Machine Learning Mastery

Sambhav Athreya

As you work through the resources / notebooks I’ll provide, keep tracking yourself through the following checklist:

  • Regression
  • Linear and Logistic
  • Gradient Descent
  • Classification
  • k-NN, Decision Trees, Random Forests
  • Unsupervised Learning / Clustering
  • K-means
  • Model Evaluation
  • How to test accuracy, prevision
  • F1 Score
  • Confusion Matrices
  • Feature Engineering
  • Data Preprocessing
  • Feature Selection
  • Hyperparameter Tuning
  • Grid Search, Random Search
  • Neural Networks
  •  CNN, ANN, RNN
  • TensorFlow, Pytorch, Keras
  • Reinforcement Learning
  • Markov Decision Processes
  • Deep Reinforcement Learning
  • Natural Language Processing
  • Tokenization, Text Preprocessing
  • Transformers
  • How does GPT work?

Github Repos for Learning ML Concepts:

  1. https://github.com/mhuzaifadev/machine-learning_zero-to-hero
  2. https://github.com/Neurojedi/Machine-Learning-Zero2Hero
  3. https://github.com/duncantmiller/ai-developer-resources

To practice ML through coding use https://www.kaggle.com/learn