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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
PCA Part 1
PCA Part 2
Reinforcement Learning Part 1
Big data technologies Part 1
Emerging Issues Part 1

Bayes' Theorem Part 1

What You Will Learn

  • Bayes' Theorem basics

Content Covered

Introduces Bayes' Theorem.

Resources

  • Naive Bayes Youtube (Duplicate)
  • Spam filter using Naive Bayes
  • Gaussian Naive Bayes Youtube (Duplicate)
  • ML Mastery Gaussian Naive Bayes
  • Bayesian Networks
  • Sentiment analysis of tweet (Multinomial NB)

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