CS207: Resources | Saylor Academy

Topic	Name	Description
	Course Syllabus

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Course Introduction

Course Syllabus

Unit 1: Introduction to Machine Learning

1.1: What is Machine Learning?

1.2: Types of Machine Learning

1.3: ML vs. AI vs. Data Science

Unit 1 Assessment

Unit 2: Machine Learning Workflow

2.1: The Machine Learning Pipeline

2.2: Significance of Each Stage

Unit 2 Assessment

Unit 3: Data Preprocessing

3.1: Data Cleaning Techniques

3.2: Normalization

3.3: Data Transformation: Encoding Categorical Variables

Unit 3 Assessment

Unit 4: Data Visualization

4.1: Data Visualization Techniques

4.2: Interpreting Visual Data

4.3: Feature Engineering

Unit 4 Assessment

Unit 5: Supervised Learning – Regression

5.1: Introduction to Regression

5.2: Evaluating Regression Models

5.3: Limitations of Regression Models

Unit 5 Assessment

Unit 6: Supervised Learning – Classification

6.1: Logistic Regression

6.2: Classification

6.3: Evaluating Classification Models

Unit 6 Assessment

Unit 7: Unsupervised Learning – Clustering

7.1: Introduction to Clustering

7.2: K-Means Clustering

7.3: Analyzing Clustering Results

Unit 7 Assessment

Unit 8: Model Evaluation and Validation

8.1: Train-Test Split and Cross-Validation

8.2: Overfitting and Underfitting

8.3: Techniques to Avoid Overfitting

Unit 8 Assessment

Unit 9: Practical Implementation of ML Models

9.1: Developing an ML Project

9.2: Project Documentation and Reproducibility

9.3: Version Control

Unit 9 Assessment

Unit 10: Ethical and Responsible AI

10.1: Ethical Considerations in ML

10.2: Responsible AI Practices

Unit 10 Assessment

Study Guide

Certificate Final Exam

Course Feedback Survey