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AI & Machine Learning

This course will help you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning. This Machine Learning Training will also help you understand the concepts of Statistics, Time Series, and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Python Machine Learning Training, students will be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

Online Learning

Learning Format

8-10 Weeks

Recommended 12-15 hrs/week


Transferrable College Credits*

Dec 12, 2022

Course Start Date

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+1 (424) 454-0406

Program Overview

Key Highlights

5+ Case Studies & Real Life Projects

Personalized Mentorship and Query Resolution Sessions with Industry Experts

Earn Transferable College Credits  Fraction of the Cost

Gain Access to Virtual Cloud Labs

Online Instructor-led classes

Complimentary Access to Career Counselling Sessions

7+ Programming Tools & Languages

World Class Faculty Members & Industry Experts

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Top Skills You will Learn

Various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning

Advance your Career

Blockchain Developer, Blockchain solution architect, Blockchain project manager,Blockchain legal consultants

Earn College Credits

This course has been evaluated and recommended by ACE for 3 credit hour in the higher division baccalaureate degree category.


There are no prerequisites for enrollment to the Cyber Security Specialization.

Course Fee

USD 1500

(USD 500 (per credit)


USD 600

(USD 200 per credit)

Only a few seats left!

Flat 60% Off. Discount Pre-applied.

Hurry Up! Offer Valid only until Dec 4, 2022

Programming Languages & Tools Covered

Course Curriculum & Syllabus

Experience 100+ hours of best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments, and live sessions. 

Hours of Content

Case Studies & Projects

Programming Tools & Languages

Part 1 -  Data Science Fundamentals

Topics Covered: Python Scripting, Object-Oriented Programming, Introduction to Data Science, Data Extraction, Wrangling, & Visualization

Learning Objectives:

  • Learn the fundamentals of Python programming language.

  • Learn how Data Science helps to analyze large and unstructured data with different tools.

  • Understand the Life cycle of Data Science

  • Learn about Tools used for Data Science

  • Learn different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Part 2- Machine Learning with Python

Topics Covered: Machine Learning with Python Fundamentals, Supervised Learning, Dimensionality Reduction, Unsupervised Learning

Learning Objectives:

  • Learn the concept of Machine Learning with Python and it’s types.

  • Learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

  • Learn about impact of dimensions within data. 

  • Learn how to perform factor analysis using PCA and compress dimensions.\learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Part 3- Advanced Machine Learning

Topics Covered: Association Rules Mining and Recommendation Systems, Reinforcement Learning, Time Series Analysis, Model Selection, and Boosting

Learning Objectives:

  • Learn Association rules and their extension towards recommendation engines with Apriori algorithm.

  • Learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by.

  • Learn how to define an optimal solution for an agent-based on-agent environment interaction.

  • Learn Markov’s Decision Process

  • Learn about Time Series Analysis to forecast dependent variables based on time. 

  • Learn about Model Selection and Boosting


Full Stack Web Development specialization course is eligible for 6 college credits. 

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Grading Policy

Your grade for this course will be calculated out of 1200 points. The minimum score required to pass and become eligible for college credit for this course is 840 points, or an overall course grade of 70%. 

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Sydney University

The UET Advantage

Learning Support

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Industry Mentors

  • Receive unparalleled guidance from industry mentors, teaching assistants and graders

  • Live Classes by Industry Experts

Student Support

  • Student Support is available all day for your convenience (24*7)

  • For urgent queries, use the Call Back option on the platform.