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.
Dec 12, 2022
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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.
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
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
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
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
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%.
The UET Advantage
Receive unparalleled guidance from industry mentors, teaching assistants and graders
Live Classes by Industry Experts
Student Support is available all day for your convenience (24*7)
For urgent queries, use the Call Back option on the platform.