Machine Learning with Python

Machine Learning with Python

This course is part of multiple programs.

Instructors: SAEED AGHABOZORGI

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

Machine Learning

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from IBM

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 6 modules in this course

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.

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

5 videosTotal 43 minutes

Introduction to Regression4 minutesPreview module
Simple Linear Regression12 minutes
Model Evaluation in Regression Models8 minutes
Evaluation Metrics in Regression Models3 minutes
Multiple Linear Regression13 minutes

3 readingsTotal 30 minutes

Errata for "Evaluation Matrix in Regression Model" Video10 minutes
Errata: Simple Linear Regression Video10 minutes
Errata: Multiple Linear Regression Video10 minutes

2 assignmentsTotal 25 minutes

Graded Quiz: Regression15 minutes
Practice Quiz: Regression10 minutes

2 app itemsTotal 30 minutes

Lab: Simple Linear Regression15 minutes
Lab: Multiple Linear Regression15 minutes

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

5 videosTotal 35 minutes

Introduction to Classification3 minutesPreview module
K-Nearest Neighbours9 minutes
Evaluation Metrics in Classification7 minutes
Introduction to Decision Trees4 minutes
Building Decision Trees10 minutes

1 readingTotal 10 minutes

Regression Trees10 minutes

2 assignmentsTotal 25 minutes

Graded Quiz: Classification15 minutes
Practice Quiz: Classification10 minutes

5 app itemsTotal 180 minutes

Lab: KNN25 minutes
Lab: Decision Trees15 minutes
(Optional) Lab: Faster Credit Card Fraud Detection using Snap ML60 minutes
Lab: Regression Trees20 minutes
(Optional) Lab: Faster Taxi Tip Prediction using Snap ML60 minutes

What's included

4 videos1 reading2 assignments3 app items1 plugin

4 videosTotal 46 minutes

Intro to Logistic Regression8 minutesPreview module
Logistic regression vs Linear regression15 minutes
Logistic Regression Training13 minutes
Support Vector Machine8 minutes

1 readingTotal 10 minutes

Multiclass Prediction10 minutes

2 assignmentsTotal 25 minutes

Graded Quiz: Linear Classification15 minutes
Practice Quiz: Linear Classification10 minutes

3 app itemsTotal 70 minutes

Lab: Logistic Regression25 minutes
Lab: SVM (Support Vector Machines)15 minutes
Lab: Multiclass Prediction30 minutes

1 pluginTotal 15 minutes

GridSearchCV: Hyperparameter Tuning in Machine Learning15 minutes

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 (3,023 ratings)

SAEED AGHABOZORGI

IBM

4 Courses495,167 learners

Joseph Santarcangelo

IBM

33 Courses1,705,081 learners

Offered by

IBM

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

4.7

16,540 reviews

5 stars
76.08%
4 stars
18.64%
3 stars
3.33%
2 stars
0.96%
1 star
0.97%

Showing 3 of 16540

Reviewed on Jun 24, 2020

This course walks us through the fundamentals of machine learning methods. The capstone project is very useful for those who have previous knowledge of machine learning and Python programming.

Reviewed on Oct 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.

Reviewed on Feb 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.

View more reviews