Python for Data Science and Machine Learning beginners

Course Curriculum

Chapter 1: Introduction to python

Lecture 1: Course Promo

Lecture 2: Thank You

Lecture 3: How to get 100% from this course

Chapter 2: Setting up environment and jupyter notebook

Lecture 1: Python Environment setup

Chapter 3: Module -2 python Arithmetic operations

Lecture 1: Terminology Alert

Lecture 2: Arithmetic Operators in python

Lecture 3: Student Community

Chapter 4: module 3- python basics list string dictionary

Lecture 1: Module Intro

Lecture 2: Terminology Alert

Lecture 3: Python-Strings

Lecture 4: Terminology Alert

Lecture 5: Python-list

Lecture 6: Python-Dictionary

Chapter 5: Numpy -Array Attributes

Lecture 1: Module Intro

Lecture 2: Terminology Alert

Lecture 3: Numpy-Basic array operations

Lecture 4: Matrix operations in numpy

Lecture 5: Terminology Alert

Lecture 6: Numpy-Random-Numbers

Lecture 7: Terminology Alert

Lecture 8: Numpy-advanced

Chapter 6: Pandas

Lecture 1: Module Intro

Lecture 2: Pandas part1

Lecture 3: pandas part 2

Lecture 4: pandas part 3

Lecture 5: pandas part4

Chapter 7: Matplotlib- Introduction

Lecture 1: Matplotlib-Introduction

Lecture 2: matplotlib1.1

Lecture 3: Matplotlib 1.2

Lecture 4: Matplotlib1.3

Lecture 5: Pandas with matplotlib

Lecture 6: Matplotlib advanced Exercise

Chapter 8: Introduction to Data Science

Lecture 1: Introduction to Data Science

Lecture 2: Data Science with python Part 1

Lecture 3: Data Science with python Part 2

Lecture 4: Data Science with python Part 3

Lecture 5: Data Science with python Part 4

Lecture 6: Data Exploration Part 1

Lecture 7: Data Exploration Part 2

Lecture 8: Data Exploration Part 3

Lecture 9: T-Test

Lecture 10: T-test in python

Lecture 11: Z-Test

Lecture 12: Chi-Square Test

Lecture 13: Bivariate Exploration 1

Lecture 14: Bivariate Exploration 2

Lecture 15: Bivariate Exploration 2

Lecture 16: Modelling basics

Lecture 17: what is linear regression

Lecture 18: Gradient Descent with linear regression

Chapter 9: Create simple machine learning models with sklearn

Lecture 1: sklearn Intro

Lecture 2: sklearn part 1

Lecture 3: Sklearn part 2

Chapter 10: Flight Delay Prediction with real world data

Lecture 1: Flight Delay Prediction Introduction

Lecture 2: Flight Delay Prediction Data Pre-processing

Lecture 3: Flight Delay Prediction Feature Generation

Lecture 4: Flight Delay Prediction with Random Forest

Lecture 5: Flight Delay Prediction final

Chapter 11: Anaconda environment and conda cheat sheet

Lecture 1: Optional: Anaconda Virtual Environments

Chapter 12: Bonus Lectures

Lecture 1: Playing with python codes

Lecture 2: Creating dashboards

Lecture 3: creating charts with python

Lecture 4: Live video Transformation with python

Lecture 5: Data Science Interview Questions