Course
Data Science
Continuing Education

Machine Learning with Python

33 Hours

Estimated learning time

Self-Paced

Progress at your own speed

Popular course

A popular course among students

About the Course

Description

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.

By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

This Course is part of a program

You can only buy it along with program.

Sections

Schedule

Asynchronous

Delivery method

Online

Deliverables

  • 0 Credits

    Academic Excellence

    Earn necessary number of credit hours for completing this content

  • Hone Important Skills

    Total Upgrade

    Such as Machine Learning, Python Programming, Machine Learning Algorithms, Data Analysis, Algorithms, Statistical Machine Learning, Applied Machine Learning, Human Learning, Decision Making, Customer Analysis, Hierarchical Clustering, Statistical Classification, Data Clustering Algorithms, Regression, Python Libraries, K-Means Clustering