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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

Gradient Descent & Regularisation Part 1

What You Will Learn

Gradient Descent
Regularisation

Content Covered

Explains Gradient Descent and Regularisation techniques.

Resources

Gradient Descent blog
Handling Class Imbalance
Bias vs Variance video
Convex function Wikipedia
Visualization of Gradient Descent and SGD video
Stochastic Gradient Descent blog
Stochastic vs Batch Gradient Descent medium

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