Section 1 : Introduction to python

Lecture 1 Course Promo copy
Lecture 2 Thank You 1:6
Lecture 3 How to get 100% from this course 0:34

Section 2 : Setting up environment and jupyter notebook

Lecture 4 Python Environment setup 17:13

Section 3 : Module -2 python Arithmetic operations

Lecture 5 Terminology Alert 5:25
Lecture 6 Arithmetic Operators in python 8:42
Lecture 7 github link 2:6
Lecture 8 Student Community 0:59

Section 4 : module 3- python basics list string dictionary

Lecture 9 Module Intro 0:14
Lecture 10 Terminology Alert 1:28
Lecture 11 Python-Strings 8:6
Lecture 12 Terminology Alert 2:27
Lecture 13 Python-list 14:56
Lecture 14 Python-Dictionary 9:8

Section 5 : Numpy -Array Attributes

Lecture 15 Module Intro 1:14
Lecture 16 Terminology Alert 2:33
Lecture 17 Numpy-Basic array operations 7:51
Lecture 18 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 19 Terminology Alert 4:46
Lecture 20 Numpy-Random-Numbers 8:40
Lecture 21 Terminology Alert 4:51
Lecture 22 Numpy-advanced 3:25

Section 6 : Pandas

Lecture 23 Module Intro 1:44
Lecture 24 Pandas part1 6:23
Lecture 25 pandas part 2 5:6
Lecture 26 pandas part 3 7:51
Lecture 27 pandas part4 16:20

Section 7 : Matplotlib- Introduction

Lecture 28 Matplotlib-Introduction 2:45
Lecture 29 matplotlib1 2:52
Lecture 30 Matplotlib 1 3:41
Lecture 31 Matplotlib1 2:10
Lecture 32 Pandas with matplotlib 2:42
Lecture 33 Matplotlib advanced Exercise 18:6

Section 8 : Introduction to Data Science

Lecture 34 Introduction to Data Science
Lecture 35 Data Science with python Part 1 14:5
Lecture 36 Data Science with python Part 2 3:13
Lecture 37 Data Science with python Part 3
Lecture 38 Data Science with python Part 4 1:35
Lecture 39 Data Exploration Part 1 2:42
Lecture 40 Data Exploration Part 2 8:59
Lecture 41 Data Exploration Part 3 3:52
Lecture 42 T-Test
Lecture 43 T-test in python 5:9
Lecture 44 Z-Test 6:58
Lecture 45 Chi-Square Test 7:31
Lecture 46 Bivariate Exploration 1 5:56
Lecture 47 Bivariate Exploration 2 6:23
Lecture 48 Bivariate Exploration 2 4:26
Lecture 49 Modelling basics 9:18
Lecture 50 what is linear regression 4:14
Lecture 51 Gradient Descent with linear regression 7:5

Section 9 : Create simple machine learning models with sklearn

Lecture 52 sklearn Intro 1:8
Lecture 53 sklearn part 1 11:51
Lecture 54 Sklearn part 2 3:34

Section 10 : Flight Delay Prediction with real world data

Lecture 55 Flight Delay Prediction Introduction 8:2
Lecture 56 Flight Delay Prediction Data Pre-processing 17:2
Lecture 57 Flight Delay Prediction Feature Generation 22:43
Lecture 58 Flight Delay Prediction with Random Forest 2:54
Lecture 59 Flight Delay Prediction final

Section 11 : Anaconda environment and conda cheat sheet

Lecture 60 Optional Anaconda Virtual Environments 18:38

Section 12 : Bonus Lectures

Lecture 61 Playing with python codes 0:40
Lecture 62 Creating dashboards 22:17
Lecture 63 creating charts with python 27:44
Lecture 64 Live video Transformation with python 11:11
Lecture 65 Data Science Interview Questions 7:37