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Machine Learning 101 with Scikit-learn and StatsModels [Video]
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Free ChapterIntroduction
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Setting Up the Working Environment
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Linear Regression with StatsModels
- Introduction to Regression Analysis
- The Linear Regression Model
- Correlation vs Regression
- Geometrical Representation
- Python Packages Installation
- Simple Linear Regression in Python
- What is Seaborn?
- What Does the StatsModels Summary Regression Table Tell us?
- SST, SSR, and SSE
- The Ordinary Least Squares (OLS)
- Goodness of Fit: The R-Squared
- The Multiple Linear Regression Model
- Adjusted R-Squared
- F-Statistic and F-Test for a Linear Regression
- Assumptions of the OLS Framework
- A1: Linearity
- A2: No Endogeneity
- A3: Normality and Homoscedasticity
- A4: No Autocorrelation
- A5: No Multicollinearity
- Dealing with Categorical Data
- Making Predictions
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Linear Regression with Sklearn
- What is sklearn?
- Game Plan for sklearn
- Simple Linear Regression with sklearn
- Simple Linear Regression with sklearn - Summary Table
- Multiple Linear Regression with sklearn
- Adjusted R-Squared
- Feature Selection through p-values (F-regression)
- Creating a Summary Table with the p-values
- Feature Scaling
- Feature Selection through Standardization
- Making Predictions with Standardized Coefficients
- Underfitting and Overfitting
- Training and Testing
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Linear Regression - Practical Example
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Logistic Regression
- Introduction to Logistic Regression
- A Simple Example of a Logistic Regression in Python
- What is the Difference Between a Logistic and a Logit Function?
- Your First Logistic Regression
- A Coding Tip (optional)
- Going through the Regression Summary Table
- Interpreting the Odds Ratio
- Dummies in a Logistic Regression
- Assessing the Accuracy of a Classification Model
- Underfitting and Overfitting
- Testing our Model and Bulding a Confusion Matrix
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Cluster Analysis
- Introduction to Cluster Analysis
- Examples of Clustering
- Classification vs Clustering
- Math Concepts Needed to Proceed
- K-Means Clustering
- A Hands-on Example of K-Means
- Categorical Data in Cluster Analysis
- The Elbow Method or How to Choose the Number of Clusters
- Pros and Cons of K-Means
- Standardization of Features when Clustering
- Cluster Analysis and Regression Analysis
- Practical Example: Market Segmentation (Part 1)
- Practical Example: Market Segmentation (Part 2)
- What Can be Done with Cluster Analysis?
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Cluster Analysis: Additional Topics
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!
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels
- Publication date:
- July 2019
- Publisher
- Packt
- Duration
- 5 hours 13 minutes
- ISBN
- 9781838987671
Download code from GitHub