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Data Science & Machine Learning

Before starting to learn ML, make sure you have a good hold of Python Programming and Data Handling using Python.

Overview Part 1 Overview Part 2
Random Variables and Probability Distributions Part 1
Expectation, Moments and CLT Part 1
Bayes' Theorem Part 1
Naïve Bayes, Gaussian Naive Bayes, Bayes' Optimal Classifier Part 1
Regression vs Classification Part 1
Bayesian Parameter Estimation Part 1
Maximum Likelihood Estimation Part 1
Maximum A Posteriori Estimation Part 1
Gradient Descent & Regularisation Part 1
Linear Regression: Normal Equation & Regularisation Part 1 Linear Regression: Normal Equation & Regularisation Part 2
Logistic Regression Concepts Part 1
Logistic Regression: Multiclass Classification Part 1
SVM with Linear Kernel Part 1
SVM: Slack Variables and Nonlinear Kernels Part 1
Decision Trees Part 1
Random Forest + Comparison Part 1
Artificial Neural Networks : Introduction Part 1
Artificial Neural Networks : Backpropagation Part 1
ANN: Activation, Loss & Optimization Part 1
Solving ODEs using ANN Part 1
Deep Learning Intro Part 1
Generative Vs. Discriminative Models Part 1
Probabilistic Graphical Models Part 1
Expectation Maximization Part 1
K-Means clustering Part 1
Gaussian Mixture Models Part 1
PCA Part 1
PCA Part 2
Reinforcement Learning Part 1
Big data technologies Part 1
Emerging Issues Part 1

Reinforcement Learning Part 1

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Content Covered

Introduction to Reinforcement Learning.

Resources

  • Amunategui RL (Duplicate)
  • Youtube zdIQkjtFX_ playlist
  • TDSC RL Implem TicTacToe (Duplicate)
  • Youtube WO3kmx4CVgg playlist
  • Youtube yMk_XtIEzH8 video
  • Youtube L8ypSXwyBds video
  • Github patrickloeber snake-ai-pytorch
  • Github maksimKorzh tictactoe-mtcs
  • Kaggle rock-paper-scissors-q-learning
  • Youtube ovlykchkW5I video
  • Youtube 2eeYqJ0uBKE video
  • Medium RL step-by-step
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