Welcome to Python Machine Learning course!

Table of Content

• Authorship

Foreword

• Introduction
  • Machine Learning Overview
    • How was the advent and evolution of machine learning?
    • Why is machine learning important?
    • Who is using ML and why (government, healthcare system, etc.)?
    • Further Reading

Core Concepts

• Cross-Validation
  • Motivation
  • Holdout Method
  • K-Fold Cross Validation
  • Leave-P-Out / Leave-One-Out Cross Validation
  • Conclusion
  • Motivation
  • Code Examples
  • References
• Linear Regression
  • Motivation
  • Overview
  • When to Use
  • Cost Function
  • Methods
    • Ordinary Least Squares
    • Gradient Descent
  • Code
  • Conclusion
  • References
• Overfitting and Underfitting
  • Overview
  • Overfitting
  • Underfitting
  • Motivation
  • Code
  • Conclusion
  • References
• Regularization
  • Motivation
  • Overview
  • Methods
    • Ridge Regression
    • Lasso Regression
  • Summary
  • References

Supervised Learning

• Logistic Regression
  • Introduction
  • When to Use
  • How does it work?
  • Multinomial Logistic Regression
  • Code
  • Motivation
  • Conclusion
  • References
• Naive Bayes Classification
  • Motivation
  • What is it?
  • Bayes’ Theorem
  • Naive Bayes
  • Algorithms
    • Gaussian Model (Continuous)
    • Multinomial Model (Discrete)
    • Bernoulli Model (Discrete)
  • Conclusion
  • References
• Decision Trees
  • Introduction
  • Motivation
  • Classification and Regression Trees
  • Splitting (Induction)
  • Cost of Splitting
  • Pruning
  • Conclusion
  • Code Example
  • References
• k-Nearest Neighbors
  • Introduction
  • How does it work?
  • Brute Force Method
  • K-D Tree Method
  • Choosing k
  • Conclusion
  • Motivation
  • Code Example
  • References
• Linear Support Vector Machines
  • Introduction
  • Hyperplane
  • How do we find the best hyperplane/line?
  • How to maximize the margin?
  • Ignore Outliers
  • Kernel SVM
  • Conclusion
  • Motivation
  • Code Example
  • References

Unsupervised Learning

• Clustering
  • Overview
  • Clustering
  • Motivation
  • Methods
    • K-Means
    • Hierarchical
  • Summary
  • References
• Principal Component Analysis
  • Introduction
  • Motivation
  • Dimensionality Reduction
  • PCA Example
  • Number of Components
  • Conclusion
  • Code Example
  • References

Deep Learning

• Multi-layer Perceptron
  • Overview
  • Motivation
  • What is a node?
  • What defines a multilayer perceptron?
  • What is backpropagation?
  • Summary
  • Further Resources
  • References
• Convolutional Neural Networks
  • Overview
  • Motivation
  • Architecture
    • Convolutional Layers
    • Pooling Layers
    • Fully Connected Layers
  • Training
  • Summary
  • References
• Autoencoders
  • Autoencoders and their implementations in TensorFlow
  • Introduction
  • Create an Undercomplete Autoencoder

Document Credentials

• LICENSE