This project-based course is designed to follow the Machine Learning course and provides students with practical experience in applying machine learning techniques to real-world problems. The course covers a range of topics, including data cleaning and preprocessing, feature engineering, model selection and evaluation.
The course consists of 25-30 hands-on projects, each focusing on a specific machine learning problem and dataset. These projects are designed to give students practical experience in applying machine learning techniques to various domains, including healthcare, finance, e-commerce, and social media.
Each project will introduce a new dataset, and students will learn how to clean, preprocess, and explore the data using Python and essential libraries such as Pandas and NumPy. They will also learn how to perform feature engineering and selection, model selection, and evaluation using Scikit-Learn and other machine learning libraries.
The course culminates in a capstone project, where students will work on a real-world machine learning problem using a large dataset. They will apply all the skills they have learned throughout the course, including data cleaning, preprocessing, feature engineering, model selection, and evaluation, with a final project report and presentation.
By the end of this course, students will have gained practical experience in applying machine learning techniques to real-world problems and will be well-prepared for a career in machine learning. They will also have a portfolio of projects that they can showcase to potential employers.
Overall, this course is an excellent opportunity for students to deepen their understanding of machine learning techniques and gain hands-on experience in solving real-world problems using machine learning.