MACHINE LEARNING & FOUNDATIONS (2 CREDITS)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – define machine learning concepts, techniques and algorithms; LO2 – explain machine learning process for the algorithms and for Phyton; LO3 – design the machine learning models and Phyton to solve problems; LO4 – analyze the machine learning models and Phyton to solve problems.

Topics:

1. Machine learning basics;
2. Supervised Learning;
3. Unsupervised Learning and Pre-processing;
4. Representing Data and Engineering;
5. Feature Engineering and Selection (1);
6. Feature Engineering and Selection (2);
7. Building, Tuning, and Deploying Models;
8. Model Evaluation and Improvement;
9. Real-World Case Studies (1);
10. Real-World Case Studies (2);
11. Real-World Case Studies (3);
12. Project presentation;
13. Processing, Wrangling, and Visualizing Data.