Online based
Downloadable exercise
Offline viewing
Fixed/flex schedule
Fixed time homework
Certificate of completion
Objectives
- Understand the core principles of machine learning and its applications.
- Identify the differences between supervised, unsupervised, and reinforcement learning.
- Gain hands-on experience in applying popular ML algorithms to real-world datasets.
- Develop the ability to preprocess and visualize data for machine learning tasks.
- Learn to evaluate and tune machine learning models for improved performance.
- Implement and deploy basic machine learning models using Python.
- Apply machine learning techniques to various domains such as classification, regression, and clustering.
Level expectations
- Beginner-friendly, step-by-step guide for learning machine learning.
- Practical coding exercises and real-world examples using Python.
- Exposure to widely-used tools and libraries in machine learning.
- Understanding of key concepts and their practical applications.
- Opportunity to work with datasets and build models from scratch.
Content
- Introduction to Machine Learning
- Python for Machine Learning
- Data Preprocessing
- Supervised Learning: Regression
- Supervised Learning: Classification
- Unsupervised Learning
- Model Evaluation and Tuning
- Deep Learning Basics (Optional Module)