Machine Learning
Overview
Welcome! In this course you will learn about the state of the art of Machine Learning and also gain practice implementing and deploying machine learning algorithms.
This course is destined for students of Data Science Filiรจre in EFREI Paris engineering school. In Data Science Filiรจre there is the following master programs:
- Data & Artificial Intelligence
- Data Engineering
- Business intelligence and Analytics
- BioInformatics
The aim of Machine Learning is to build computer systems that can adapt to their environments and learn from experience. Learning techniques and methods from this field are successfully applied to a variety of learning tasks in a broad range of areas, including, for example, spam recognition, text classification, gene discovery, financial forecasting. The course will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as regression and classification. The course will give you the basic ideas and intuition behind these methods, as well as a more formal statistical and computational understanding. You will have an opportunity to experiment with machine learning techniques in Python and apply them to a selected problem.
Schedule
| Session | Topic | Slides | Lab |
|---|---|---|---|
| 1 | Introduction to ML Regression |
๐ | ๐ป |
| 2 | Classification: Logistic Regression & Regularization | ๐ | ๐ป |
| 3 | Decision Trees & Random Forests | ๐ | ๐ป |
| 4 | Introduction to Neural Networks & Deep Learning | ๐ | ๐ป |
| 5 | Challenge | ๐ป |