4.7
stars
13,761 ratings

SAEED AGHABOZORGI +1 more instructor

318,622 already enrolled

Offered By

Part of the Bachelor of Applied Arts and Sciences degree
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IBM Skills Network

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Intermediate Level

A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.

Approx. 12 hours to complete

English

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What you will learn

Describe the various types of Machine Learning algorithms and when to use them
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics

Skills you will gain

SciPy and scikit-learn
Machine Learning
regression
classification
Hierarchical Clustering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.

Approx. 12 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.71/5 (2,292 Ratings)

SAEED AGHABOZORGI

Ph.D., Sr. Data Scientist

322,282 Learners

4 Courses

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

914,752 Learners

26 Courses

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

Start working towards your Bachelor's degree

This course is part of the 100% online Bachelor of Applied Arts and Sciences from IBM Skills Network. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating94%(24,467 ratings)

Week1

Week 1

1 hour to complete

Introduction to Machine Learning

1 hour to complete

5 videos (Total 29 min)

Week2

Week 2

2 hours to complete

Regression

2 hours to complete

5 videos (Total 43 min)

Week3

Week 3

4 hours to complete

Classification

4 hours to complete

5 videos (Total 35 min), 1 reading, 7 quizzes

Week4

Week 4

3 hours to complete

Linear Classification

3 hours to complete

4 videos (Total 46 min), 1 reading, 5 quizzes

Reviews

4.7

2434 reviews

5 stars
75.97%
4 stars
18.64%
3 stars
3.41%
2 stars
0.94%
1 star
1.02%

TOP REVIEWS FROM MACHINE LEARNING WITH PYTHON

by FOOct 8, 2020

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

by CADec 31, 2019

could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice

by AGFeb 1, 2020

Quite an informative course, well presented material without being overbearing for newcomers to ML. Highly recommended to everyone with prior CS experience who wants to get into AI/ML workloads.

by SKAug 3, 2019

The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.

View all reviews

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Machine Learning with Python

About this Course

What you will learn

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