Machine Learning from the scratch using Python

Course Curriculum

Chapter 1: Python for Machine Learning

Lecture 1: Introduction to Python in Data Science

Lecture 2: Arithmetic Functions

Lecture 3: Defining, Storing Variables and Datatypes

Lecture 4: Working with Data Types

Lecture 5: PRACTICE

Lecture 6: Introduction to Lists

Lecture 7: SLICING

Lecture 8: Accessing List Values

Lecture 9: Sub-setting Lists

Lecture 10: Advanced List Operations

Lecture 11: Built in Functions 1.1

Lecture 12: Built in Functions 1.2

Lecture 13: Function Arguments 1.1

Lecture 14: Function Arguments 1.2

Lecture 15: Introduction to String Methods 1.1

Lecture 16: Introduction to String Methods 1.2

Lecture 17: Importing Python Packages

Lecture 18: Introduction to String Methods 1.3

Lecture 19: Subsetting and Comparing Arrays 1.1

Lecture 20: Introduction to NumPy Arrays

Lecture 21: Subsetting and Comparing Arrays 1.2

Chapter 2: Machine Learning

Lecture 1: Introduction to AI

Lecture 2: Introduction to ML( Supervised and Unsupervised Learning)

Lecture 3: ML : KNN ( Lp Norms)

Lecture 4: ML : KNN ( Euclidean and Manhattan Distance)

Lecture 5: ML : KNN ( Minkowski, Hamming and Cosine Distance )

Lecture 6: ML : Over and Under Fitting( Cross Validation and K-Fold CV )

Lecture 7: Project 1 : Creating the First Model using KNN and finding the Accuracy.

Lecture 8: ML : Linear Regression

Lecture 9: Project 2 : based on SIMPLE LINEAR REGRESSION

Lecture 10: Project 3 : based on MULTIPLE LINEAR REGRESSION

Lecture 11: ML : HYPOTHESIS TESTING ( Statistics Fundamentals )

Lecture 12: ML : Decision Tree with Gini Index

Lecture 13: ML : Decision Tree with Information Gain

Lecture 14: Project 4 : CASE STUDY based on DECISION TREE