Section 1 : Course introduction

Lecture 1 Introduction - Why are you here and what we will accomplish here 1:40
Lecture 2 One important thing before you start Pdf
Lecture 3 What are the prerequesits for data science and this course
Lecture 4 Check you system 5:10
Lecture 5 Download all the source files Text

Section 2 : pandas for data science

Lecture 6 0 All you need to know about Series 23:20
Lecture 7 1 pandas for data scientists 7:46
Lecture 8 2 pandas for data scientists 5:37
Lecture 9 3 pandas for data scientists 6:21
Lecture 10 4 pandas for data scientists 8:48
Lecture 11 5 Broadcasting operations 7:11
Lecture 12 6 Counting 5:53
Lecture 13 7 The issue with missing values - a common problem in machine learning 10:44
Lecture 14 8 Dealing with missing values 2 10:55
Lecture 15 9 The right data in the right format 6:7
Lecture 16 10 Sorting your data properly 6:19
Lecture 17 11 How to slice your data 1
Lecture 18 12 How to slice your data 2 5:9
Lecture 19 13 How to check for missing values
Lecture 20 14 A machine learning insight - a full case study 25:16
Lecture 21 15 Master dates 11:58
Lecture 22 16 How to deal with dublicates 6:4
Lecture 23 17 How to play with the Index 7:39
Lecture 24 18 Slicing techniques 11:19
Lecture 25 19 Slicing techniques 2 21:50
Lecture 26 20 More data science techniques in pandas 11:51
Lecture 27 21 Data querying in pandas 6:42
Lecture 28 22 How to work with dates 26:18
Lecture 29 23 How to work with dates 2 3:36
Lecture 30 24 How to work with dates 3 5:3
Lecture 31 25 How to work with dates 4 3:36
Lecture 32 26 Grouping in pandas beginner to advanced 18:0
Lecture 33 27 The Multiindex 19:40
Lecture 34 28 Data science and Finance 26:23
Lecture 35 29 In depth combining dataframes 31:18
Lecture 36 30 Useful ways to deal with strings (regex example) 15:39
Lecture 37 31 Bonus Tips and Tricks 9:5
Lecture 38 32 Bonus Tips and Tricks 2 7:8
Lecture 39 33 Bonus Tips and Tricks 3 11:48

Section 3 : Introduction to numpy - what you need to know

Lecture 40 34 What are Tensors 8:25
Lecture 41 35 Introduction to numpy 1 6:42
Lecture 42 36 Introduction to numpy 2 10:26
Lecture 43 37 Introduction to numpy 3 9:55
Lecture 44 38 Introduction to numpy 4

Section 4 : Data Visualization

Lecture 45 39 Matplotlib - a how to guide 25:26
Lecture 46 40 Matplotlib - advanced
Lecture 47 41 Matplotlib - advanced 37:6

Section 5 : Master Data Visualization with Seaborn

Lecture 48 42 Seaborn introduction 1:34
Lecture 49 43 how to master seaborn 1 5:28
Lecture 50 44 how to master seaborn 2 5:8
Lecture 51 45 how to master seaborn 3 5:35
Lecture 52 46 how to master seaborn 4 3:38
Lecture 53 47 how to master seaborn 5 5:5
Lecture 54 48 how to master seaborn 6 6:48
Lecture 55 49 how to master seaborn 7 3:22
Lecture 56 50 how to master seaborn 8 5:28
Lecture 57 51 how to master seaborn 9 5:24
Lecture 58 52 how to master seaborn 10 6:54
Lecture 59 53 how to master seaborn 11 2:14
Lecture 60 54 how to master seaborn 12 2:22
Lecture 61 55 how to master seaborn 13 3:43
Lecture 62 56 how to master seaborn 14 3:37
Lecture 63 57 The end of the road - What to do now 1:4
Lecture 64 More learning resources for your AI learning journey Pdf
Lecture 65 Bonus - How to use Transfer learning to predict ice cream 12:13