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Machine Learning - Linear and Logistic Regression

Video Description

Build robust models in Excel, R, and Python! This Linear and Logistic Regression online training course will teach you how to build robust linear models and do logistic regressions that will stand up to scrutiny when you apply them to real world situations. You will learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons. Supplemental Materials included!

Table of Contents

  1. INTRODUCTION
    1. You, This Course, and Us 00:01:55
  2. CONNECT THE DOTS WITH LINEAR REGRESSION
    1. Using Linear Regression to Connect the Dots 00:09:04
    2. Two Common Applications of Regression 00:05:24
    3. Extending Linear Regression to Fit Non-linear Relationships 00:02:36
  3. BASIC STATISTICS USED FOR REGRESSION
    1. Understanding Mean and Variance 00:06:04
    2. Understanding Random Variables 00:16:55
    3. The Normal Distribution 00:09:32
  4. SIMPLE REGRESSION
    1. Setting up a Regression Problem 00:11:37
    2. Using Simple Regression to Explain Cause-Effect Relationships 00:04:57
    3. Using Simple Regression for Explaining Variance 00:08:07
    4. Using Simple Regression for Prediction 00:04:04
    5. Interpreting the Results of a Regression 00:07:26
    6. Mitigating Risks in Simple Regression 00:07:57
  5. APPLYING SIMPLE REGRESSION
    1. Applying Simple Regression in Excel 00:11:57
    2. Applying Simple Regression in R 00:11:14
    3. Applying Simple Regression in Python 00:06:06
  6. MULTIPLE REGRESSION
    1. Introducing Multiple Regression 00:07:04
    2. Some Risks Inherent to Multiple Regression 00:10:06
    3. Benefits of Multiple Regression 00:03:49
    4. Introducing Categorical Variables 00:06:58
    5. Interpreting Regression results - Adjusted R-squared 00:07:02
    6. Interpreting Regression results - Standard Errors of Coefficients 00:08:12
    7. Interpreting Regression results - T-Statistics and P-Values 00:05:33
    8. Interpreting Regression results - F-Statistic 00:02:52
  7. APPLYING MULTIPLE REGRESSION USING EXCEL
    1. Implementing Multiple Regression in Excel 00:08:54
    2. Implementing Multiple Regression in R 00:06:26
    3. Implementing Multiple Regression in Python 00:04:21
  8. LOGISTIC REGRESSION FOR CATEGORICAL DEPENDENT VARIABLES
    1. Understanding the Need for Logistic Regression 00:09:24
    2. Setting up a Logistic Regression Problem 00:06:03
    3. Applications of Logistic Regression 00:09:55
    4. The Link between Linear and Logistic Regression 00:08:13
    5. The Link between Logistic Regression and Machine Learning 00:04:16
  9. SOLVING LOGISTIC REGRESSION
    1. Understanding the Intuition behind Logistic Regression and the S-curve 00:06:21
    2. Solving Logistic Regression using Maximum Likelihood Estimation 00:10:03
    3. Solving Logistic Regression using Linear Regression 00:05:32
    4. Binomial vs Multinomial Logistic Regression 00:05:21
  10. APPLYING LOGISTIC REGRESSION
    1. Predict Stock Price Movements using Logistic Regression in Excel 00:09:53
    2. Predict Stock Price Movements using Logistic Regression in R 00:08:00
    3. Predict Stock Price Movements using Rule-based and Linear Regression 00:06:44
    4. Predict Stock Price Movements using Logistic Regression in Python 00:04:50