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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Haas School of Business, Berkeley |
The Syllabus
- Module 1: Introduction to Machine Learning
- Module 2: Fundamentals of Machine Learning
- Module 3: Introduction to Data Analysis
- Module 4: Fundamentals of Data Analysis
- Module 5: Practical Applications I
- Module 6: Clustering and Principal Component Analysis
- Module 7: Linear and Multiple Regression
- Module 8: Feature Engineering and Overfitting
- Module 9: Model Selection and Regularization
- Module 10: Time Series Analysis and Forecasting
- Module 11: Practical Applications II
- Module 12: Classification and k-Nearest Neighbors
- Module 13: Logistic Regression
- Module 14: Decision Trees
- Module 15: Gradient Descent and Optimization
- Module 16: Support Vector Machines
- Module 17: Practical Applications III
- Module 18: Natural Language Processing
- Module 19: Recommendation Systems
- Module 20: Capstone I
- Module 21: Ensemble Techniques (GBM, XGB, and Random Forest)
- Module 22: Deep Neural Networks I
- Module 23: Deep Neural Networks II
- Module 24: Capstone II