CS-Machine Learning Course

CS - Machine Learning

• Lesson 01 Course Introduction

  04:52Preview

  • Course Introduction

    04:52

• Lesson 02 Introduction to AI and Machine Learning

  19:36Preview

  • 2.1 Learning Objectives

    00:43

  • 2.2 Emergence of Artificial Intelligence

    01:56

  • 2.3 Artificial Intelligence in Practice

    01:48

  • 2.4 Sci-Fi Movies with the Concept of AI

    00:22

  • 2.5 Recommender Systems

    00:45

  • 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science Part A

    02:47

  • 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science Part B

    01:23

  • 2.8 Definition and Features of Machine Learning

    01:30

  • 2.9 Traditional Approach vs. Machine Learning Approach

    01:48

  • 2.10 Machine Learning Techniques

    02:21

  • 2.11 Applications of Machine Learning Part A

    01:34

  • 2.12 Applications of Machine Learning Part B

    02:11

  • 2.13 Key Takeaways

    00:28

• Lesson 03 Data Preprocessing

  36:19Preview

  • 3.1 Learning Objectives

    00:38

  • 3.2 Data Exploration Loading Files Part A

    02:52

  • 3.2 Data Exploration Loading Files Part B

    01:56

  • 3.3 Demo: Importing and Storing Data

    01:27

  • 3.4 Data Exploration Techniques Part One

    02:56

  • 3.5 Data Exploration Techniques Part Two

    02:47

  • 3.6 Seaborn

    02:18

  • 3.7 Demo: Correlation Analysis

    02:38

  • 3.8 Data Wrangling

    01:27

  • 3.9 Missing Values in a Dataset

    01:55

  • 3.10 Outlier Values in a Dataset

    01:49

  • 3.11 Demo: Outlier and Missing Value Treatment

    04:18

  • 3.12 Data Manipulation

    00:47

  • 3.13 Functionalities of Data Object in Python Part A

    01:49

  • 3.14 Functionalities of Data Object in Python Part B

    01:33

  • 3.15 Different Types of Joins

    01:32

  • 3.16 Typecasting

    01:23

  • 3.17 Demo: Labor Hours Comparison

    01:54

  • 3.18 Key Takeaways

    00:20

• Lesson 04 Supervised Learning

  01:21:04Preview

  • 4.1 Learning Objectives

    00:31

  • 4.2 Supervised Learning

    02:17

  • 4.3 Supervised Learning- Real Life Scenario

    00:53

  • 4.4 Understanding the Algorithm

    00:52

  • 4.5 Supervised Learning Flow

    01:50

  • 4.6 Types of Supervised Learning Part A

    01:54

  • 4.7 Types of Supervised Learning Part B

    02:03

  • 4.8 Types of Classification Algorithms

    01:01

  • 4.9 Types of Regression Algorithms Part A

    03:20

  • 4.10 Regression Use Case

    00:34

  • 4.11 Accuracy Metrics

    01:23

  • 4.12 Cost Function

    01:48

  • 4.13 Evaluating Coefficients

    00:53

  • 4.14 Demo: Linear Regression

    13:47

  • 4.15 Challenges in Prediction

    01:45

  • 4.16 Types of Regression Algorithms Part B

    02:40

  • 4.17 Demo: Bigmart

    21:55

  • 4.18 Logistic Regression Part A

    01:58

  • 4.19 Logistic Regression Part B

    01:38

  • 4.20 Sigmoid Probability

    02:05

  • 4.21 Accuracy Matrix

    01:36

  • 4.22 Demo: Survival of Titanic Passengers

    14:07

  • 4.23 Key Takeaways

    00:14

• Lesson 05 Feature Engineering

  27:52Preview

  • 5.1 Learning Objectives

    00:27

  • 5.2 Feature Selection

    01:28

  • 5.3 Regression

    00:53

  • 5.4 Factor Analysis

    01:57

  • 5.5 Factor Analysis Process

    01:05

  • 5.6 Principal Component Analysis (PCA)

    02:31

  • 5.7 First Principal Component

    02:43

  • 5.8 Eigenvalues and PCA

    02:32

  • 5.9 Demo: Feature Reduction

    05:47

  • 5.10 Linear Discriminant Analysis

    02:27

  • 5.11 Maximum Separable Line

    00:44

  • 5.12 Find Maximum Separable Line

    03:12

  • 5.13 Demo: Labeled Feature Reduction

    01:53

  • 5.14 Key Takeaways

    00:13

• Lesson 06 Supervised Learning Classification

  55:46Preview

  • 6.1 Learning Objectives

    00:34

  • 6.2 Overview of Classification

    02:05

  • 6.3 Classification A Supervised Learning Algorithm

    00:52

  • 6.4 Use Cases

    02:37

  • 6.5 Classification Algorithms

    00:20

  • 6.6 Decision Tree Classifier

    02:17

  • 6.7 Decision Tree Examples

    01:45

  • 6.8 Decision Tree Formation

    00:47

  • 6.9 Choosing the Classifier

    02:55

  • 6.10 Overfitting of Decision Trees

    01:00

  • 6.11 Random Forest Classifier Bagging and Bootstrapping

    02:22

  • 6.12 Decision Tree and Random Forest Classifier

    01:06

  • 6.13 Performance Measures Confusion Matrix

    02:21

  • 6.14 Performance Measures Cost Matrix

    02:06

  • 6.15 Demo: Horse Survival

    08:30

  • 6.16 Naive Bayes Classifier

    01:28

  • 6.17 Steps to Calculate Posterior Probability Part A

    01:44

  • 6.18 Steps to Calculate Posterior Probability Part B

    02:21

  • 6.19 Support Vector Machines Linear Separability

    01:05

  • 6.20 Support Vector Machines Classification Margin

    02:05

  • 6.21 Linear SVM Mathematical Representation

    02:03

  • 6.22 Non linear SVMs

    01:06

  • 6.23 The Kernel Trick

    01:19

  • 6.24 Demo: Voice Classification

    10:42

  • 6.25 Key Takeaways

    00:16

• Lesson 07 Unsupervised Learning

  28:29Preview

  • 7.1 Learning Objectives

    00:30

  • 7.2 Overview

    01:50

  • 7.3 Example and Applications of Unsupervised Learning

    02:17

  • 7.4 Clustering

    01:49

  • 7.5 Hierarchical Clustering

    02:28

  • 7.6 Hierarchical Clustering Example

    02:01

  • 7.7 Demo: Clustering Animals

    05:39

  • 7.8 K-means Clustering

    01:46

  • 7.9 Optimal Number of Clusters

    01:24

  • 7.10 Demo: Cluster Based Incentivization

    08:32

  • 7.11 Key Takeaways

    00:13

• Lesson 08 Time Series Modeling

  37:44Preview

  • 8.1 Learning Objectives

    00:24

  • 8.2 Overview of Time Series Modeling

    02:16

  • 8.3 Time Series Pattern Types Part A

    02:16

  • 8.4 Time Series Pattern Types Part B

    01:19

  • 8.5 White Noise

    01:07

  • 8.6 Stationarity

    02:13

  • 8.7 Removal of Nonstationarity

    02:13

  • 8.8 Demo: Air Passengers ONE

    14:33

  • 8.9 Time Series Models Part A

    02:14

  • 8.10 Time Series Models Part B

    01:28

  • 8.11 Time Series Models Part C

    01:51

  • 8.12 Steps in Time Series Forecasting

    00:37

  • 8.13 Demo: Air Passengers TWO

    05:01

  • 8.14 Key Takeaways

    00:12

• Lesson 09 Ensemble Learning

  35:41Preview

  • 9.01 Ensemble Learning

    00:24

  • 9.2 Overview

    02:41

  • 9.3 Ensemble Learning Methods Part A

    02:28

  • 9.4 Ensemble Learning Methods Part B

    02:37

  • 9.5 Working of AdaBoost

    01:43

  • 9.6 AdaBoost Algorithm and Flowchart

    02:28

  • 9.7 Gradient Boosting

    02:36

  • 9.8 XGBoost

    02:23

  • 9.9 XGBoost Parameters Part A

    03:15

  • 9.10 XGBoost Parameters Part B

    02:30

  • 9.11 Demo: Pima Indians Diabetes

    04:14

  • 9.12 Model Selection

    02:08

  • 9.13 Common Splitting Strategies

    01:45

  • 9.14 Demo: Cross Validation

    04:18

  • 9.15 Key Takeaways

    00:11

• Lesson 10 Recommender Systems

  25:45Preview

  • 10.1 Learning Objectives

    00:28

  • 10.2 Introduction

    02:17

  • 10.3 Purposes of Recommender Systems

    00:45

  • 10.4 Paradigms of Recommender Systems

    02:45

  • 10.5 Collaborative Filtering Part A

    02:14

  • 10.6 Collaborative Filtering Part B

    01:58

  • 10.7 Association Rule Mining

    01:47

  • 10.8 Association Rule Mining Market Basket Analysis

    01:43

  • 10.9 Association Rule Generation Apriori Algorithm

    00:53

  • 10.10 Apriori Algorithm Example Part A

    02:11

  • 10.11 Apriori Algorithm Example Part B

    01:18

  • 10.12 Apriori Algorithm Rule Selection

    02:52

  • 10.13 Demo: User Movie Recommendation Model

    04:19

  • 10.14 Key Takeaways

    00:15

• Lesson 11 Text Mining

  43:58Preview

  • 11.1 Learning Objectives

    00:22

  • 11.2 Overview of Text Mining

    02:11

  • 11.3 Significance of Text Mining

    01:26

  • 11.4 Applications of Text Mining

    02:23

  • 11.5 Natural Language ToolKit Library

    02:35

  • 11.6 Text Extraction and Preprocessing Tokenization

    00:33

  • 11.7 Text Extraction and Preprocessing N grams

    00:55

  • 11.8 Text Extraction and Preprocessing Stop Word Removal

    01:24

  • 11.9 Text Extraction and Preprocessing Stemming

    00:44

  • 11.10 Text Extraction and Preprocessing Lemmatization

    00:35

  • 11.11 Text Extraction and Preprocessing POS Tagging

    01:17

  • 11.12 Text Extraction and Preprocessing Named Entity Recognition

    00:54

  • 11.13 NLP Process Workflow

    00:53

  • 11.14 Demo: Processing Brown Corpus

    10:05

  • 11.15 Structuring Sentences Syntax

    01:54

  • 11.16 Rendering Syntax Trees

    00:55

  • 11.17 Structuring Sentences Chunking and Chunk Parsing

    01:38

  • 11.18 NP and VP Chunk and Parser

    01:39

  • 11.19 Structuring Sentences Chinking

    01:44

  • 11.20 Context Free Grammar (CFG)

    01:56

  • 11.21 Demo: Twitter Sentiments

    07:46

  • 11.22 Key Takeaways

    00:09