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
Online
In Person
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

Before embarking on the ‘Machine Learning’ course, it is imperative to establish a robust foundational knowledge. We suggest the following preparatory steps:

Introduction to Data Science: Acquaint yourself with the basic principles of data science.

Statistics Background: In case you lack proficiency in statistics, we recommend enrolling in a course such as Introduction to Statistical Reasoning. Grasping statistical concepts is a key determinant of success in machine learning.

Winter 2025 Schedule

Date & Time
Details
Format
 
-
Monday 6:00PM - 9:30PM PT
Future Offering (Opens November 04, 2024 12:00:00 AM)
See Details
Instructor: Benjamin Winjum
400713
Fee:
$1,095.00
In Personformat icon
Location: UCLA
Notes

Enrollment limited; early enrollment advised. Visitors not permitted.

Enrollment deadline: January 20th, 2025

Refund Deadline
No refunds after January 19, 2025
Course Requirements
Internet access required to retrieve course materials.
Schedule
Type
Date
Time
Location
Discussion
Mon Jan 13, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Jan 20, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Jan 27, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Feb 3, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Feb 10, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Feb 17, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Feb 24, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Mar 3, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
Discussion
Mon Mar 10, 2025
6:00PM PT - 9:30PM PT
UCLA
Rolfe Hall 3126
-
This section has no set meeting times.
Future Offering (Opens November 04, 2024 12:00:00 AM)
See Details
Instructor: Joel Kowalewski
400712
Fee:
$1,095.00
Onlineformat icon
Notes

Enrollment limited; early enrollment advised. Enrollment deadline: January 20th, 2024

Refund Deadline
No refunds after January 10, 2025
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…

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