Section 1 : Introduction to this Course

Lecture 1 Introduction to this Course 1:37

Section 2 : Introduction to Data Science & Machine Learning

Lecture 2 Terminology of Machine Learning and Data Science 1:37
Lecture 3 Data Science Example -1 4:58
Lecture 4 Data Science Example-2 2:56
Lecture 5 Data Science Example-3 4:18
Lecture 6 So What is Data Science 6:10
Lecture 7 Types of Machine Learning Techniques

Section 3 : Introduction to R & RStudio

Lecture 8 Install R and RStudio 1:17
Lecture 9 Introduction to RStudio 8:24
Lecture 10 What is Package 3:42
Lecture 11 How to Install Package 6:57

Section 4 : Data Types and Data Structures

Lecture 12 Data Types 6:31
Lecture 13 Vectors
Lecture 14 Basic Operations in Vectors 8:45
Lecture 15 List 13:32
Lecture 16 DataFrame 20:23
Lecture 17 Matrices 16:29
Lecture 18 Accessing the Elements or Subsetting 27:3
Lecture 19 How to read csv file 4:11

Section 5 : Data Visualization using ggplot2

Lecture 20 Numerical and Categorical Variables 6:18
Lecture 21 One Numerical Variable 7:58
Lecture 22 Two Numerical Variables 5:11
Lecture 23 Two Numerical and One Categorical 4:0
Lecture 24 Two Numerical and Two Categorical 3:57
Lecture 25 One Categorical Variable Barplot 3:17
Lecture 26 Two Categorical Barplot 4:13
Lecture 27 More than FiveSix Variables Facet_Wrap 10:26
Lecture 28 One,Two and more than three Variables Boxplot 5:45

Section 6 : Data Manipulation

Lecture 29 Introduction to Data Manipulation 6:6
Lecture 30 Select the Column Select 6:41
Lecture 31 Filter the rows Filter 7:34
Lecture 32 Mutate Create New Column 3:9
Lecture 33 Summarize the columns Summarize 5:32
Lecture 34 Summarize by groups Group and Summarize 7:14
Lecture 35 apply,lapply and sapply functions 11:47

Section 7 : Problems dealt in Machine Learning

Lecture 36 Difference between Regression & Classification 8:5

Section 8 : Model Fitting Process Classification

Lecture 37 Model Fitting Process Importing Required Libraries 3:17
Lecture 38 Model Fitting Process Set the Seed 1:26
Lecture 39 Model Fitting Process Reading the data set 9:30
Lecture 40 Model Fitting Process Converting Categorical into Factor 3:42
Lecture 41 Model Fitting Process Data Partition 4:29
Lecture 42 Model Fitting Process Fitting Model 17:30
Lecture 43 Model Fitting Process Predictions 12:54

Section 9 : Other Classification Models

Lecture 44 Random Forest 8:10
Lecture 45 What is Support Vector Machines 4:8
Lecture 46 Support Vector Machines 10:49

Section 10 : Regression

Lecture 47 Linear Regression 31:27

Section 11 : Advanced Algorithms Like Neural Networks

Lecture 48 Neural Networks 18:54

Section 12 : Dimensionality Reduction Techniques

Lecture 49 Introduction to Principal Component Analysis 2:29
Lecture 50 Scaling in R 8:27
Lecture 51 Intuition of Principal Component Analysis 5:16
Lecture 52 Implementation of PCA in R 26:51

Section 13 : Cross validation

Lecture 53 What is Cross validation 7:30
Lecture 54 How to do Cross Validation in R 23:52

Section 14 : Difference between Deep learning & Machine Learning

Lecture 55 Difference between Deep learning & Machine Learning 11:43

Section 15 : H20 framework

Lecture 56 Deeplearning 25:51

Section 16 : Python Installation

Lecture 57 How to install Python through Anaconda 3:6
Lecture 58 Introduction to Python Libraries 8:16

Section 17 : Python Data Structures useful for Machine Learning

Lecture 59 What is List in python 13:3
Lecture 60 How to sort a list 6:13
Lecture 61 How to join two list 5:52
Lecture 62 How to append elements to existing list 6:25
Lecture 63 What is range in python 7:55
Lecture 64 Sets 9:35
Lecture 65 What is dictionary in python 12:12
Lecture 66 What is enumerate in python 11:1
Lecture 67 What are lambda 11:7
Lecture 68 What is list comprehension in python 11:15
Lecture 69 Basic intro to classes and objects in python 14:48
Lecture 70 How to save a python dataframe into csv file 7:13

Section 18 : Machine Learning With Python

Lecture 71 Intro to Machine learning with python-Part 1 2:52
Lecture 72 Download data set -Part 2 0:47
Lecture 73 Explanation about data set -Part 3 1:27
Lecture 74 Importing libraries -Part 4 2:51
Lecture 75 Reading data set -Part 5 1:8
Lecture 76 Basic Data Exploration -a Part 6 4:2
Lecture 77 Basic Data Exploration -b Part 6 4:59
Lecture 78 Basic Data Exploration -c Part 6 5:4
Lecture 79 Basic Data Exploration -d Part 6 3:45
Lecture 80 Basic Data Cleaning Part -7 4:5
Lecture 81 Defining X & Y -Part 8 3:22
Lecture 82 Defining Model -Part 9 1:19
Lecture 83 Performing Cross-validation-Part 10 3:23
Lecture 84 Performing predictions on test data set-Part 1 Pdf

Section 19 : Other Models in Python

Lecture 85 K-Nearest Neighbour Pdf
Lecture 86 Random Forest Classifier 8:50

Section 20 : Regression from Scratch in Python

Lecture 87 Part-1 6:31
Lecture 88 Part-2 14:21
Lecture 89 Part-3 Pdf
Lecture 90 Part-4 6:12