Description

This six-week, hands-on course is designed for students who want to learn the essentials of machine learning and how to apply them in practice. Students will explore core topics including supervised and unsupervised learning, neural networks, and convolutional neural networks. Each week will feature interactive Google Colab notebooks, enabling students to apply their knowledge hands-on. The course will culminate with a mini-project, where students will build their own end-to-end classification model. By the end of the course, students will develop a strong foundational understanding of machine learning, empowering them to tackle real-world problems with their own models.

Syllabus

1. Basics of Machine Learning, Supervised vs. Unsupervised Learning
2. Neural Networks and Gradient Descent
3. Convolutional Neural Networks
4. Popular Machine Learning Algorithms
5. Sequence and Time-Series Models
6. Real-World Strategies & Best Practices for ML