Machine Learning A to Z

Calculus

1. Functions - Logarithms, tanh, sigmoidal
2. Derivatives - Gradient, hessian

Statistical Data Analysis

1. Data Preprocessing
2. PCA : Principal Component Analysis
3. LDA : Latent Dirchlet Analysis
4. t-SNE : T-distributed stochastic neighbor embedding

Supervised Learning

• Classification algorithms
  • Logistic Regression
  • KNN
  • Naive Bayes classifier
  • Decision Trees
  • Support Vector Machines
  • Adaboost
  • Neural Networks
• Regression Analysis
  • Linear Regression
• Recommender Systems
• Evaluation
  • Loss Functions
    • Shannon Information, Entropy, KL Divergence
    • MSE
  • Metrics NDCG

Unsupervised Learning

Algorithms

• K-means clustering
• Mixture Models

Reinforcement learning

Computer Vision

• Bayes filters and Kalman filters

ML system design

• Grokking ML system design interview
• A recipe for training neural networks

Refernce Links

• ML flashcards
• ML interviews
• Aman's AI notes, Vinija's AI notes

Deep Learning

• Computer Vision

  • CNN basics

• Natural Language Processing

  • Sequence modelling
  • RNN

• Generative Modelling