300 Linear courses

These lectures will introduce the optimal transport (OT) toolbox, with two applications in econometrics. The first one will pertain to the estimation of matching models. We start by introducing the discrete OT problem and its entropic regularization, and inverse OT, as well as its estimation using generalized linear models. The second application will deal with quantile methods. The one-dimensional OT problem will be discussed as well as its connections with the notions of quantile and rank is then covered. Connection with quantile regression will be discussed and the ‘vector quantile regression’ problem will then be introduced. Part I Introduction (3h) S1. Monge-Kantorovich duality (1h30) S2. Computational optimal transport (1h30) https://www.math-econ-code.org/optimal-assignment [https://www.math-econ-code.org/optimal-assignment] Part II OT and matching models (3h) S3. Matching with Transferable Utility and random utility (1h30) https://www.math-econ-code.org/regularized-optimal-transport [https://www.math-econ-code.org/regularized-optimal-transport] S4. Estimation of matching models (1h30) https://www.math-econ-code.org/matching-estimation [https://www.math-econ-code.org/matching-estimation] Part III OT and quantiles (2h) S5. 1D optimal transport and quantiles (1h) https://www.math-econ-code.org/one-dimensional-assignment [https://www.math-econ-code.org/one-dimensional-assignment] S5. Connection with quantile regression (1h) https://www.math-econ-code.org/quantile-regression [https://www.math-econ-code.org/quantile-regression]

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

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

Get started with using linear algebra in your data science projects

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 Getting Data Ready for Regression Model Transportation Problem in Excel using Goal Seek 00:12:00 Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Univariate analysis and EDD 00:03:00 Discriptive Data Analytics in Excel 00:10:00 Outlier Treatment 00:04:00 Identifying and Treating Outliers in Excel 00:04:00 Missing Value Imputation 00:03:00 Identifying and Treating missing values in Excel 00:04:00 Variable Transformation in Excel 00:03:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy Variable Creation in Excel 00:07:00 Correlation Analysis 00:09:00 Creating Correlation Matrix in Excel 00:08:00 Creating Regression Model The Problem Statement 00:01:00 Basic Equations and Ordinary Least Squares (OLS) method 00:08:00 Assessing accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy: RSE and R squared 00:07:00 Creating Simple Linear Regression model 00:02:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting results of Categorical variables 00:05:00 Creating Multiple Linear Regression model 00:07:00 What-if analysis Excel: Running Linear Regression using Solver 00:08:00 Assessment Assessment - Linear Regression Analysis In MS Excel 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

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

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

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

OVERVIEW This comprehensive course on Algebra Fundamentals will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Algebra Fundamentals 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 Algebra Fundamentals. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Algebra Fundamentals 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 17 sections • 83 lectures • 11:02:00 total length •Lecture 1 Introduction: 00:03:00 •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 •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 •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 •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 •Lecture 25 Rules of brackets: 00:04:00 •Lecture 26 Simplification by removing brackets: 00:11:00 •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 •Lecture 32 What are Identities?: 00:05:00 •Lecture 33 Identity ( a + b ) ²: 00:13:00 •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 •Lecture 41 -Changing the subject of formula: 00:08:00 •Lecture 42 - Linear Inequalities: 00:12:00 •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 •Lecture 49 -Simplification of algebraic fractions: 00:06:00 •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 •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 •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 •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 •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

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.