Machine Learning 101 with Scikit-learn and StatsModels

Video Description

New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis

About This Video

  • Learn machine learning with StatsModels and sklearn
  • Apply machine learning skills to solve real-world business cases
  • Get started with linear regression, logistic regression, and cluster analysis

In Detail

Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques.

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science.

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist!

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Table of Contents

  1. Chapter 1 : Introduction
    1. What Does the Course Cover? 00:03:20
  2. Chapter 2 : Setting Up the Working Environment
    1. Setting Up the Environment - An Introduction (Do Not Skip, Please)! 00:00:41
    2. Why Python and Why Jupyter? 00:04:54
    3. Installing Anaconda 00:03:03
    4. The Jupyter Dashboard - Part 1 00:02:27
    5. The Jupyter Dashboard - Part 2 00:05:14
    6. Installing sklearn 00:01:18
  3. Chapter 3 : Linear Regression with StatsModels
    1. Introduction to Regression Analysis 00:01:28
    2. The Linear Regression Model 00:05:50
    3. Correlation vs Regression 00:01:44
    4. Geometrical Representation 00:01:26
    5. Python Packages Installation 00:04:40
    6. Simple Linear Regression in Python 00:07:12
    7. What is Seaborn? 00:01:22
    8. What Does the StatsModels Summary Regression Table Tell us? 00:05:47
    9. SST, SSR, and SSE 00:03:38
    10. The Ordinary Least Squares (OLS) 00:03:14
    11. Goodness of Fit: The R-Squared 00:05:31
    12. The Multiple Linear Regression Model 00:02:56
    13. Adjusted R-Squared 00:06:01
    14. F-Statistic and F-Test for a Linear Regression 00:02:01
    15. Assumptions of the OLS Framework 00:02:21
    16. A1: Linearity 00:01:51
    17. A2: No Endogeneity 00:04:10
    18. A3: Normality and Homoscedasticity 00:05:48
    19. A4: No Autocorrelation 00:03:31
    20. A5: No Multicollinearity 00:03:26
    21. Dealing with Categorical Data 00:06:44
    22. Making Predictions 00:03:30
  4. Chapter 4 : Linear Regression with Sklearn
    1. What is sklearn? 00:02:15
    2. Game Plan for sklearn 00:01:56
    3. Simple Linear Regression with sklearn 00:05:38
    4. Simple Linear Regression with sklearn - Summary Table 00:04:49
    5. Multiple Linear Regression with sklearn 00:03:11
    6. Adjusted R-Squared 00:04:46
    7. Feature Selection through p-values (F-regression) 00:04:41
    8. Creating a Summary Table with the p-values 00:02:10
    9. Feature Scaling 00:05:38
    10. Feature Selection through Standardization 00:05:23
    11. Making Predictions with Standardized Coefficients 00:03:53
    12. Underfitting and Overfitting 00:02:42
    13. Training and Testing 00:06:54
  5. Chapter 5 : Linear Regression - Practical Example
    1. Practical Example (Part 1) 00:12:00
    2. Practical Example (Part 2) 00:06:13
    3. Practical Example (Part 3) 00:03:16
    4. Practical Example (Part 4) 00:08:10
    5. Practical Example (Part 5) 00:07:35
  6. Chapter 6 : Logistic Regression
    1. Introduction to Logistic Regression 00:01:20
    2. A Simple Example of a Logistic Regression in Python 00:04:42
    3. What is the Difference Between a Logistic and a Logit Function? 00:04:00
    4. Your First Logistic Regression 00:02:48
    5. A Coding Tip (optional) 00:02:27
    6. Going through the Regression Summary Table 00:04:07
    7. Interpreting the Odds Ratio 00:04:30
    8. Dummies in a Logistic Regression 00:04:33
    9. Assessing the Accuracy of a Classification Model 00:03:22
    10. Underfitting and Overfitting 00:03:43
    11. Testing our Model and Bulding a Confusion Matrix 00:05:05
  7. Chapter 7 : Cluster Analysis
    1. Introduction to Cluster Analysis 00:03:41
    2. Examples of Clustering 00:04:32
    3. Classification vs Clustering 00:02:32
    4. Math Concepts Needed to Proceed 00:03:20
    5. K-Means Clustering 00:04:41
    6. A Hands-on Example of K-Means 00:07:48
    7. Categorical Data in Cluster Analysis 00:02:50
    8. The Elbow Method or How to Choose the Number of Clusters 00:06:11
    9. Pros and Cons of K-Means 00:03:24
    10. Standardization of Features when Clustering 00:04:33
    11. Cluster Analysis and Regression Analysis 00:01:32
    12. Practical Example: Market Segmentation (Part 1) 00:06:04
    13. Practical Example: Market Segmentation (Part 2) 00:06:59
    14. What Can be Done with Cluster Analysis? 00:04:48
  8. Chapter 8 : Cluster Analysis: Additional Topics
    1. Other Types of Clustering 00:03:40
    2. The Dendrogram 00:05:21
    3. Heatmaps 00:04:34

Product Information

  • Title: Machine Learning 101 with Scikit-learn and StatsModels
  • Author(s): 365 Careers
  • Release date: July 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838987671