Lesson 01 Course Introduction
Course Introduction
Accessing Practice Lab
Lesson 02 Introduction to AI and Machine Learning
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 AI
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 Takeaways
Knowledge Check
Lesson 03 Data Preprocessing
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
Lesson 04 Supervised Learning
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 Algorithms: Part A
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 Takeaways
Knowledge Check
Health Insurance Cost
Lesson 05 Feature Engineering
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
Simplifying Cancer Treatment
Lesson 06 Supervised Learning Classification
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
Classify Kinematic Data
Lesson 07 Unsupervised Learning
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
Clustering Image Data
Lesson 08 Time Series Modeling
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
IMF Commodity Price Forecast
Lesson 09 Ensemble Learning
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
Tuning Classifier Model with XGBoost
Lesson 10 Recommender Systems
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
Book Rental Recommendation
Lesson 11 Text Mining
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
FIFA World Cup
Lesson 12 Project Highlights
Project Highlights
Uber Fare Prediction
Amazon – Employee Access
Practice Projects
California Housing Price Prediction
Phishing Detector with LR
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