By the end of this course, learners will be able to build, train, and evaluate machine learning and deep learning models using Python, Scikit-learn, and TensorFlow. They will confidently preprocess datasets, apply classical algorithms, visualize insights, and design neural networks to solve real-world problems.
Master Machine Learning with TensorFlow: Basics to Advanced
Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Preprocess datasets, apply classical ML algorithms, and visualize insights in Python.
Build, train, and evaluate machine learning models with Scikit-learn.
Design and implement neural networks with TensorFlow for real-world problems.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
September 2025
Assessments
21 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
There are 5 modules in this course
This module introduces learners to the foundations of machine learning, its real-world applications, and the tools needed to begin hands-on practice. Students explore what machine learning is, how machines learn, and where ML is applied across industries, setting the stage for practical TensorFlow projects.
What's included
9 videos4 assignments1 plugin
This module equips learners with essential ML tools such as Anaconda, Jupyter Notebook, and Python libraries. Students learn to manage environments, leverage third-party packages, and perform numerical computations with NumPy for efficient machine learning pipelines.
What's included
14 videos4 assignments
This module focuses on preparing, analyzing, and visualizing data using Pandas, Matplotlib, and Seaborn. Learners handle complex datasets, manage missing values, and create insightful visualizations to uncover patterns, trends, and anomalies essential for ML readiness.
What's included
38 videos5 assignments
This module covers essential preprocessing techniques, data transformation, and classical ML algorithms. Students practice feature engineering, scaling, encoding, and regression modeling while leveraging Scikit-learn to prepare clean and structured datasets.
What's included
22 videos4 assignments
This module introduces deep learning with TensorFlow, covering computational graphs, operations, regression models, and neural networks. Students build and train models using activation functions, optimizers, and the MNIST dataset for hands-on image classification.
What's included
27 videos4 assignments
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."
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
More questions
Financial aid available,