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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

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

Reinforcement Learning Part 1

Big data technologies Part 1

Emerging Issues Part 1

Expectation, Moments and CLT Part 1

What You Will Learn

Expectation, moments, CLT

Content Covered

Discusses expectation, moments, and the Central Limit Theorem.

Resources

Reference video
UChicago Paper
Conditional probability
Practice problems

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