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
Machine Learning with Python
This course is part of multiple programs.
Instructors: SAEED AGHABOZORGI +1 more
482,835 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(16,422 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
- Category: Machine Learning
- Category: regression
- Category: Hierarchical Clustering
- Category: classification
- Category: SciPy and scikit-learn
Details to know
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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 (2,995 ratings)SAEED AGHABOZORGI
IBM
4 Courses486,741 learners
Joseph Santarcangelo
IBM
33 Courses1,670,573 learners
Offered by
IBM
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Frequently asked questions
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