Machine Learning with Python Fundamentals
Master machine learning concepts and techniques using Python through hands-on practice with supervised and unsupervised learning methods.
Master machine learning concepts and techniques using Python through hands-on practice with supervised and unsupervised learning methods.
This comprehensive course introduces practical machine learning using Python. Students learn the fundamentals of both supervised and unsupervised learning, exploring key algorithms and statistical modeling techniques. The curriculum covers classification, regression, clustering, and dimensional reduction, with popular models like Random Forests and Support Vector Machines. Through hands-on labs, learners apply theoretical knowledge to real-world scenarios, building practical skills in implementing machine learning solutions.
4.5
(415 ratings)
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What you'll learn
Understand the fundamentals of supervised and unsupervised machine learning
Implement classification and regression algorithms using Python
Master clustering and dimensionality reduction techniques
Apply statistical modeling concepts to machine learning problems
Develop practical skills through hands-on programming exercises
Build real-world prediction models using various algorithms
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 5 modules in this course
This comprehensive course provides a practical introduction to machine learning using Python. The curriculum covers fundamental concepts including supervised and unsupervised learning, statistical modeling, and algorithm implementation. Students learn through hands-on practice with real-world applications, exploring various techniques like classification, regression, clustering, and dimensional reduction. The course emphasizes practical skill development through interactive labs and projects.
Introduction to Machine Learning
Module 1
Regression
Module 2
Classification
Module 3
Unsupervised Learning
Module 4
Recommender Systems
Module 5
Fee Structure
Instructor
38 Courses
Pioneering Data Scientist Leading Enterprise Analytics Innovation
Saeed Aghabozorgi, PhD, serves as a Senior Data Scientist at IBM, where he specializes in developing enterprise-level applications that transform complex data into actionable business knowledge. His expertise spans data mining, machine learning, and statistical modeling, with particular emphasis on large-scale datasets. As an accomplished educator, his courses have reached over 100,000 learners worldwide, maintaining an impressive 4.7 instructor rating. His most notable contribution includes the Machine Learning with Python course, which has enrolled more than 482,000 students and covers comprehensive topics from supervised learning to advanced clustering techniques. Through his work at IBM, he continues to advance the field of data science by developing cutting-edge analytical methods and sharing his expertise through educational initiatives that bridge the gap between theoretical knowledge and practical application.
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4.5 course rating
415 ratings
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