Introduction to Machine Learning with Python and pip

In this article, we will explore the world of machine learning using Python and pip. The objective of this course is to provide a broad survey of approaches and techniques in machine learning. Over the next few weeks, we will cover a variety of topics that are essential for anyone looking to learn about machine learning.

Course Objectives

The main objectives of this course are to introduce students to the fundamental concepts and algorithms that enable computational artifacts to modify and improve their performance through experience. We will be covering decision trees, logistic regression, support vector machines, ensemble methods, Bayesian methods, neural networks, clustering, and dimensionality reduction.

Instructor Information

Our instructors for this course are Sujie Zhou and Romain Camilleri. Sujie's office hours are on Fridays from 3:00pm-5:00pm in CSE2 151, while Romain's office hours are on Thursdays from 1:00pm-3:00pm in CSE2 150. Both instructors will also be holding additional office hours on Monday and Wednesday mornings.

Communication

Please note that all communication with the instructor and TAs must be done through this email unless there is a reason for privacy. All announcements and resources will be posted via Canvas, so be sure to check regularly for updates.

In conclusion, we are excited to embark on this journey of machine learning with Python and pip. With the guidance of our instructors and the support of our peers, we are confident that we will gain a comprehensive understanding of this exciting field.

Python2 pip