Sample Syllabus

Course Description

This course, Introduction to Machine Learning with Python (DS310), introduces students to the end-to-end process of developing machine learning models using Python and Scikit-Learn. Machine learning is a field of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. In this course, students will learn about supervised and unsupervised learning algorithms, model training techniques, model evaluation, and deployment.

Prerequisites

• DA210: Introduction to Data Analysis with Python

Corequisites

• DA320: Mathematics for Machine Learning

Learning Objectives

By the end of this course, students will be able to:

• Understand the principles and techniques of machine learning.
• Implement supervised and unsupervised learning algorithms using Python and Scikit-Learn.
• Train, evaluate, and deploy machine learning models.
• Apply machine learning techniques to real-world data sets.

Course Structure

The course content will be presented through a series of lectures, hands-on workshops, and projects. Students will be evaluated through assignments, a final exam, and a data science project.

Assignments

Throughout the semester, students will be given assignments that will require them to implement and evaluate machine learning models using Python and Scikit-Learn. These assignments will allow students to practice their machine learning skills.

Final Exam

At the end of the semester, students will take a final exam that will cover all the material presented in the lectures. The final exam will prioritize the understanding and application of machine learning concepts and techniques. The exam will count towards a significant portion of the overall course grade.

Schedule

The following is a general outline of the topics covered in the course:

Week	Topic
1	Introduction to Machine Learning
2	Supervised Learning: Regression
3	Supervised Learning: Classification
4	Unsupervised Learning: Clustering
5	Feature and Model Selection
6	Model Training Methods: Handling Bias & Overfitting
7	Model Evaluation
8	Continuous Monitoring
9	Ensemble Learning
10	Advanced Topics in Machine Learning
11	Machine Learning Applications
12	Final Exam Preparation and Course Reflection