1.1.Course Introduction

1.2.Accessing Practice Lab

2.1.Learning objectives

2.2.Emergence of Artificial Intelligence

2.3. Artificial Intelligence in Practice

2.4. Sci-Fi Movies with the Concept of Artificial Intelligence

2.5. Recommender Systems

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

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

2.8 Definition and Features of Machine Learning

2.9 Machine Learning Approaches

2.10 Machine Learning Techniques

2.11. Applications of machine learning: Part A

2.12. Applications of machine learning: Part B

2.13. Key Takeways

3.1. Learning Objectives

3.2. Data Exploration Loading Files: Part A

3.2. Data Exploration Loading Files: Part B

3.3. Demo: Importing and Storing Data

Practice: Automobile Data Exploration – A

3.4. Data Exploration Techniques: Part A

3.5. Data Exploration Techniques: Part B

3.6. Seaborn

3.7. Demo: Correlation Analysis

Practice: Automobile Data Exploration – B

3.8. Data Wrangling

3.9. Missing Values in a Dataset

3.10. Outlier Values in a Dataset

3.11. Demo: Outlier and Missing Value Treatment

Practice: Data Exploration – C

3.12 Data Manipulation

3.13 Functionalities of Data Object in Python: Part A

3.14 Functionalities of Data Object in Python: Part B

3.15 Different Types of Joins

3.16 Typecasting

3.17 Demo: Labor Hours Comparison

Practice: Data Manipulation

3.18 Key Takeaways

Knowledge Check

Storing Test Results

4.1. Learning Objectives

4.2. Supervised Learning

4.3 Supervised Learning- Real-Life Scenario

4.4 Understanding the Algorithm

4.5 Supervised Learning Flow

4.6 Types of Supervised Learning: Part A

4.7 Types of Supervised Learning: Part B

4.8 Types of Classification Algorithms

4.9 Types of Regression Algorithm: PartA

4.10 Regression Use Case

4.11 Accuracy Metrics

4.12 Cost Function

4.13 Evaluating Coefficients

4.14 Demo: Linear Regression

Practice: Boston Homes – A

4.15 Challenges in Prediction

4.16 Types of Regression Algorithms: Part B

4.17 Demo: Bigmart

Practice: Boston Homes – B

4.18 Logistic Regression: Part A

4.19 Logistic Regression: Part B

4.20 Sigmoid Probability

4.21 Accuracy Matrix

4.22 Demo: Survival of Titanic Passengers

Practice: Iris Species

4.23 Key Takeways

Knowledge Check

5.1 Learning Objectives

5.2 Feature Selection

5.3 Regression

5.4 Factor Analysis

5.5 Factor Analysis Process

5.6 Principal Component Analysis (PCA)

5.7 First Principal Component

5.8 Eigenvalues and PCA

5.9 Demo: Feature Reduction

Practice: PCA Transformation

5.10 Linear Discriminant Analysis

5.11 Maximum Separable Line

5.12 Find Maximum Separable Line

5.13 Demo: Labeled Feature Reduction

Practice: LDA Transformation

5.14 Key Takeaways

Knowledge Check

6.1 Learning Objectives

6.2 Overview of Classification

Classification: A Supervised Learning Algorithm

6.4 Use Cases of Classification

6.5 Classification Algorithms

6.6 Decision Tree Classifier

6.7 Decision Tree Examples

6.8 Decision Tree Formation

6.9 Choosing the Classifier

6.10 Overfitting of Decision Trees

6.11 Random Forest Classifier- Bagging and Bootstrapping

6.12 Decision Tree and Random Forest Classifier

Performance Measures: Confusion Matrix

Performance Measures: Cost Matrix

6.15 Demo: Horse Survival

Practice: Loan Risk Analysis

6.16 Naive Bayes Classifier

6.17 Steps to Calculate Posterior Probability: Part A

6.18 Steps to Calculate Posterior Probability: Part B

6.19 Support Vector Machines : Linear Separability

6.20 Support Vector Machines : Classification Margin

6.21 Linear SVM : Mathematical Representation

6.22 Non-linear SVMs

6.23 The Kernel Trick

6.24 Demo: Voice Classification

Practice: College Classification

6.25 Key Takeaways

Knowledge Check

7.1 Learning Objectives

7.2 Overview

7.3 Example and Applications of Unsupervised Learning

7.4 Clustering

7.5 Hierarchical Clustering

7.6 Hierarchical Clustering Example

7.7 Demo: Clustering Animals

Practice: Customer Segmentation

7.8 K-means Clustering

7.9 Optimal Number of Clusters

7.10 Demo: Cluster Based Incentivization

Practice: Image Segmentation

7.11 Key Takeaways

Knowledge Check 

8.1 Learning Objectives

8.2 Overview of Time Series Modeling

8.3 Time Series Pattern Types: Part A

8.4 Time Series Pattern Types: Part B

8.5 White Noise

8.6 Stationarity

8.7 Removal of Non-Stationarity

8.8 Demo: Air Passengers – A

Practice: Beer Production – A

8.9 Time Series Models: Part A

8.10 Time Series Models: Part B

8.11 Time Series Models: Part C

8.12 Steps in Time Series Forecasting

8.13 Demo: Air Passengers – B

Practice: Beer Production – B

8.14 Key Takeaways

Knowledge Check

9.01 Ensemble Learning

9.2 Overview

9.3 Ensemble Learning Methods: Part A

9.4 Ensemble Learning Methods: Part B

9.5 Working of AdaBoost

9.6 AdaBoost Algorithm and Flowchart

9.7 Gradient Boosting

9.8 XGBoost

9.9 XGBoost Parameters: Part A

9.10 XGBoost Parameters: Part B

9.11 Demo: Pima Indians Diabetes

Practice: Linearly Separable Species

9.12 Model Selection

9.13 Common Splitting Strategies

9.14 Demo: Cross Validation

Practice: Model Selection

9.15 Key Takeaways

Knowledge Check

10.1 Learning Objectives

10.2 Introduction

10.3 Purposes of Recommender Systems

10.4 Paradigms of Recommender Systems

10.5 Collaborative Filtering: Part A

10.6 Collaborative Filtering: Part B

10.7 Association Rule Mining

Association Rule Mining: Market Basket Analysis

10.9 Association Rule Generation: Apriori Algorithm

10.10 Apriori Algorithm Example: Part A

10.11 Apriori Algorithm Example: Part B

10.12 Apriori Algorithm: Rule Selection

10.13 Demo: User-Movie Recommendation Model

Practice: Movie-Movie recommendation

10.14 Key Takeaways

Knowledge Check

11.1 Learning Objectives

11.2 Overview of Text Mining

11.3 Significance of Text Mining

11.4 Applications of Text Mining

11.5 Natural Language ToolKit Library

11.6 Text Extraction and Preprocessing: Tokenization

11.7 Text Extraction and Preprocessing: N-grams

11.8 Text Extraction and Preprocessing: Stop Word Removal

11.9 Text Extraction and Preprocessing: Stemming

11.10 Text Extraction and Preprocessing: Lemmatization

11.11 Text Extraction and Preprocessing: POS Tagging

11.12 Text Extraction and Preprocessing: Named Entity Recognition

11.13 NLP Process Workflow

11.14 Demo: Processing Brown Corpus

Wiki Corpus

11.15 Structuring Sentences: Syntax

11.16 Rendering Syntax Trees

11.17 Structuring Sentences: Chunking and Chunk Parsing

11.18 NP and VP Chunk and Parser

11.19 Structuring Sentences: Chinking

11.20 Context-Free Grammar (CFG)

11.21 Demo: Structuring Sentences

Practice: Airline Sentiment

11.22 Key Takeaways

Knowledge Check 

Project Highlights

Uber Fare Prediction

Amazon – Employee Access

 Practice Projects