common Factor HCF: 00:13:00 •Lecture 6: HCF - Division Method: 00:10:00 •Lecture 7: Least Common Multiple LCM: 00:14:00 •Downloadable Resources: 00:00:00 •Lecture 1: Classification of Fractions: 00:09:00 •Lecture 2: Convert mixed to improper and improper to mixed fractions: 00:05:00 •Lecture 3: Equivalent Fractions: 00:07:00 •Lecture 4: Comparing Fractions after converting to like fractions: 00:09:00 •Lecture 5: Reducing a fraction to the lowest term: 00:05:00 •Lecture 6: Addition & Subtraction of fractions: 00:09:00 •Lecture 7: Multiplication and Division of Fractions: 00:11:00 •Lecture 8: Find Fractions of whole number quantities or measurements: 00:08:00 •Downloadable Resources: 00:00:00 •Lecture 1: Expanded form of Decimals: 00:08:00 •Lecture 2: Expanded form to Decimal form: 00:03:00 •Lecture 3: Writing fractions to Decimals: 00:04:00 •Lecture 4: Converting Decimals to a fraction: 00:05:00 •Lecture 5: Like & Unlike Decimals: 00:04:00 •Lecture 6: Comparing & Ordering Decimal Numbers: 00:09:00 •Lecture 7: Addition and Subtraction of Decimals: 00:06:00 •Lecture 8: Multiplication of Decimals: 00:07:00 •Lecture 9: Division of Decimals: 00:06:00 •Downloadable Resources: 00:00:00 •Lecture 1: Multiply whole numbers and decimals by 10,100 & 1000: 00:09:00 •Lecture 2: Divide whole numbers and decimals by 10,100 & 1000: 00:05:00 •Downloadable Resources: 00:00:00 •Lecture 1: Squaring of one-digit and two-digit numbers: 00:06:00 •Lecture 1: Simple Formula in words for one step or two steps operations: 00:09:00 •Downloadable Resources: 00:00:00 •Lecture 1: Intro to the metric system: 00:09:00 •Lecture 2: Length, Mass & Capacity: 00:10:00 •Lecture 3: Length, Mass & Capacity Addition & Subtraction: 00:05:00 •Lecture 1: Converting 12-hour time to a 24-hour time: 00:04:00 •Lecture 2: Converting 24-hour time to a 12-hour time: 00:05:00 •Lecture 3: Conversion to different units of Time: 00:09:00 •Lecture 4: Addition with Time: 00:08:00 •Lecture 5: Subtraction with Time: 00:08:00 •Lecture 6: Multiplication with Time: 00:07:00 •Lecture 7: Division with Time: 00:06:00 •Lecture 8: Finding Time interval between given timings Part 1: 00:05:00 •Lecture 9: Finding Time interval between given timings Part 2: 00:10:00 •Lecture 10: Past Paper problem Q1: 00:02:00 •Lecture 11: Past Paper problem Q2: 00:02:00 •Lecture 12: Past Paper problem Q3: 00:05:00 •Lecture 13: Past Paper problem Q4: 00:03:00 •Lecture 14: Past Paper problem Q5: 00:02:00 •Lecture 1: Order of Operations - PEMDAS: 00:10:00 •Lecture 2: Practice Problems on - PEMDAS: 00:05:00 •Downloadable Resources: 00:00:00 •Lecture 1: Estimation and approximation: 00:07:00 •Lecture 2: Estimation using Fractions and Decimals: 00:03:00 •Lecture 1: Simple Ratio and Direct Proportion Part 1: 00:09:00 •Lecture 2: Simple Ratio and Direct Proportion Part 2: 00:04:00 •Lecture 3: Simple Ratio and Direct Proportion Part 3: 00:04:00 •Lecture 1: Scale Drawing: 00:06:00 •Lecture 1: What is the meaning of Percentage?: 00:02:00 •Lecture 2: Percentage to Fraction: 00:07:00 •Lecture 3: Percentage of a quantity and conversation to Decimal: 00:05:00 •Lecture 4: Expressing one Quantity as a Percentage of another Quantity: 00:07:00 •Lecture 5: Calculate Percentages of amounts, increase, decrease by 5,10,15%: 00:11:00 •Lecture 1: Calculate simple interest in multiples of 5% on amounts of money: 00:11:00 •Lecture 2: Calculate discounts in multiples of 5% on amounts of money: 00:08:00 •Downloadable Resources: 00:00:00 •Lecture 1: Problems based on Money: 00:06:00 •Downloadable Resources: 00:00:00 •Lecture 1: What is statistics?: 00:04:00 •Lecture 2: Representation of discrete data in Tables: 00:11:00 •Lecture 3: One-way Tables: 00:03:00 •Lecture 4: Two-way Tables: 00:04:00 •Lecture 5: Grouping of a discrete data Part 1: 00:05:00 •Lecture 6: Grouping of a discrete data Part 2: 00:04:00 •Lecture 7: Represent discrete data in Bar Charts (Graphs)Part 1: 00:08:00 •Lecture 8: Represent discrete data in Bar Charts (Graphs)Part 2: 00:07:00 •Lecture 9: Pie Charts (Circle graphs) Part 1: 00:08:00 •Lecture 10: Pie Charts (Circle graphs) Part 2: 00:05:00 •Lecture 11: Pie Charts (Circle graphs) Part 3: 00:08:00 •Lecture 12: Mean & Range: 00:09:00 •Downloadable Resources: 00:00:00 •Lecture 1: Probability on a scale from 0 to 1: 00:12:00 •Lecture 2: Probabilities of simple events Part 1: 00:03:00 •Lecture 3: Probabilities of simple events Part 2: 00:06:00 •Downloadable Resources: 00:00:00 •Lecture 1: Lines & Angles: 00:08:00 •Lecture 2: Introduction to Triangles: 00:05:00 •Lecture 3: Polygons: 00:05:00 •Lecture 4: More 2D Figures: 00:07:00 •Lecture 5: Line symmetry: 00:05:00 •Lecture 1: Area & Perimeter Part 1: 00:08:00 •Lecture 2: Area & Perimeter Part 2: 00:06:00 •Lecture 3: Area & Perimeter Part 3: 00:08:00 •Downloadable Resources: 00:00:00 •Lecture 1: Calculation of the volumes & Surface Area of cubes and cuboids: 00:08:00