Build a strong foundation in Machine Learning by learning how to design, train, and evaluate models using real-world datasets. This course covers key algorithms such as regression, classification, decision trees, and ensemble methods, along with essential concepts like model evaluation, hyperparameter tuning, and handling imbalanced data. You will also explore unsupervised learning techniques and deployment strategies, helping you move from theory to practical implementation.
Requirements
- Basic computer skills like Windows, Internet, etc.
- Stable internet connection for accessing course content
- Willingness to learn and solve real-world problems
- Basic familiarity with using the terminal/command line (helpful but not required)
Curriculum
- 3 Sections
- 15 Lessons
- 3 Weeks
Expand all sectionsCollapse all sections
- Week 12
- Week 27
- Week 36
FAQs
Yes! The course starts from the very basics
2 years. Watch as many times you want to watch.
Instructor
