Machine Learning A-Z™

Udemy Course - Machine Learning A-Z™: Hands-On Python & R

• Exercises and examples from Udemy Course
• My learning notes

My learning notes

• Part 1 - Data Preprocessing
  • Python
  • R
• Part 2 - Regression
  • 2-1 - Simple Linear Regression
    • Python
    • R
  • 2-2 - Multiple Linear Regression
    • Python
  • 2-3 - Polynomial Regression
    • Python
  • 2-4 - Support Vector Regression (SVR)
    • Python
  • 2-5 - Decision Tree Regression
    • Python
  • 2-6 - Random Forest Regression
    • Python
  • 2-7 - Evaluating Regression Models Performance & Regularization Methods
    • Material
• Part 3 - Classification
  • 3-1 - Logistic Regression
    • Python
  • 3-2 - K-Nearest Neighbors (K-NN)
    • Python
  • 3-3 - Support Vector Machine (SVM)
  • 3-4 - Kernel SVM
  • 3-5 - Naive Bayes
  • 3-6 - Decision Tree Classification
  • 3-7 - Random Forest Classification
  • 3-8 - Evaluating Classification Models Performance
• Part 4 - Clustering
  • 4-1 - K-Means Clustering
  • 4-2 - Hierarchical Clustering
• Part 5 - Association Rule Learning
  • 5-1 - Apriori
  • 5-2 - Eclat
• Part 6 - Reinforcement Learning
  • 6-1 - Upper Confidence Bound (UCB)
  • 6-2 - Thompson Sampling
• Part 7 - Natural Language Processing
  • 7-1 - Natural Language Processing
• Part 8 - Deep Learning
  • 8-1 - Artificial Neural Networks (ANN)
  • 8-2 - Convolutional Neural Networks (CNN)
• Part 9 - Dimensionality Reduction
  • 9-1 - Principal Component Analysis (PCA)
  • 9-2 - Linear Discriminant Analysis (LDA)
  • 9-3 - Kernel PCA
• Part 10 - Model Selection & Boosting
  • 10-1 - Model Selection
  • 10-2 - XGBoost