320 Linear courses delivered Online

Essential 5G training course description This course is designed to give delegates an explanation of the technologies and interworking requirements of the next generation of cellular communications. It is not a definitive set of descriptions but a possibility of the final deployment. we will investigate the 10 pillars for 5G which will include various Radio Access Technologies that are required to interwork smoothly. We will look at the 4G Pro features and other RATs. What will you learn List the ten pillars of 5G deployment. Describe the 5G Internet. Explain virtualization and RAT virtulization. Describe Software Defined Networks (SDN). Explain carrier aggregation. Describe the mobile cloud. Explain an overall picture of 5G architecture. Essential 5G training course details Who will benefit: Anyone looking for an understanding of the technologies and interworking requirements of the next generation of cellular communications. Prerequisites: None. Duration 3 days Essential 5G training course contents Drivers for 5G 5G Road Map, 10 Pillars of 5G, evolving RATs, oSON, MTCm, mm-wave, backhaul, EE, new spectrum, spectrum sharing, RAN virtualisation. 4G LTE Advanced MIMO technology in release 8, Downlink & uplink MIMO R8, MIMO technology in LTE advanced, Downlink 8-layer SU-MIMO, Downlink MU-MIMO, Uplink MU-MIMO, Uplink transmit diversity, Coordinated multi-point operation (CoMP), Independent eNB & remote base station configurations, Downlink CoMP. ICIC & eICIC ICIC, Homogeneous to heterogeneous network evolution, Introduction to eICIC, Macro-pico scenario, Macro-femto scenario, Time orthogonal frequencies. Almost Blank Subframe (ABS). Carrier aggregation Component carriers (CC), CC aggregation deployments, Intra-band contiguous solutions, Intra-band non-contiguous solutions, Inter-band non-contiguous solutions, CA bandwidth classes, Aggregated transmission bandwidth configurations (ATBC), Possible carrier aggregation configs. eIMTA TDD UL-DL reconfig. for traffic adaptation, Reconfig. mechanisms, Interference mitigation schemes, Dynamic & flexible resource allocation. 5G architectures 5G in Europe, horizon 2020 framework, 5G infrastructure PPP, METIS project, 5G in North America, academy research, company R&D, 5G specifications. The 5G internet High-level view of Cloud Services, The Internet of Things & context awareness, Network reconfiguration & virtualization support, server proliferation, how VMs fix underutilised server problem, enter the hypervisor, why are VM such a big deal? SDN, evolution of the data centre network, high availability, low latency, scalability, security, cost model explodes, service-oriented API. OpenFlow switches, OpenFlow controllers, how SDN works. The big picture, pulling it all together, why the network had to change, how SDN & NFV tie together. Evolutionary approach to the internet, architectures for distributed mobility management, MEDIEVAL & MEDIVO projects, a clean slate approach, mobility first architecture. VNet, INM, NetInf, ForMux, MEEM. Generic Path (GP) & anchorless mobility (AM), Quality of Service support, network resource provisioning, resourcing inside a network. IntServ, RSVP, DiffServ, CoS. Emerging approach for resource over- provisioning, example use case architecture for scalable resource control scenarios in the 5G internet. Integrating SDN/NFV for efficient resource over-reservation control, control information repository, service admission control policies, network resource provisioning, control enforcement functions, network configurations & operations. Small cells for 5G Average spectral efficiency evolution, WiFi & Femto cells, Capacity limits. Achievable gains with densifications, multi-antenna techniques, small cells. Mobile data demand, approach & methodology, subscriber density and traffic demand projections to 2020. Demand versus capacity, global mobile data traffic increase modelling, country level backhaul traffic projections, Small cell challenges, backhaul, spectrum, automation. Cooperation for next gen wireless networks Diversity & relaying strategies, cooperation & network coding, ARQ & MAC protocols, NCCARQ & PRCSMA packet exchange, Physical layer impact on MAC protocol analysis, NCCARQ overview, PHY layer impact, Case study on NCCARQ. Mobile clouds Mobile cloud, Mobile cloud enablers, mobile user domain, wireless technologies, WWAN WLAN and WPAN range, Bluetooth, IEEE.802.15.4 & software stacks, infrared, near field communications (NFC). Network coding, store & forward vs compute & forward, linear network coding, random linear coding. Security for 5G communications Potential 5G communication systems architectures, Security issues & challenges. Mobile malware attacks targeting the UE, 5G mobile botnets, access networks, attacks on 4G networks, C-RNTI & packet sequence number based UE location tracking, false buffer status reports attacks, message insertion attacks, HeNB attacks, physical attacks, credential attacks, configuration and protocol attacks, attacks on MON, user data & identity attacks, mobile operator's core network, DDoS attacks targeting MON, signalling amplification, HSS saturation, external IP networks.

The Perfect your Algebra Fundamentals is a wonderful learning opportunity for anyone who has a passion for this topic and is interested in enjoying a long career in the relevant industry. It's also for anyone who is already working in this field and looking to brush up their knowledge and boost their career with a recognised certification. This Perfect your Algebra Fundamentals consists of several modules that take around 11 hours to complete. The course is accompanied by instructional videos, helpful illustrations, how-to instructions and advice. The course is offered online at a very affordable price. That gives you the ability to study at your own pace in the comfort of your home. You can access the modules from anywhere and from any device. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays 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 Video lessons Online study materials Mock exams Multiple-choice assessment Certification Upon successful completion of the course, you will be able to obtain your course completion PDF Certificate at £9.99. Print copy by post is also available at an additional cost of £15.99 and the same for PDF and printed transcripts. Course Content Introduction Lecture 1 Intro video Algebra Introduction final 00:02:00 Fundamental concepts on Algebraic Expressions Lecture 2 Terminology used in Algebra 00:05:00 Lecture 3 Language of Algebra 00:06:00 Lecture 4 Practice Questions 00:06:00 Lecture 5 Finding numerical value of an algebraic expression 00:14:00 Operations on Algebraic Expressions Lecture 6 Revision of Directed number ( integers 00:06:00 Lecture 7 Addition and subtraction of monomial expressions 00:06:00 Lecture 8 Addition of algebraic expressions with many terms 00:10:00 Lecture 9 Subtraction of algebraic expressions 00:10:00 Indices ( Exponents) Lecture 10 The rules of Indices in algebra 00:11:00 Lecture 11 Fractional indices 00:10:00 Lecture 12 Understanding indices (practice questions) 00:07:00 Lecture 13 Problems from IGCSE Last year papers 00:09:00 Multiplication and Division of Algebraic expressions Lecture 14 Multiplication of monomial algebraic expressions 00:05:00 Lecture 15 Multiplication of monomial with binomials and trinomials 00:11:00 Lecture 16 Division of algebraic expression by a monomial 00:07:00 Lecture 17 Division of algebraic expression by another polynomial 00:09:00 Lecture 18 Division of a polynomial by another polynomial with remainder 00:11:00 Brackets in Algebra Lecture 19 Rules of brackets 00:04:00 Lecture 20 Simplification by removing brackets 00:11:00 Linear equations in one variable Lecture 21 Simplification of algebraic fractions 00:07:00 Lecture 22 Rules to solve linear equations in one variable 00:03:00 Lecture 23 Solving linear equations in one variable 00:07:00 Lecture 24 Solving complex linear equations in one variable 00:10:00 Lecture 25 Word problems on linear equations in one variable 00:13:00 Algebraic Identities Lecture 26 Standard Identities (a + b )² and (a - b )² identities 00:11:00 Lecture 27 Standard Identity ( a - b ) ( a + b) = a ² - b ² 00:08:00 Lecture 28 Standard Identities ( a + b + c ) ² = a ² + b ² + c ² + 2 a b + 2 a c +2 b c 00:07:00 Lecture 29 Standard Identities ( a + b ) ³ and ( a - b ) ³ 00:09:00 Lecture 30 Standard Identities a ³ + b ³ and a ³ - b ³ 00:06:00 Lecture 31 Standard Identities a ³ + b ³ + c ³ - 3 a b c 00:10:00 Formula : Change of subject of formula Lecture 32 -Changing the subject of formula 00:08:00 Linear Inequalities Lecture 33 Linear Inequalities 00:12:00 Resolve into factors Lecture 34 Factorization by taking out common factor 00:10:00 Lecture 35 Factorization by grouping the terms 00:09:00 Lecture 36 Factorize using identity a ² - b ² 00:07:00 Lecture 37 Factorize using identity (a + b )² and (a - b )² 00:08:00 Lecture 38 Factorize using identity ( a + b + c ) ² 00:05:00 Lecture 39 Factorization by middle term split 00:12:00 Algebraic Fractions Lecture 40 Simplification of algebraic fractions 00:06:00 Coordinate axis - points and Line graph Lecture 41 All that you need to know about co ordinate axis 00:04:00 Lecture 42 Some important facts needed to draw line graph 00:03:00 Lecture 43 How to draw a line graph on coordinate plane 00:03:00 Lecture 44 Drawing line graphs 00:06:00 System of simultaneous linear equations in two variables Lecture 45 Simultaneous Linear Equations in two variables- intro 00:03:00 Lecture 46 Graphical method of solving linear equations 00:06:00 Lecture 47 Graphical method - more sums 00:10:00 Lecture 48 Method of Elimination by substitution 00:09:00 Lecture 49 Method of Elimination by Equating coefficients 00:11:00 Lecture 50 Method of Elimination by cross multiplication 00:07:00 Lecture 51 Equations reducible to simultaneous linear equations 00:12:00 Lecture 52 Word Problems on Linear equations 00:18:00 Polynomials Lecture 53 Polynomials and Zeros of polynomials 00:10:00 Lecture 54 Remainder Theorem 00:04:00 Lecture 55 Factor Theorem 00:08:00 Lecture 56 Practice problems on Remainder and Factor Theorem 00:09:00 Lecture 57 Factorization using factor Theorem 00:10:00 Quadratic Polynomials Lecture 58 Zeros of polynomials α, β & γ 00:10:00 Lecture 59 Relation between zeros and coefficients of a polynomials 00:13:00 Lecture 60 Writing polynomials if zeros are given 00:06:00 Lecture 61 Practice problems on zeros of polynomials 00:10:00 Lecture 62 Problems solving with α and β (part 1) 00:11:00 Lecture 63 Problems solving with α and β (part 2) 00:10:00 Quadratic Equations Lecture 64 what are Quadratic equations 00:03:00 Lecture 65 Solutions by factorization method 00:12:00 Lecture 66 Solutions by completing square formula 00:06:00 Lecture 67 Deriving Quadratic formula 00:05:00 Lecture 68 Practice problems by Quadratic formula 00:07:00 Lecture 69 Solving complex quadratic equations by Quadratic Formula 00:11:00 Lecture 70 Solutions of reducible to Quadratic Formula 00:09:00 Lecture 71 Skilled problems on Quadratic Equations 00:07:00 Lecture 72 Exponential problems reducible to Quadratic Equations 00:06:00 Lecture 73 Nature of Roots of Quadratic Equations 00:09:00 Lecture 74 Word problems on quadratic Equations Part 1 00:13:00 Lecture 75 Word problems on quadratic Equations Part 2 00:11:00 lecture 76 word problems on Quadratic 00:12:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 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.

This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.

The Build Your Algebra Fundamentals is a wonderful learning opportunity for anyone who has a passion for this topic and is interested in enjoying a long career in the relevant industry. It's also for anyone who is already working in this field and looking to brush up their knowledge and boost their career with a recognised certification. This Build Your Algebra Fundamentals consists of several modules that take around 11 hours to complete. The course is accompanied by instructional videos, helpful illustrations, how-to instructions and advice. The course is offered online at a very affordable price. That gives you the ability to study at your own pace in the comfort of your home. You can access the modules from anywhere and from any device. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays 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 Video lessons Online study materials Mock exams Multiple-choice assessment Certification Upon successful completion of the course, you will be able to obtain your course completion PDF Certificate at £9.99. Print copy by post is also available at an additional cost of £15.99 and the same for PDF and printed transcripts. Course Content Introduction Lecture 1 Introduction 00:03:00 Fundamental concepts on Algebraic Expressions Lecture 2 What is Algebra 00:02:00 Lecture 3 Simple Equations 00:05:00 Lecture 4 What are Polynomials 00:04:00 Lecture 5 Terms in Polynomials 00:03:00 Lecture 6 Degree of Polynomials 00:05:00 Lecture 7 Writing statements to algebraic form 00:04:00 Operations on Algebraic Expressions Lecture 8 Integers and common mistakes in solving integers 00:13:00 Lecture 9 Arrangement of Terms 00:07:00 Lecture 10 Powers on integers 00:04:00 Lecture11 Simplification using BODMAS 00:08:00 Lecture 12 Distributive Properties in Polynomials 00:04:00 Lecture 13 Simplify Polynomials 00:10:00 Lecture 14 Additions of Polynomials 00:06:00 Lecture 15 Subtractions of Polynomials 00:10:00 Indices ( Exponents) Lecture 16 The rules of Indices in algebra 00:11:00 Lecture 17 Fractional indices 00:10:00 Lecture 18 Understanding indices (practice questions) 00:07:00 Lecture 19 Problems from IGCSE Last year papers 00:09:00 Multiplication and Division of Algebraic expressions Lecture 20 Multiplication of monomial to Polynomial 00:09:00 Lecture 21 Multiplication of Polynomial by Polynomial 00:06:00 Lecture 22 Division of algebraic expression by a monomial 00:08:00 Lecture 23 Division of algebraic expression by another polynomial 00:09:00 Lecture 24 Division of a polynomial by another polynomial with remainder 00:11:00 Brackets in Algebra Lecture 25 Rules of brackets 00:04:00 Lecture 26 Simplification by removing brackets 00:11:00 Linear equations in one variable Lecture 27 Simplification of algebraic fractions 00:07:00 Lecture 28 Rules to solve linear equations in one variable 00:03:00 Lecture 29 Solving linear equations in one variable 00:07:00 Lecture 30 Solving complex linear equations in one variable 00:10:00 Lecture 31 Word problems on linear equations in one variable 00:13:00 Algebraic Identities Lecture 32 What are Identities? 00:05:00 Lecture 33 Identity ( a + b ) ² 00:13:00 Lecture 34 Identity ( a - b ) ² new 00:07:00 Lecture 35 Identity a² - b² = (a-b) (a +b ) new 00:07:00 Lecture 36 -- Standard Identities ( a + b + c ) ² = a ² + b ² + c ² + 2 a b + 2 a c +2 b c old 00:07:00 Lecture 37 Identity (x + a) (x + b) Identity Derivation & Application new 00:08:00 Lecture 38 Pascal's Triangle _ Identity ( a + b ) ³ new 00:07:00 Lecture 39 Identities( a - b ) ³, ( a ³ + b ³) and (a ³ - b ³) new 00:13:00 Lecture 40 - Standard Identities a ³ + b ³ + c ³ - 3 a b c 00:10:00 Formula : Change of subject of formula Lecture 41 -Changing the subject of formula 00:08:00 Linear Inequalities Lecture 42 - Linear Inequalities 00:12:00 Resolve into factors Lecture 43 - Factorization by taking out common factor 00:10:00 Lecture 44 - Factorization by grouping the terms 00:09:00 Lecture 45 - factorize using identity a ² - b ² 00:07:00 Lecture 46 - factorize using identity (a + b )² and (a - b )² (2) 00:08:00 Lecture 47 - factorize using identity ( a + b + c ) ² 00:05:00 Lecture 48 - factorization by middle term split 00:12:00 Algebraic Fractions Lecture 49 -Simplification of algebraic fractions 00:06:00 Coordinate axis - points and Line graph Lecture 50 All that you need to know about co ordinate axis 00:04:00 Lecture 51 Some important facts needed to draw line graph 00:03:00 Lecture 52 - How to draw a line graph on coordinate plane 00:03:00 Lecture 53 Drawing line graphs 00:06:00 System of simultaneous linear equations in two variables Lecture 54 Simultaneous Linear Equations in two variables- intro 00:03:00 Lecture 55 Graphical method of solving linear equations 00:06:00 Lecture 56 Graphical method - more problems 00:10:00 Lecture 57 Method of Elimination by substitution 00:09:00 Lecture 58 Method of Elimination by Equating coefficients 00:11:00 Lecture 59 Method of Elimination by cross multiplication 00:07:00 Lecture 60 Equations reducible to simultaneous linear equations 00:12:00 Lecture 61 Word Problems on Linear equations 00:18:00 Polynomials Lecture 62 Polynomials and Zeros of polynomials 00:10:00 Lecture 63 Remainder Theorem 00:04:00 Lecture 64 Factor Theorem 00:08:00 Lecture 65 Practice problems on Remainder and Factor Theorem 00:09:00 Lecture 66 Factorization using factor Theorem 00:10:00 Quadratic Polynomials Lecture 67 Zeros of polynomials α, β & γ 00:10:00 Lecture 68 Relation between zeros and coefficients of a polynomials 00:13:00 Lecture 69 Finding polynomials if zeros are known 00:06:00 Lecture 70 Practice problems on zeros of polynomials 00:10:00 Lecture 71Problems solving with α and β (part 1) 00:11:00 Lecture 72 Problems solving with α and β (part 2) 00:10:00 Quadratic Equations Lecture73 what are Quadratic equations 00:03:00 Lecture 74 Solutions by factorization method 00:12:00 Lecture 75 Solutions by completing square formula 00:06:00 Lecture 76 Deriving Quadratic formula 00:05:00 Lecture 77 Practice problems by Quadratic formula 00:07:00 Lecture 78 Solving complex quadratic equations by Quadratic Formula 00:11:00 Lecture 79 Solutions of reducible to Quadratic Formula 00:09:00 Lecture 80 Skilled problems on Quadratic Equations 00:07:00 Lecture 81 Exponential problems reducible to Quadratic Equations 00:06:00 Lecture 82 Nature of Roots of Quadratic Equations 00:09:00 Lecture 83 Word problems on quadratic Equations Part 1 00:13:00 Lecture 84 Word problems on quadratic Equations Part 2 00:11:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 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.

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist? Well then, you've come to the right place!

Overview This comprehensive course on Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning with Python comes with accredited certification, 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 Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning with Python 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 Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00

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 Welcome to the course Introduction 00:02:00 Setting up R Studio and R crash course Installing R and R studio 00:05:00 Basics of R and R studio 00:10:00 Packages in R 00:10:00 Inputting data part 1: Inbuilt datasets of R 00:04:00 Inputting data part 2: Manual data entry 00:03:00 Inputting data part 3: Importing from CSV or Text files 00:06:00 Creating Barplots in R 00:13:00 Creating Histograms in R 00:06:00 Basics of Statistics Types of Data 00:04:00 Types of Statistics 00:02:00 Describing the data graphically 00:11:00 Measures of Centers 00:07:00 Measures of Dispersion 00:04:00 Introduction to Machine Learning Introduction to Machine Learning 00:16:00 Building a Machine Learning Model 00:08:00 Data Preprocessing for Regression Analysis Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Importing the dataset into R 00:03:00 Univariate Analysis and EDD 00:03:00 EDD in R 00:12:00 Outlier Treatment 00:04:00 Outlier Treatment in R 00:04:00 Missing Value imputation 00:03:00 Missing Value imputation in R 00:03:00 Seasonality in Data 00:03:00 Bi-variate Analysis and Variable Transformation 00:16:00 Variable transformation in R 00:09:00 Non Usable Variables 00:04:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy variable creation in R 00:05:00 Correlation Matrix and cause-effect relationship 00:10:00 Correlation Matrix in R 00:08:00 Linear Regression Model The problem statement 00:01:00 Basic equations and Ordinary Least Squared (OLS) method 00:08:00 Assessing Accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy - RSE and R squared 00:07:00 Simple Linear Regression in R 00:07:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting result for categorical Variable 00:05:00 Multiple Linear Regression in R 00:07:00 Test-Train split 00:09:00 Bias Variance trade-off 00:06:00 Test-Train Split in R 00:08:00 Regression models other than OLS Linear models other than OLS 00:04:00 Subset Selection techniques 00:11:00 Subset selection in R 00:07:00 Shrinkage methods - Ridge Regression and The Lasso 00:07:00 Ridge regression and Lasso in R 00:12:00 Classification Models: Data Preparation The Data and the Data Dictionary 00:08:00 Importing the dataset into R 00:03:00 EDD in R 00:11:00 Outlier Treatment in R 00:04:00 Missing Value imputation in R 00:03:00 Variable transformation in R 00:06:00 Dummy variable creation in R 00:05:00 The Three classification models Three Classifiers and the problem statement 00:03:00 Why can't we use Linear Regression? 00:04:00 Logistic Regression Logistic Regression 00:08:00 Training a Simple Logistic model in R 00:03:00 Results of Simple Logistic Regression 00:05:00 Logistic with multiple predictors 00:02:00 Training multiple predictor Logistic model in R 00:01:00 Confusion Matrix 00:03:00 Evaluating Model performance 00:07:00 Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00 Linear Discriminant Analysis Linear Discriminant Analysis 00:09:00 Linear Discriminant Analysis in R 00:09:00 K-Nearest Neighbors Test-Train Split 00:09:00 Test-Train Split in R 00:08:00 K-Nearest Neighbors classifier 00:08:00 K-Nearest Neighbors in R 00:08:00 Comparing results from 3 models Understanding the results of classification models 00:06:00 Summary of the three models 00:04:00 Simple Decision Trees Basics of Decision Trees 00:10:00 Understanding a Regression Tree 00:10:00 The stopping criteria for controlling tree growth 00:03:00 The Data set for this part 00:03:00 Importing the Data set into R 00:06:00 Splitting Data into Test and Train Set in R 00:05:00 Building a Regression Tree in R 00:14:00 Pruning a tree 00:04:00 Pruning a Tree in R 00:09:00 Simple Classification Tree Classification Trees 00:06:00 The Data set for Classification problem 00:01:00 Building a classification Tree in R 00:09:00 Advantages and Disadvantages of Decision Trees 00:01:00 Ensemble technique 1 - Bagging Bagging 00:06:00 Bagging in R 00:06:00 Ensemble technique 2 - Random Forest Random Forest technique 00:04:00 Random Forest in R 00:04:00 Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques 00:07:00 Gradient Boosting in R 00:07:00 AdaBoosting in R 00:09:00 XGBoosting in R 00:16:00 Maximum Margin Classifier Content flow 00:01:00 The Concept of a Hyperplane 00:05:00 Maximum Margin Classifier 00:03:00 Limitations of Maximum Margin Classifier 00:02:00 Support Vector Classifier Support Vector classifiers 00:10:00 Limitations of Support Vector Classifiers 00:01:00 Support Vector Machines Kernel Based Support Vector Machines 00:06:00 Creating Support Vector Machine Model in R The Data set for the Classification problem 00:01:00 Importing Data into R 00:08:00 Test-Train Split 00:09:00 Classification SVM model using Linear Kernel 00:16:00 Hyperparameter Tuning for Linear Kernel 00:06:00 Polynomial Kernel with Hyperparameter Tuning 00:10:00 Radial Kernel with Hyperparameter Tuning 00:06:00 The Data set for the Regression problem 00:03:00 SVM based Regression Model in R 00:11:00 Assessment Assessment - Machine Learning Masterclass 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)