Machine Learning Using Python

COM SCI X 450.4

Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.

READ MORE ABOUT THIS COURSE
Fall
Winter
Spring
Summer
Live Online
Online
Starting at $1,095.00
As few as 10 weeks
4.0

What you can learn.

  • 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
  • Implement machine learning solutions

About this course:

This course introduces machine learning using Python. Students will learn structured and unstructured data processing, linear regression modeling and non-linear modeling methods used in machine learning algorithm development, optimization techniques, neural networks and deep learning. This field is made possible due to the rapid and simultaneous evolution of available data, statistical methods and computing power. Students learn the origins and practical applications of machine learning, how knowledge is defined and represented by computers, and the basic concepts that differentiate machine learning approaches. Machine learning algorithms can be divided into two main groups: supervised learners who are used to construct predictive models and unsupervised learners who are used to build descriptive models. Students learn the classification, numeric predictor, pattern detection and clustering algorithms. Students learn to train a model, evaluate and improve its performance. Algorithm uses are illustrated with real-world cases, such as breast cancer diagnosis, spam filtering, identifying bank loan risk, predicting medical expenses, estimating wine quality, identifying groceries frequently purchased together and finding teen market segments. 
Prerequisites
COM SCI X 450.1 Introduction to Data Science or consent of instructor.

Fall 2024 Schedule

Date & Time
Details
Format
 
-
Monday 6:00PM - 9:30PM PT
Future Offering (Opens July 29, 2024 12:00:00 AM)
See Details
Instructor: Benjamin Winjum
398903
Fee:
$1,095.00
Live Onlineformat icon
Location: UCLA
Notes

Enrollment limited. Enrollment deadline: June 30, 2024. Internet access required. Materials required.

Refund Deadline
No refunds after October 06, 2024
Course Requirements
Internet access required to retrieve course materials.
Schedule
Type
Date
Time
Location
Discussion
Mon Sep 23, 2024
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Oct 7, 2024
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Oct 21, 2024
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Nov 4, 2024
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Nov 18, 2024
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
-
This section has no set meeting times.
Future Offering (Opens July 29, 2024 12:00:00 AM)
See Details
Instructor: Joel Kowalewski
399629
Fee:
$1,095.00
Onlineformat icon
Notes

Enrollment limited. Enrollment deadline: October 7, 2024. Internet access required. Materials required.

Refund Deadline
No refunds after September 27, 2024
Course Requirements
Internet access required to retrieve course materials.

Contact Us

Our team members are here to help. Hours: Mon-Fri, 8am-5pm.

This course applies towards the following certificates & specializations…

Ready to start
your future?
By signing up, you agree to UCLA Extension’s Privacy Policy.

vector icon of building

Corporate Education

Learn how we can help your organization meet its professional development goals and corporate training needs.

Learn More

vector icon of building

Donate to UCLA Extension

Support our many efforts to reach communities in need.

Innovation Programs

Student Scholarships

Coding Boot Camp

Lifelong Learning