Introduction to Machine Learning Using Python

Get an introduction to machine learning using Python. Machine learning is a key skill for data scientists and specialized roles like machine learning engineer, AI developers and other AI focused positions. Many industries depend on practitioners of machine learning to create products that parse, reduce, simplify and categorize data, and then extract actionable intelligence from that data. In this course, you will use Python to learn machine learning concepts, terms and methodology, and gain an intuitive understanding of the mathematics underlying it by building actual applications. The techniques you'll learn can be a starting point to build real-world applications such as search engines, image analysis, bioinformatics, industrial automation, speech recognition and more.

Prerequisites:

Python programming experience
Knowledge of statistics as covered in an undergraduate level course

Learner Outcomes

Collect, explore, visualize, and prepare data for machine learning problems using Python
Understand how machine learning algorithms make predictions
Identify appropriate machine learning algorithms for your project
Train, evaluate, monitor, and improve machine learning models
Analyze the performance of machine learning solutions on datasets and learn to improve model metrics
Gain further familiarity with key libraries: pandas, numpy, matplotlib, seaborn, scikit learn.
Interpret and communicate the results of machine learning models to business stakeholders.

Sections

Oct 07 to Dec 16

Type: Live Online

Instructor:

Sridevi Pudipeddi

Cost: $1,045.00

6:00PM to 9:00PM Pacific Time

Oct 07, 2025 to Dec 16, 2025

Live Online

View Details

30.00

Live Online

Online

2 semester units

Section Notes

Students will receive access to the course in Canvas on the course start date. Zoom meeting information will be emailed to enrolled students before the first class meeting and posted to the announcements page in Canvas.

Section Materials

(Optional)
Python for Machine Learning by Sebastian Raschka and Vahid Mirjalili
© 2019 Packt 3rd edition ISBN 978-1789955750
(Optional)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligence Systems by Aurelien Geron
© 2019 O’Reilly Media 2nd edition ISBN 978-1492032649
(Optional)
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
© 2017 O’Reilly Media 2nd edition ISBN 978-1491957660

Fall 2025 enrollment opens on June 16!

This course applies to the following programs:

Learner Outcomes

Sections

Program in Data Science

Databases & Big Data

Data Science

Data Science Capstone

Notes

English Language Proficiency Requirements

Introduction to Machine Learning Using Python

Learner Outcomes

Sections

Section 027

Program in Data Science

Databases & Big Data

Data Science

Data Science Capstone