Python机器学习笔记

Python 机器学习

Intro to machine learning

  1. How models Work
  2. Basic Data Exploration
  3. First Model
  4. Model Validation
  5. Underfitting and Overfitting
  6. Random Forests
  7. Machine Learning Competitions

Intermediate Machine Learning

  1. Introduction
  2. Missing Values
  3. Categorical Variables
  4. Pipelines
  5. Cross-Validation
  6. XGBoost
  7. Data Leakage

Time Series

  1. Linear Regression With Time Series
  2. Trend
  3. Seasonality
  4. Time series as Feature
  5. Hybrid Models
  6. Forecasting With Machine Learning

Machine Learning Explainability

  1. Use cases for model insights
  2. Permutation Important
  3. Partial Plots
  4. SHAP Values
  5. Advanced Uses of SHAP Values

Intro to Deep Learning

  1. A Single Neuron
  2. Deep Neural Networks
  3. Stochastic Gradient Descent
  4. Underfitting and Overfitting
  5. Dropout and Batch Normalization
  6. Binary Classification

Intro to Game AI and Reinforcement Learning

  1. Play the Game
  2. One-Step Lookahead
  3. N-Step lookahead
  4. Deep Reinforcement Learning