Machine Learning is very powerful and many people are shifting their careers into the Machine learning field. The reason behind machine learning popularity is its power to make useless data into more meaningful data. **Coursera** has a wide range of **Machine Learning courses**. That’s why I have listed the 10 **Best Courses for Machine Learning on Coursera**. So give your few minutes and find out Best Courses for Machine Learning on Coursera for you.

Now without any further ado, let’s get started-

**Best Courses for Machine Learning on Coursera**

- 1. Machine Learning- Stanford University
- 2. Machine Learning with Python- IBM
- 3. Machine Learning Specialization- University of Washington
- 4. IBM Machine Learning Professional Certificate- IBM
- 5. Machine Learning: Algorithms in the Real World Specialization- Alberta Machine Intelligence Institute
- 6. Machine Learning with TensorFlow on Google Cloud Platform Specialization- Google Cloud
- 7. Advanced Machine Learning Specialization- National Research University Higher School of Economics
- 8. Machine Learning for All- University of London
- 9. Mathematics for Machine Learning Specialization- Imperial College London
- 10. Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization- Google Cloud

**1. ****Machine Learning**– Stanford University

**Machine Learning**– Stanford University

**Instructor-** Andrew Ng

**Rating-** 4.9/5

**Time to Complete- **60 hours

This is one of the Best Courses for Machine Learning on Coursera. This course is created by **Andrew Ng** the **Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University**.

This Course provides you a broad introduction to **machine learning, data-mining, and statistical pattern recognition.** All the math required for Machine Learning is well discussed in this course.

This course uses the open-source programming language **Octave**. Octave gives an easy way to understand the fundamentals of Machine Learning.

Now, let’s see what will you learn in this Course-

**Topics Covered-**

- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR

**Extra Benefits-**

- You will get a
**Shareable Certificate**. Along with that, you will learn various case studies and applications. That will teach you how to apply machine learning algorithms to building**smart robots.** - You will also learn
**text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and others.**

**Who Should Enroll?**

- This Course is Most Suitable for
**Complete Beginners**. But people with some basic understanding of ML can also enroll.

**Interested to Enroll?**

If yes, then check out the details here- **Machine Learning**

### 2. **Machine Learning with Python**– IBM

**Machine Learning with Python**– IBM

**Rating-** 4.7/5

**Time to Complete- **22 hours

This is another Machine Learning course for Beginners. This course starts with the basics of Machine Learning. **Python** is used in this course to implement Machine Learning algorithms.

The best part of this course is the **practical advice given after each machine learning algorithm**. Before starting a new algorithm, the trainer gives you the details of how the algorithm works, its pros, cons, and which type of problem can be solved by this algorithm.

Now, let’s see the Topics covered in that course-

**Topics Covered-**

- Introduction to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
- Final Project

Now, let’s see the benefits you will get after completing this course-

**Extra Benefits-**

- You will get a
**Shareable Certificate**. Along with that, you will earn an I**BM digital badge**. - You will get
**FREE**career resources after completing the Professional Certificate. - This course includes
**Resume builder and Mock interviews.**

**Who Should Enroll?**

- This course is good for beginners in Machine Learning, who wanna learn Machine Learning with
**Python**.

**Interested to Enroll?**

If yes, then check out the details here- **Machine Learning with Python**

**3. Machine Learning Specialization**– **University of Washington**

**Rating-** 4.7/5

**Time to Complete- **7 months (If you spend 3 hours/week)

This is the specialization program offered by Coursera. In this specialization program, you will learn major areas of Machine Learning such as **Prediction, Classification, Clustering, and Information Retrieval.** Throughout this program, you will get hands-on experience with machine learning from a series of **practical case-studies.**

In the first case study, you will predict house prices using **regression.** In the second case study, you will **analyze sentiment & loan default prediction** using **classification.** The last and third case study is all about **Finding Similar Documents.**

So in a nutshell, this is the perfect program for you if you are looking for more practical-based learning. There are 4 courses in this specialization program. Now let’s see the details of the courses-

**Courses Details-**

**Machine Learning Foundations: A Case Study Approach****Machine Learning: Regression****Machine Learning: Classification****Machine Learning: Clustering & Retrieval**

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.**

**Who Should Enroll?**

- Those who have some previous knowledge in
**Python and basic math.**

**Interested to Enroll?**

If yes, then check out the details here-**Machine Learning Specialization**

**4. IBM Machine Learning Professional Certificate– IBM**

**Rating- **4.7/5

**Time to Complete- **6 months(If you spend 3 hours/week)

This is the professional certificate offered by **IBM** where you will learn machine learning basics and main types of Machine Learning- **Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning.** You will also learn **Time Series Analysis and Survival Analysis.**

This certificate program will provide **theoretical understanding** as well as **practical understanding** of the main algorithms, uses, and best practices related to Machine Learning.

You will also gain exposure to a series of t**ools, libraries, cloud services, datasets, algorithms, assignments, and projects**. There are 6 courses in this professional certificate. Now, let’s see the details of the courses-

**Courses List-**

**Exploratory Data Analysis for Machine Learning****Supervised Learning: Regression****Supervised Learning: Classification****Unsupervised Learning****Deep Learning and Reinforcement Learning****Specialized Models: Time Series and Survival Analysis**

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - You will also receive a digital Badge from IBM
- Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.**

**Who Should Enroll?**

- Those who have some background in Math, Stats, and Python programming.

**Interested to Enroll?**

If yes, then check out all details here- **IBM Machine Learning Professional Certificate**

**5. Machine Learning: Algorithms in the Real World Specialization– Alberta Machine Intelligence Institute**

**Rating- **4.6/5

**Time to Complete- **4 months( If you spend 3 hours/week)

This is the specialization program where you will learn how to apply** machine learning to data analysis and automation**. This specialization program will teach you how to prepare data for effective machine learning applications.

You will also learn how to implement **supervised learning techniques** on real case studies to analyze business case scenarios where **decision trees, k-nearest neighbors**,** and support vector machines** are optimally used.

There are 4 courses in this specialization program. Now let’s see the details of the courses-

**Courses List-**

**Introduction to Applied Machine Learning****Machine Learning Algorithms: Supervised Learning Tip to Tail****Data for Machine Learning****Optimizing Machine Learning Performance**

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.**

**Who Should Enroll?**

- Those who have previous understanding of
**analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming**

**Interested to Enroll?**

If yes, then check out all details here- **Machine Learning: Algorithms in the Real World Specialization**

**6. ****Machine Learning with TensorFlow on Google Cloud Platform Specialization**– Google Cloud

**Machine Learning with TensorFlow on Google Cloud Platform Specialization**– Google Cloud

**Rating- **4.5/5

**Time to Complete- **3 months (6 hours/week)

This is another great specialization program for learning machine learning with TensorFlow. In this specialization, you will design and build a **TensorFlow 2.x input data pipeline.** You will experiment with **end-to-end ML**, starting from **building an ML-focused strategy** and progressing into **model training, and optimization**, with **hands-on labs using Google Cloud Platform**.

This specialization program is a 5 course series. Now, let’s see the details of the courses-

**Courses List-**

**How Google does Machine Learning****Launching into Machine Learning****Introduction to TensorFlow****Feature Engineering****Art and Science of Machine Learning**

**Skills Gain-**

- Tensorflow
- Machine Learning
- Feature Engineering
- Cloud Computing
- Application Programming Interfaces (API)
- Inclusive ML
- Google Cloud Platform
- Bigquery
- Data Cleansing
- Python Programming
- Build Input Data Pipeline
- keras

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

**Who Should Enroll?**

- Who has
**data engineering or programming experience**and is**interested in learning how to apply machine learning in practice.**

**Interested to Enroll?**

If yes, then check out all details here-**Machine Learning with TensorFlow on Google Cloud Platform Specialization**

**7. ****Advanced Machine Learning Specialization**– National Research University Higher School of Economics

**Advanced Machine Learning Specialization**– National Research University Higher School of Economics

**Rating-** 4.5/5

**Time to Complete- **10 months (If you spend 6 hours per week)

This Specialization series is an advanced series of courses. If you want to learn** more than the basics of Machine Learning**, then this is the best choice for you.

This specialization program fills out all the gaps in your knowledge in Machine Learning. As this is an advanced series of courses, that’s why you need to have **more math knowledge.** In short, this specialization program is for those **who are already in the industry**. This course will sharpen their skills.

Throughout this Specialization program,** you will create several projects, that will help you to build a more powerful portfolio.** This Specialization Program contains 7 Courses. Let’ see all these courses-

**Courses List-**

**Introduction to Deep Learning****How to Win a Data Science Competition: Learn from Top Kagglers****Bayesian Methods for Machine Learning****Practical Reinforcement Learning****Deep Learning in Computer Vision****Natural Language Processing****Addressing Large Hadron Collider Challenges by Machine Learning**

**Extra Benefits-**

- You will get a Shareable Certificate.
- You will get a chance to work on a wide variety of real-world problems like
**image captioning and automatic game playing.** - Along with that, you will get a chance to take advice from
**Top Kaggle machine learning practitioners and CERN scientists.**

**Who Should Enroll?**

- Those who have
**Intermediate level knowledge**in Machine Learning. - Or the one who is already in the industry and wants to sharpen Machine Learning skills.

**Interested to Enroll?**

If yes, then check out the details here- **Advanced Machine Learning Specialization**

**8. Machine Learning for All– University of London**

**Rating- **4.7/5

**Time to Complete-** 22 hours

This is a **beginner-level course** where you will get a basic idea of machine learning, even if you don’t have any background in math or programming. You will also get hands-on and use user-friendly tools developed at Goldsmiths, the University of London to actually train a machine learning model to recognize images.

But this course doesn’t cover programming-based machine learning tools like python and TensorFlow. That’s why anyone can do this course just to understand the fundamentals of machine learning.

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

**Who Should Enroll?**

- Those who just want to understand the basics of machine learning without any programming and mathematical understanding.

**Interested to Enroll?**

If yes, then check out all details here- **Machine Learning for All**

**9. ****Mathematics for Machine Learning Specialization**– Imperial College London

**– Imperial College London**

**Mathematics for Machine Learning Specialization****Rating-** 4.6/5

**Time to Complete- **4 Months (4 hours/week)

This is one of the best specialization programs that covers **all mathematical topics** required for Machine Learning. The aim of this specialization program is to **fill the gap** and build an intuitive understanding of mathematics.

This specialization program is a** 3-course** series. In the first course, you will learn **Linear Algebra, vectors, matrices, and how it relates to data.**

The Second Course of this specialization is **Multivariate Calculus**. In this course, you will get a deeper understanding of how to optimize fitting functions to get good fits to data.

The last course of this specialization program is **Dimensionality Reduction with Principal Component Analysis.** This course uses the mathematics from the first two courses to **compress high-dimensional data.** Along with that, you will work on different real-world projects.

After completing this specialization program, you will have gained the **prerequisite mathematical knowledge** to continue your Machine Learning journey. Let’s see the details of the courses-

**Courses List-**

**Mathematics for Machine Learning: Linear Algebra****Mathematics for Machine Learning: Multivariate Calculus****Mathematics for Machine Learning: PCA**

Now, let’s see what skills you will gain after completing this program-

**Skills Gain-**

- Eigenvalues And Eigenvectors
- Principal Component Analysis (PCA)
- Multivariate Calculus
- Linear Algebra
- Basis (Linear Algebra)
- Transformation Matrix
- Linear Regression
- Vector Calculus
- Gradient Descent
- Dimensionality Reduction
- Python Programming

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You have
**High school level maths knowledge**. And basic knowledge of Python and NumPy is required for**Course 3**.

**Interested to Enroll?**

If yes, then check out all details here- **Mathematics for Machine Learning Specialization**

**10. ****Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization**– Google Cloud

**– Google Cloud**

**Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization****Rating- **4.5/5

**Time to Complete- **3 months (5 hours/week)

This specialization program is **an advanced level program** focused on **advanced machine learning topics using Google Cloud Platform**. This specialization program teaches you how to build **scalable, accurate, and production-ready models** for **structured data, image data, time-series, and natural language text.**

This specialization is a 5- Course series. Let’s see the details of the courses-

**Courses List-**

**End-to-End Machine Learning with TensorFlow on GCP****Production Machine Learning Systems****Image Understanding with TensorFlow on GCP****Sequence Models for Time Series and Natural Language Processing****Recommendation Systems with TensorFlow on GCP**

Now, let’s see what skills you will gain after completing this program-

**Skills Gain-**

- Tensorflow
- Convolutional Neural Network
- Estimator
- Advanced Machine Learning

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

**Who Should Enroll?**

- Those who have
**basic Knowledge**of**machine learning and TensorFlow**, experience**in Python**, and knowledge of**basic statistics**.

**Interested to Enroll?**

If yes, then check out all details here-** Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization**

And here the list end. I hope these** Best Courses for Machine Learning on Coursera** will definitely help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

**Conclusion**

In this article, I tried to cover the 10 Best Courses for Machine Learning on Coursera. If you have any doubt or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.