Machine-Learning-Articles-and-Lectures

I have written a series of Machine Learning and Data Science Lectures and articles based on learnings from a course.

The lectures are written chronologically starting from introduction to covering all major topics.

These lectures are worth reading especially if you are trying to brush up and revise these topics.

Lec 1: Introduction to AI-ML Lec

  Need for ML?
  
  What is ML, DL, AI?
  
  Differences in traditional and ML methods

Lec 2: Introduction to AI-ML - Part -2

  Different Types Of learnings :  Supervised, Unsupervised, Reinforcement Learnings, Batch and Online Learning
 
  ML piplines

Lec 3:Python Basics

 Datatypes, operators, Functions

 map(),lambda(),filter(),reduce()

Lec 4: Python Packages required for ML

Lec 7: Statistics Part -1: Descriptive Statistics

  Descriptive Statistics
  
  Measure of Central Tendencies, Dispersion Measures, Shape Distribution, Outliers