IBM

Machine Learning with Python

This course is part of multiple programs.

Taught in English

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SAEED AGHABOZORGI
Joseph Santarcangelo

Instructors: SAEED AGHABOZORGI

422,674 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(15,363 reviews)

|

94%

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

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 

Details to know

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Assessments

11 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(15,363 reviews)

|

94%

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

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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 quizzes

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 videos2 readings2 quizzes2 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 quizzes5 app items

What's included

4 videos1 reading2 quizzes3 app items

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 quizzes1 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 quiz1 peer review1 app item

Instructors

Instructor ratings
4.7 (2,730 ratings)
SAEED AGHABOZORGI
IBM
4 Courses426,590 learners
Joseph Santarcangelo
IBM
25 Courses1,353,097 learners

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IBM

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