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.
Machine Learning with Python
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Machine Learning with Python
This course is part of multiple programs.
Instructors: SAEED AGHABOZORGI
492,186 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(16,552 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 13 hours
Learn at your own pace
94%
Most learners liked this course
What you'll 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'll gain
Details to know
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Add to your LinkedIn profile
Assessments
11 assignments
Taught in English
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There are 6 modules in this course
In this module, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.
What's included
5 videos2 assignments
In this module, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.
What's included
5 videos3 readings2 assignments2 app items
In this module, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.
What's included
5 videos1 reading2 assignments5 app items
What's included
4 videos1 reading2 assignments3 app items1 plugin
In this module, you will learn about clustering specifically k-means clustering. You learn how the k-means clustering algorithm works and how to use k-means clustering for customer segmentation.
What's included
3 videos2 assignments1 app item
In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.
What's included
3 readings1 assignment1 peer review1 app item
Instructors
Instructor ratings
4.7 (3,026 ratings)Offered by
Recommended if you're interested in Machine Learning
DeepLearning.AI
Coursera Project Network
University of London
University of Pennsylvania
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Learner reviews
4.7
16,552 reviews
- 5 stars
76.08%
- 4 stars
18.65%
- 3 stars
3.32%
- 2 stars
0.96%
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0.97%
Showing 3 of 16552
JL
5
Reviewed on Dec 5, 2018
I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
CA
4
Reviewed on Dec 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
FG
5
Reviewed on Aug 28, 2019
Very informative course, showing mostly how to use many different Machine Learning techniques. Although mathematical details are not discussed much, the intuition of the methods are discussed.
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Frequently asked questions
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