CS207: Fundamentals of Machine Learning

Topic	Name	Description
	Course Introduction Video
	Course Syllabus
Unit 1: Introduction to Machine Learning	Unit 1 Introduction Video
	Unit 1 Learning Outcomes
1.1: What is Machine Learning?	Introduction to Machine Learning
1.2: Types of Machine Learning	Types of ML Systems
1.3: ML vs. AI vs. Data Science	AI, ML, and Data Science: Roles and Relationships
Unit 2: Machine Learning Workflow	Unit 2 Introduction Video
	Unit 2 Learning Outcomes
2.1: The Machine Learning Pipeline	ML Pipeline
2.2: Significance of Each Stage	Significance of Each Stage in ML Software Development
Unit 3: Data Preprocessing	Unit 3 Introduction Video
	Unit 3 Learning Outcomes
3.1: Data Cleaning Techniques	Data Cleaning Techniques	This is a book resource with multiple pages. Navigate between the pages using the buttons.
3.2: Normalization	Understanding Normalization of Numerical Data
3.3: Data Transformation: Encoding Categorical Variables	Categorical Data: Vocabulary and One-Hot Encoding
Unit 4: Data Visualization	Unit 4 Introduction Video
	Unit 4 Learning Outcomes
4.1: Data Visualization Techniques	Visual Data Analysis
4.2: Interpreting Visual Data	Interpretation of Data Visualizations
4.3: Feature Engineering	Feature Engineering and Feature Selection	This is a book resource with multiple pages. Navigate between the pages using the buttons.
Unit 5: Supervised Learning – Regression	Unit 5 Introduction Video
	Unit 5 Learning Outcomes
5.1: Introduction to Regression	Linear Regression
5.2: Evaluating Regression Models	Evaluating Regression Models: Metrics and Loss Functions	This is a book resource with multiple pages. Navigate between the pages using the buttons.
5.3: Limitations of Regression Models	Multicollinearity	This is a book resource with multiple pages. Navigate between the pages using the buttons.
Unit 6: Supervised Learning – Classification	Unit 6 Introduction Video
	Unit 6 Learning Outcomes
6.1: Logistic Regression	Logistic Regression: Sigmoid Function
6.2: Classification	Classification	This is a book resource with multiple pages. Navigate between the pages using the buttons.
6.3: Evaluating Classification Models	Accuracy, Recall, Precision, and Related Metrics	This is a book resource with multiple pages. Navigate between the pages using the buttons.
Unit 7: Unsupervised Learning – Clustering	Unit 7 Introduction Video
	Unit 7 Learning Outcomes
7.1: Introduction to Clustering	Clustering	This is a book resource with multiple pages. Navigate between the pages using the buttons.
7.2: K-Means Clustering	What is K-Means Clustering?
7.3: Analyzing Clustering Results	Manual Similarity Measure
	Evaluating Results
Unit 8: Model Evaluation and Validation	Unit 8 Introduction Video
	Unit 8 Learning Outcomes
8.1: Train-Test Split and Cross-Validation	Training and Evaluation of AI/ML Models
8.2: Overfitting and Underfitting	Fitting, Overfitting, and Underfitting
8.3: Techniques to Avoid Overfitting	Model Complexity	This is a book resource with multiple pages. Navigate between the pages using the buttons.
Unit 9: Practical Implementation of ML Models	Unit 9 Introduction Video
	Unit 9 Learning Outcomes
9.1: Developing an ML Project	Sales Insight: Logistic Regression for Purchase Prediction
9.2: Project Documentation and Reproducibility	Reproducible Research
9.3: Version Control	Version Control System
Unit 10: Ethical and Responsible AI	Unit 10 Introduction Video
	Unit 10 Learning Outcomes
10.1: Ethical Considerations in ML	Ethical Issues
10.2: Responsible AI Practices	Ways to Prioritize Responsible Practices
Study Guide	CS207 Study Guide
Course Feedback Survey	Course Feedback Survey