OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

Introduction to Machine Learning with Python

Adwith Malpe

This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.

Read more

This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.

You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

What's inside

Syllabus

Course Introduction
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models. You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores supervised learning, unsupervised learning, deep learning, image processing, and GANs, which are foundational in machine learning today
Taught by Adwith Malpe, a seasoned instructor in machine learning
Develops machine learning models using Python, a highly relevant language in industry
Examines a range of machine learning algorithms, which helps learners tackle a variety of real-world problems
Requires no prior programming or computer science background, making it accessible to beginners
May require additional software or tools that learners may need to purchase, which could pose a financial barrier for some

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Beginner-friendly python machine learning intro

According to students, this course is a largely positive and excellent introduction to machine learning with Python, especially for those with no prior programming or computer science background. Learners consistently praise the instructor's ability to explain complex topics clearly and make them accessible and easy to follow. The hands-on labs and practical exercises are frequently highlighted as a significant strength, helping to solidify understanding. However, some students note that the advanced machine learning topics like deep learning and GANs are covered somewhat superficially and may require additional resources for a deeper dive. Overall, it serves as a solid foundation for anyone starting their machine learning journey.
Perfect starting point for those new to Python and ML.
"Highly recommended for anyone starting their ML journey!"
"Absolutely loved this course! A true gem for beginners."
"Fantastic course for anyone with zero ML or Python experience."
Practical exercises and labs reinforce learning effectively.
"I particularly enjoyed the hands-on labs; they really helped solidify my understanding."
"The practical exercises were well-designed and reinforced the concepts perfectly."
"The labs are fantastic for hands-on practice."
Complex ML concepts are made easy to grasp for beginners.
"The instructor explains complex topics clearly, making them accessible even without prior programming knowledge."
"As someone completely new to Python and ML, I found the explanations incredibly easy to follow."
"The instructor breaks down complex ideas into digestible chunks. The support materials are also very helpful."
May be too basic for those with some prior ML knowledge.
"If you have some background, you might find it too basic, but then the advanced parts are too shallow."
"I wish there were more challenging assignments for intermediate learners."
Later modules on advanced ML lack sufficient depth.
"The 'advanced topics' module felt superficial. Deep learning, image processing, and GANs are massive topics..."
"Some parts, especially in the later modules on GANs, felt a bit rushed and could benefit from more detailed examples."
"If you're hoping to really understand or build these, you'll need other resources. For general ML intro, it's fine."
"The course content on advanced topics (GANs specifically) was very high-level and didn't provide enough depth for practical implementation."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Machine Learning with Python with these activities:
Compile a collection of machine learning resources
Organizing and maintaining a repository of valuable resources will provide ongoing support and facilitate continued learning.
Browse courses on Online Courses
Show steps
  • Identify and gather relevant articles, tutorials, and documentation.
  • Organize the resources into a structured collection.
  • Share the collection with other learners or store it for personal use.
Review statistics basics
Recalling the fundamentals of statistics will strengthen your foundational understanding for this course.
Show steps
  • Review concepts of probability and distributions.
  • Practice solving probability problems.
  • Review hypothesis testing methods.
Join a machine learning study group
Engaging with peers in a study group will provide opportunities for discussion, knowledge exchange, and support.
Browse courses on Collaboration
Show steps
  • Find or create a study group focused on machine learning.
  • Set regular meeting times and establish a study plan.
  • Collaborate on projects, discuss concepts, and share resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete Python coding exercises
Reinforcing your Python skills through practice will enhance your ability to implement machine learning algorithms effectively.
Browse courses on Python Programming
Show steps
  • Find online coding challenges or platforms.
  • Select exercises that cover Python concepts relevant to machine learning.
  • Solve the exercises and debug your code.
Build a simple machine learning model
Working through the process of creating a machine learning model will build your confidence in applying theories to real datasets.
Show steps
  • Choose a simple dataset.
  • Select a suitable learning algorithm.
  • Train and evaluate the model using Python.
Create a visual representation of machine learning concepts
Creating visual representations of machine learning concepts will enhance your understanding and ability to communicate them to others.
Browse courses on Data Visualization
Show steps
  • Choose a specific machine learning concept.
  • Design a visual representation (e.g., diagram, flowchart, infographic).
  • Create the visual representation using suitable tools.
Explore advanced machine learning topics
Extending your understanding of machine learning through tutorials will expand your knowledge and prepare you for more advanced concepts.
Show steps
  • Identify a specific advanced topic of interest.
  • Find reputable tutorials or online courses on the topic.
  • Follow the tutorials and complete the exercises.
Mentor junior learners in machine learning
Mentoring others will reinforce your knowledge, develop your communication skills, and contribute to the community.
Browse courses on Mentoring
Show steps
  • Identify opportunities to mentor junior learners.
  • Provide guidance, support, and feedback on their learning journey.
  • Create or participate in mentoring programs.

Career center

Learners who complete Introduction to Machine Learning with Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use machine learning and statistical techniques to extract insights from data. They work with businesses to help them make better decisions by identifying patterns and trends in data. Machine learning is a large part of data science, and Python is one of the most common programming languages used in the field. This course provides a solid foundation in machine learning with Python, which can help you on your journey to becoming a successful Data Scientist.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They work with data scientists to identify the right machine learning algorithms for a given problem and then implement and deploy those models. A background in programming languages like Python is essential for these roles. This course provides a comprehensive introduction to machine learning in Python, which includes supervised learning, unsupervised learning, and deep learning. Additionally, the course covers advanced topics like image processing and generative adversarial networks (GANs), which are important areas in machine learning today.
See salaries and explore the career path for Machine Learning Engineer
Statistician
Statisticians use data to analyze and interpret data. Machine learning is increasingly being used in statistics to automate tasks and improve the accuracy of analysis. This course provides a good foundation in machine learning with Python, which is a valuable skill for Statisticians.
See salaries and explore the career path for Statistician
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. Machine learning is increasingly being used in data analysis to automate tasks and improve the accuracy of analysis. This course provides a good introduction to machine learning with Python, which is a popular programming language in data analysis. By learning the concepts of machine learning, you can make yourself a more effective and efficient Data Analyst.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts use machine learning and statistical techniques to analyze financial data and make investment decisions. Python is a popular programming language in quantitative finance, and machine learning is increasingly being used to develop trading strategies and risk management models. This course can help you build a solid foundation in machine learning with Python, which is a valuable skill for Quantitative Analysts.
See salaries and explore the career path for Quantitative Analyst
Data Engineer
Data Engineers build and maintain data pipelines. Machine learning is increasingly being used in data engineering to automate tasks and improve the efficiency of data pipelines. While this course does not directly focus on data engineering, it provides a good foundation in machine learning with Python, which is a valuable skill for Data Engineers.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts use data to help businesses make better decisions. Machine learning is increasingly being used in business analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on business analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Business Analysts.
See salaries and explore the career path for Business Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment decisions. Machine learning is increasingly being used in financial analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on financial analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Financial Analysts.
See salaries and explore the career path for Financial Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. Machine learning is increasingly being used in risk analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on risk analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Risk Analysts.
See salaries and explore the career path for Risk Analyst
Actuary
Actuaries use data to assess and manage risks. Machine learning is increasingly being used in actuarial science to automate tasks and improve the accuracy of analysis. While this course does not directly focus on actuarial science, it provides a good foundation in machine learning with Python, which is a valuable skill for Actuaries.
See salaries and explore the career path for Actuary
Data Architect
Data Architects design and build data systems. Machine learning is increasingly being used in data architecture to automate tasks and improve the performance of data systems. While this course does not directly focus on data architecture, it provides a good foundation in machine learning with Python, which is a valuable skill for Data Architects.
See salaries and explore the career path for Data Architect
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. Machine learning is increasingly being used to automate marketing tasks and improve the effectiveness of marketing campaigns. While this course does not focus on marketing, it provides a good foundation in machine learning with Python, which can be a valuable skill for Marketing Managers.
See salaries and explore the career path for Marketing Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. Machine learning is increasingly being used to automate tasks and improve the efficiency of operations. While this course does not focus on operations management, it provides a good foundation in machine learning with Python, which is a valuable skill for Operations Managers.
See salaries and explore the career path for Operations Manager
Product Manager
Product Managers are responsible for the development and launch of new products. Machine learning is increasingly being used to develop new products and improve existing products. While this course does not focus on product management, it provides a good foundation in machine learning with Python, which is a valuable skill for Product Managers.
See salaries and explore the career path for Product Manager
Software Engineer
Software Engineers design, develop, and maintain software applications. Machine learning is increasingly being used in software development to improve the performance and efficiency of software applications. This course provides a good foundation in machine learning with Python, which is a widely-used programming language in software development. By learning the concepts of machine learning, you can make yourself a more valuable and in-demand Software Engineer.
See salaries and explore the career path for Software Engineer

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Machine Learning with Python.
Cover image
Build a Large Language Model (From Scratch)
Save
Provides a comprehensive introduction to machine learning with Python, covering various supervised and unsupervised learning algorithms. It also covers essential topics such as data preprocessing, model evaluation, and hyperparameter tuning, making it a great companion to this course.
Build a Large Language Model (From Scratch)
Paperback
Check
Build a Large Language Model (From Scratch)
Kindle Edition
Check
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Offers a practical approach to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides hands-on examples and covers a wide range of machine learning concepts, making it a valuable resource for practicing and solidifying the techniques taught in this course.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Machine Learning Q and AI
Save
As a textbook commonly used in academic institutions, this book provides a thorough introduction to machine learning concepts with a focus on Python implementation. It covers supervised and unsupervised learning, model selection, and evaluation, complementing the content of this course effectively.
Machine Learning Q and AI: 30 Essential Questions...
Paperback
$$
Machine Learning Q and AI: 30 Essential Questions...
Kindle Edition
$$
Cover image
Deep Learning with Python, Second Edition
Save
Comprehensive guide to deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. While it may go beyond the scope of this course, it provides a solid foundation for those interested in exploring deep learning further.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
An Introduction to Statistical Learning
Save
While this book has a broader scope than this course, it provides a comprehensive introduction to statistical learning methods, including regression, classification, and clustering. Its focus on mathematical foundations and statistical concepts is particularly valuable for those seeking a deeper understanding of the underlying principles.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Pattern Recognition and Machine Learning
Save
Offers a rigorous and comprehensive treatment of pattern recognition and machine learning. Its focus on theoretical foundations and mathematical derivations provides a solid grounding for those interested in a deeper understanding of the subject matter.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Probabilistic Machine Learning
Save
While it goes beyond the scope of this course, this book provides a unique perspective on machine learning from a Bayesian and optimization standpoint. Its focus on probabilistic models and optimization techniques is valuable for those interested in exploring alternative approaches to machine learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
The Elements of Statistical Learning
Save
Serves as a comprehensive reference for statistical learning methods and algorithms. While it may not be as beginner-friendly as other resources, it provides a thorough treatment of advanced topics and is commonly used as a textbook in graduate-level courses.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Machine Learning in Action
Save
Offers a practical and hands-on approach to machine learning, focusing on real-world applications. Its emphasis on code examples and case studies makes it a valuable resource for those interested in implementing and deploying machine learning models.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Machine Learning with Python for Everyone
Save
Offers a beginner-friendly introduction to machine learning concepts, focusing on making the subject accessible to those with little to no prior knowledge.
Machine Learning with Python for Everyone
Paperback
Check
Machine Learning with Python for Everyone
Kindle Edition
Check
Cover image
MACHINE LEARNING
Save
Takes an algorithmic approach to machine learning, providing detailed explanations of various algorithms and their applications. Its focus on mathematical foundations is particularly valuable for those interested in understanding the theoretical underpinnings of machine learning.
Machine Learning: An Algorithmic Perspective,...
Hardcover
$$$
Machine Learning: An Algorithmic Perspective...
Paperback
$$
An Algorithmic Perspective on Machine Learning (2nd...
Paperback
$$$
Machine Learning: An Algorithmic Perspective,...
Kindle Edition
$$$
Understanding Machine Learning
Save
Offers a comprehensive and rigorous treatment of machine learning theory and algorithms. Its focus on mathematical proofs and derivations provides a solid foundation for those interested in the theoretical aspects of machine learning.
Understanding Machine Learning: From Theory to...
Hardcover
$$$
Understanding Machine Learning: From Theory to...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Introduction to Machine Learning with Python.
Machine Learning
Most relevant
Machine Learning for Engineers: Algorithms and...
Most relevant
Fundamentals of Machine Learning in Finance
Most relevant
Big Data Science with the BD2K-LINCS Data Coordination...
Most relevant
Data Mining for Smart Cities
Most relevant
Machine Learning with Python
Most relevant
Data Science Methods and Algorithms [2025]
Most relevant
The Nuts and Bolts of Machine Learning
Most relevant
Deep Learning with Python and Keras
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Level
Introductory
Via
Coursera
Institution
Arizona State University
Instructor
Adwith Malpe
Language
English
This course belongs to a collection called Python: A Guided Journey from Introduction to Application. It's comprised of three courses:
  1. Data Analysis and Visualization with Python
  2. Introduction to Machine Learning with Python (viewing)
  3. Object-Oriented Programming and GUI with Python
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores supervised learning, unsupervised learning, deep learning, image processing, and GANs, which are foundational in machine learning today
Taught by Adwith Malpe, a seasoned instructor in machine learning
Develops machine learning models using Python, a highly relevant language in industry
Examines a range of machine learning algorithms, which helps learners tackle a variety of real-world problems
Requires no prior programming or computer science background, making it accessible to beginners
May require additional software or tools that learners may need to purchase, which could pose a financial barrier for some
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Introduction to Machine Learning with Python.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser