#### 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