Introduction to Machine Learning with Python

Online

Build a stronger understanding of foundational machine learning by using Python. This online course is designed for professionals looking to upskill as well as students and job seekers interested in learning the fundamentals of machine learning applications and data mining in Python. Skills gained in the course can be used across a broad array of industry roles, including marketing, business analytics, data analytics, and web and Python programming. 

Through online lectures, interactive assignments and projects, you’ll develop practical applications such as search engines, image analysis, bioinformatics, industrial automation, speech recognition and more. Examine machine learning concepts, gain a basic understanding of supervised machine learning and Bayesian classifiers, learn concepts in unsupervised machine learning and clustering algorithms and apply neural networks to machine learning. 

Learning Outcomes 

  • Identify and formulate machine learning problems using Python 
  • Understand and implement algorithms to solve simple machine learning problems 
  • Analyze the performance of machine learning solutions on practical datasets 

Skills You’ll Gain 

  • Usage of histograms for supervised machine learning and Bayesian classifiers  
  • Design and application of classifiers like k-nearest neighbors, linear machines and decision trees  
  • Unsupervised machine learning and cluster algorithms such as expectation maximization and k-means clustering 
Academic Units
2
Section Number
233PYT305
Instruction Method
Online class

Section Notes

This is an online course with class materials that can be accessed throughout the week. The course is structured to move from one week to the next.

Access to the online course platform, Canvas, will be granted to students one business day before the scheduled start date of the course.

Students pursuing the full Python for Data Analysis Professional Concentration must earn a grade of C or higher (not a C minus) in order for this course to count towards the requirements for the Professional Concentration. Courses applied towards the Python for Data Analysis Professional Concentration must be completed within five years.

Refund Deadline: 3/25/24.  Refunds and/or enrollment transfers will not be approved after this date.

Enrollment Policies

Click here or visit https://cpe.ucdavis.edu/student-services/withdrawals-refunds-and-transfers to view complete enrollment policy information including details on withdrawals and transfers.

Refund Deadline: 3/25/24.  Refunds and/or enrollment transfers will not be approved after this date.

Prerequisites

Introduction to Python Programming (Course Number: 508127) or Python for Data Analysis (Course Number: 508130)

 

Technical Requirements

To ensure your success in this online course, please review our technical requirements page.

Discounts

Info Session Discount

Attendees of UC Davis Continuing and Professional Education Information Sessions may be eligible for $100 discount on a class by entering the Coupon Code provided at the Information Session.  Contact UC Davis Continuing and Professional Education Student Services office, at (800) 752-0881, if you have questions about this discount.