11 Best Coursera courses for Data Science and Machine Learning You Should Not Miss to Check

Introduction

When you are trying to build a foundation in the field of Machine Learning and Data Science, one of the best approaches is to pursue some reputed courses for more robust and systematic learning. There are many online platforms that offer online courses in data science and machine learning and Coursera is one of the leaders in this online learning space. In this article, we’ll be looking at some of the best Coursera courses for Data Science and Machine Learning you should really check. 

We will do a detailed round-up of these courses so that you can compare them and make an informed decision on which course is best suited for you. We will cover the course content, learn about the instructors, and find out student reviews. Even if you are not a beginner some of these courses should benefit you to understand the low-level nuances of ML and data science.

Best Coursera courses for Data Science and Machine Learning –

1. AI for Everyone By deeplearning.ai | Level Beginner | Rating 4.8 | Students 570K+ | Duration 4 Weeks

AI for Everyone is a Coursera by deeplearning.ai where students are introduced to concepts of AI, Machine Learning, Deep Learning, and Data Science. This course is majorly targeted to non-technical people from various domains who just want to get a high-level understanding of these topics. Hence all these technical concepts are explained in simple terms so that even laymen from business backgrounds can understand them and leverage AI for their projects.

The course takes you through how AI projects are implemented in organizations and how AI can be used in different types of business verticals. Along with this, the course talks about the social and economic impact of AI on society and threats like discrimination and adversarial attacks.

Instructor for AI for Everyone Course 

1. Andrew NG

Andrew Ng is a world-renowned professor in the field of Computer Science, especially because of his exemplary work in the field of Artificial Intelligence. Apart from being a professor at Stanford University, Andrew has founded companies like Landing AI, his own company deeplearning.ai provides this course. In the past, he has been the Chief Scientist at Baidu and has to lead the Google Brain Project.

Students Review for AI for Everyone Course

As per the popular reviews by Coursera students, AI for Everyone is a good introductory course for people who are from a non-technical background and looking to learn more about Artificial Intelligence and its implementation in other fields. People who are searching for in-depth technical knowledge should remember that this course covers a basic introduction with less theoretical/practical content and focus more on AI that can help people from a business background.

2. IBM Applied AI Professional Certificate (6 Courses) | Level Beginner | Rating 4.6 | Students 25K+ | Duration 7 Months Approx

IBM Applied AI Professional Certificate is a collection of 6 Coursera courses by IBM where students get to learn about different technologies of AI like machine learning, data science, chatbots, computer vision along with real-world use cases, and their implementation. Being an IBM program, 3 courses are dedicated to their flagship IBM Watson platform, but you will still find some Python exposure as well. This is ideal for someone with less coding experience, as you can still complete this certificate with ease and will also learn a lot. Here is the course list of this certificate program –

1. Introduction to Artificial Intelligence (AI)
2. Getting Started with AI using IBM Watson
3. Building AI Powered Chatbots without Programming
4. Python for Data Science and AI
5. Building AI Application with Watson APIs
6. Introduction to Computer Vision with Watson and OpenCV

Instructors for IBM Applied AI Professional Certificate

1. Instructor – Rav Ahuja

Rav Ahuja is working as IBM’s Global Program Director where he is leading growth strategy, curriculum creation, and other partner-based programs at IBM Skills Network. Rav has contributed to co-founding Cognitive Class (IBM’s Initiative to teach modern-day technologies). He works at IBM Canada Lab in Toronto and has shared his valuable experience in the making of IBM Data Science Professional Certificate.

2. Instructor – Antonio Cangiano

Antonio Cangiano is a software developer combined with his passion for the technical expertise of 12 years, he has contributed in numerous ways to IBM during these years. Besides he is also a blogger and author of two books.

3. Instructor – Joseph Santarcangelo

With a Ph.D. in Electrical Engineering, Joseph is working on the latest machine learning research studies along with signal processing, and computer vision. Joseph has been associated with IBM since he finished off his Ph.D.

4. Instructor – Tanmoy Bakshi

A child prodigy, Tanmay Bakshi has been actively working as a 15-year old in the field of AI and Machine learning. Apart from being a Youtuber, an author, TED Speaker, Google Developer Expert for Machine Learning, he also works with IBM Cloud and IBM Watson teams.

5. Instructor – Yi Leng Yao

Yi Leng Yao is working as a Machine Learning Engineer for IBM where he has helped in incorporating robust ML Models into enterprise-level applications. With customers as focus, Yi looks to provide such useful applications that are easy-to-use for the customers and at the same time contain the latest features of Machine Learning.

6. Instructor – Sacchit Chadha

Sachhit had interned at IBM while he was studying at the University of Waterloo. During his undergraduate studies, he was able to garner a good amount of work experience in the tech industry. His work interests include blockchain development, computer vision, iOS development.

Students Review for IBM Applied AI Professional Certificate

The popular reviews for IBM Applied Professional Certificate program by Coursera students mention that although some of its courses are focused around IBM Watson, you can still learn general concepts on chatbot development, computer vision fundamentals. It is advised to have a bit of prior python coding experience for a better understanding of this course. You may find that the course lacks a structure to some extent, but the concepts are explained well to cover up this.

3. Machine Learning Course by Stanford University | Level Intermediate | Rating 4.9 | Students 3.5M+ | Duration 11 Weeks Approx

Machine Learning Course by Stanford University is one of the earlier and best Coursera of Machine Learning on Coursera. Having served millions of students worldwide, this course has now gained a cult status among machine learning aspirants. The course covers all the basic machine learning methodologies of supervised learning, unsupervised learning. The concepts are explained with the mathematical background in easy to understand manner and the practical hands-on assignments are done in Matlab.

Instructors for Stanford Machine Learning Course

1. Andrew NG

Andrew Ng is a world-renowned professor in the field of Computer Science, especially because of his exemplary work in the field of Artificial Intelligence. Apart from being a professor at Stanford University, Andrew has founded companies like Landing AI, his own company deeplearning.ai provides this course. In the past, he has been the Chief Scientist at Baidu and has to lead the Google Brain Project.

Students Review for Stanford Machine Learning Course

Coursera students agree in their review that this Stanford Machine Learning course teaches you all the fundamentals required to start up your career in Machine Learning. The projects, assignments, and quizzes will assist in learning the concepts with in-depth details. The main disadvantage is the usage of Octave Matlab for assignments, this makes this course outdated in terms of practical application as now Python has emerged as the popular choice of language for ML.

4. Deep Learning Specialization (5 courses) by deeplearning.ai | Level Intermediate | Rating 4.8 | Students 820K+ | Duration 4 Months Approx

Deep Learning Specialization by deeplearning.ai is a program of 5 courses on deep learning. The instructor of this course, Andrew NG is well known for his ability to teach complex topics in an easy manner. Hence all 5 courses in this specialization will ensure that you understand the intricate concepts of neural network and deep learning along with the maths behind it in a simple manner. The practical implementation is done with Python and Tensorflow. The 5 courses of Deep Learning Specialization are listed below and you can do these courses individually as well.

1. Neural Networks and Deep Learning
2. Improving Deep Neural Network: Hyperparameter Tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

Instructors for Deep Learning Specialization by deeplearning.ai

1. Instructor – Andrew NG

Andrew Ng is a world-renowned professor in the field of Computer Science, especially because of his exemplary work in the field of Artificial Intelligence. Apart from being a professor at Stanford University, Andrew has founded companies like Landing AI, his own company deeplearning.ai provides this course. In the past, he has been the Chief Scientist at Baidu and has to lead the Google Brain Project.

2. Instructor – Younes Bensouda Mourri

Younes has been the co-creator of many of the artificial intelligence graduate courses at Stanford. And he has been involved in two of the highest-rated and best Coursera courses for Machine Learning and Deep Learning.

3. Instructor – Kian Katanforoosh

Kian Katanforoosh is the founding member of deeplearning.ai and CS lecturer at Stanford. He is also the founder of workera.ai which is the assessment platform skills for data science, machine learning, and data engineer.

Students Review for Deep Learning Specialization by deeplearning.ai

Coursera students say in their review that Deep Learning Specialization by deeplearning.ai gives them the balanced mathematical concepts required for machine learning and deep learning without going into too much details. There are several interactions with the industry experts that will help you in knowing about the latest technologies used in the industry along with hands-on experience. The assignments are at a basic level that will help you in getting acquainted with deep learning concepts.

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5. Mathematics for Machine Learning Specialization (3 Courses) by Imperial College London | Level Intermediate | Rating 4.6 | Students 190K+ | Duration 4 Months Approx

Artificial Intelligence, Machine Learning, and Data Science use a lot of mathematical concepts which are often ignored by beginners. This is because many people who aspire for machine learning are working professionals who have forgotten mathematical concepts. This specialization is very ideal for these peoples for creating a base of maths for machine learning. The course will require students to have some experience with Python and Numpy. Below are the individual courses in this specialization –

1. Mathematics for Machine Learning: Linear Algebra
2. Mathematics for Machine Learning: Multivariate Calculus
3. Mathematics for Machine Learning: PCA

Instructors for Mathematics for Machine Learning Specialization

1. David Dye

David works as a Professor of Metallurgy at Imperial College London’s Department of Materials. David has been working extensively in developing alloys for jet engines, nuclear and caloric materials for reducing fuel burn. His work has given him exposure to ‘big data analysis’ problems.

2. Samuel J. Cooper

Dr. Samuel J Cooper is a Lecturer in the energy science and materials design department of the Dyson School of Design at Imperial College London. After having completed his Ph.D. on characterization and optimization of battery. Samuel’s research group is looking to develop next-generation energy storage methods.

3. Marc Peter Deisenroth

Marc Deisenroth is working as a Lecturer in the Statistical Machine Learning Department of Computing at Imperial College London. Marc has received a couple of Ph.D. scholarship awards from Google and Microsoft.

4. A. Freddie Page

Dr. Freddie Page has been performing research experiments to learn more about the materials that can slow light down in its movement to a stop. He is also in the search of the ways light interacts with graphene.

Students Review of for Mathematics for Machine Learning Specialization

According to students’ reviews in Coursera, Mathematics for Machine Learning Specialization is highly beneficial for those who want to learn in-depth mathematical concepts for machine learning. Mathematical concepts can be quite tricky at times, if you will take this course, be prepared to put in extra effort, and patience to grasp the topics.

6. Machine Learning Specialization (4 Courses) by University of Washington | Level Beginner | Rating 4.6 | Students 340K+ | Duration 7 Months Approx

This machine learning specialization by the University of Washington comprises 4 courses that introduce the basics of Machine Learning to beginners along. The 3 courses cover the basics of Prediction, Classification, Clustering, and Information Retrieval. The good thing about these courses is that they teach these ML concepts through case studies which makes it easy for students to understand and create large scaled ML models. Below is the list of courses covered in this specialization –

1. Machine Learning Foundations: A Case Study Approach
2. Machine Learning: Regression
3. Machine Learning: Classification
4. Machine Learning: Clustering & Retrieval

Instructors for Machine Learning Specialization by Univ. of Washington

1. Instructor – Emily Fox

Emily Fox works as an assistant professor of Machine Learning at the University of Washington’s Statistics Department. She has been awarded several research scholarship awards. Her main research interests include large-scale Bayesian dynamic modeling and computations.

2. Instructor – Carlos Guestrin

Carlos Guestrin is working as the Amazon Professor of Machine Learning in the Computer Science and Engineering Department of the University of Washington. He has co-founded a company called Dato Inc. for building intelligent applications that use Machine Learning at large-scale. Carlos has previously worked for Carnegie Mellon University, Intel Research Lab. His genius mind has been acknowledged with multiple awards.

Student’s Review for Machine Learning Specialization by Univ. of Washington

Students review says that this course specialization takes you through real-world implementations of ML and is one of the best Coursera course on machine learning. People who don’t have a lot of programming experience will not face any problem in completing the course. There are assignments where the course uses outdated libraries but you can try to implement them using the latest ML libraries. This course uses proprietary software for executing assignments which may cause some trouble in the installation of software and completion of assignments.

7. IBM Data Science Certificate (9 Courses) | Level Beginner | Rating 4.6 | Students 590K+ | Duration 10 Months Approx

This specialization by IBM Data Science Certificate program contains 9 courses that will teach you about different tools and technologies used in the Data Science Industry. The specialization is quite comprehensive as it covers a wide range of topics like data science, Python, Managing data using Databases, SQL, Data Visualization, Data Analysis, Statistical Analysis, Predictive Modelling, and Machine Learning Algorithms. You will also get exposure to IBM Cloud and IBM AI platform Watson in this course. Below is the list of 9 courses included in the specialization –

1. What is Data Science?
2. Tools for Data Science
3. Data Science Methodology
4. Python for Data Science and AI
5. Databases and SQL for Data Science
6. Data Analysis with Python
7. Data Visualization with Python
8. Machine Learning with Python
9. Applied Data Science Capstone

Instructors for IBM Data Science Certificate Program

1. Instructor – Alex Aklson

Mr. Alex Aklson, Ph.D. is providing his services as a Data Scientist at IBM Canada. Alex has been actively involved in data science projects for building smart systems and other useful application-based projects.

2. Instructor – Romeo Kienzler

Romeo Kienzler has collected almost 20 years of experience in Software Engineering, Database Administration, and Information Integration. Romeo has been a Data Scientist for IBM for almost a decade now.

3. Instructor – Saeed Aghabozorgi

A Senior Data Scientist in IBM, Saeed Aghabozorgi has been associated with the teams that develop enterprise-level applications for extracting useful insights from data. He is persistent in his research in the field of Data Mining and looks to get involved in deep learning, machine learning related studies.

4. Instructor – Svetlana Levitan

Svetlana Levitan is a Senior Developer Advocate with IBM Center for Data and AI Technologies. She looks to contribute to open standards for Machine Learning Model Deployment, PMML, and ONNX. Svetlana looks to explore new technologies and promote women in STEM.

5. Instructor – Polong Lin

Polong is a data scientist at IBM where his work revolves around data science advocacy and partnerships. Polong also leads the largest meetup group for data science in Toronto.

Student’s Review for IBM Data Science Certificate Program

Reviews by Coursera students say that IBM Data Science Certificate is a comprehensive specialization for beginners who are looking to get in-depth knowledge of data science techniques, tools, and use-cases. Usage of IBM Watson for assignments gives an additional benefit to the course, enhances the learning experience. As the course covers basics, people who have some knowledge of Data Science may find the initial part of the course boring.

8. Advanced ML with TF on GCP Specialization (9 Courses) by Google | Level Advanced | Rating 4.5 | Students 44K+ | Duration 3 Months Approx

This 5-course specialization by Google Cloud Platform is for all those people who want to hone their advanced machine learning skills. This specialization will train the attendees with hands-on experience of optimizing the models, deployment of models, and scaling the models for production environments using Tensorflow and GCP. It takes you advanced topics like production deployment, sequence models, recommendations systems.  Below is the list of courses available with this specialization –

1. End-to-End Machine Learning with TensorFlow on GCP
2. Production Machine Learning Systems
3. Image Understanding with TensorFlow on GCP
4. Sequence Models for Time Series and Natural Language Processing
5. Recommendation Systems with TensorFlow on GCP

Instructors for this Specialization

For this specialization, the Google Cloud Training team has taken the responsibility of teaching all courses. This team has built projects that are secure, reliable, scalable, and are capable of including all the components necessary for running an application over the Cloud.

Student Reviews for this Specialization

This course provides a good overview of the end-to-end machine learning process. Attendees also learn about real-world applications of Google Cloud Platform in the machine learning domain. With the help of TensorFlow students will be taught about the machine learning model deployment. But some reviews point out that certain sections of the course have become outdated, so you’ll have to be attentive to implement apt methods.

9. Applied Data Science with Python Specialization (5 Courses) by Univ. of Michigan | Level Intermediate | Rating 4.5 | Students 580K+ | Duration 5 Months Approx

Applied Data Science with Python Specialization consists of 5 courses that teach learners Data Science using Python. This course will require you to have some prerequisite knowledge of Python as assignments are done with Python libraries like pandas, matplotlib, scikit-learn, nltk, and networkx. Topics like machine learning, data visualization, textual analysis are discussed in-depth in the courses. It is one of the best Coursera courses for data science. Below is the list of courses under this specialization –

1. Introduction to Data Science in Python
2. Applied Plotting, Charting & Data Representation in Python
3. Applied Machine Learning in Python
4. Applied Text Mining in Python
5. Applied Social Network Analysis in Python

Instructors for Applied Data Science with Python

1. Instructor – Christopher Brooks

Christopher Brooks is currently serving as Research Assistant Professor in the School of Information and Director of Learning Analytics at the University of Michigan. His main research focus is to build tools that can improve students’ teaching and learning experience. Christopher is working arduously to apply machine learning, information visualization, and data mining techniques to his research goals.

2. Instructor – Kevyn Collins Thompson

Kevyn Collins Thompson works as an Associate Professor in the Information and Computer Science in the School of Information at the University of Michigan. He also uses his experience for building algorithms that can help in connecting people through information for education.

3. Instructor – V.G. Vinod Vydiswaran

V.G. Vinod Vydiswaran is an Assistant Professor of Learning Health Sciences, Medical School, and Assistant Professor of Information in School of Information at the University of Michigan. He looks to use information trustworthiness, large-scale text mining, and analysis, NLP for building models that can provide solutions to real-world problems.

4. Instructor -Daniel Romero

Daniel Romero is another Assistant Professor working at the School of Information at the University of Michigan. Daniel is inclined towards research fields like empirical and theoretical analysis of Social and Information Networks.

Reviews for Applied Data Science with Python

The student’s reviews on Coursera will tell you that Applied Data Science with Python is one of the best Coursera courses for data science. You will also learn how to handle dirty data and learn preprocessing techniques. However, the course does not talk a lot about the basics of Python, so one should have a little programming experience and basic knowledge about data science.

10. Reinforcement Learning Specialization (4 Courses) by Univ. of Alberta | Level Intermediate | Rating 4.6 | Students 590K+ | Duration 10 Months Approx

Reinforcement Learning Specialization consists of 4 Courses and will teach you the basics of RL and adaptive learning. The fourth project deals with designing a complete reinforcement learning system as a capstone project. Below is the list of courses available in this specialization –

1. Fundamentals of Reinforcement Learning
2. Sample-based Learning Methods
3. Prediction and Control with Function Approximation
4. A Complete Reinforcement Learning System (Capstone)

Instructors for Reinforcement Learning Specialization

1. Instructors – Martha White

Martha White works as an Assistant Professor in the Computing Sciences Department at the University of Alberta. She is working to build algorithms that will help agents in learning continuously on data streams, with representation learning and reinforcement learning.

2. Instructors -Adam White

Adam White is holding the same position as Martha at the University of Alberta, he also provides his services as Senior Research Scientist at DeepMind. Adam is working to emulate human-like intelligence in different types of agents.

Students Review for Reinforcement Learning Specialization

The students of Coursera review that Reinforcement Learning Specialization covers all the mathematical foundations of reinforcement learning. The coding assignments are helpful in increasing the understanding of this subject. However, this course is not comprehensive for RL and you may need external resources for more learning.

11. Data Science Specialization (10 Courses) by Johns Hopkins Univ. | Level Beginner | Rating 4.5 | Students 930K+ | Duration 11 Months Approx

Data Science Specialization by Johns Hopkins University is one of the best Coursera courses for data science. It consists of 10 courses and the assignments, projects are done in R language which makes it stand out from other Python-based data science specializations on the platform. For beginners, there is a dedicated course for the R programming language. Below is the complete list of courses available in this specialization –

1. The Data Scientist’s Toolbox
2. R Programming
3. Getting and Cleaning Data
4. Exploratory Data Analysis
5. Reproducible Research
6. Statistical Inference
7. Regression Models
8. Practical Machine Learning
9. Developing Data Products
10. Data Science Capstone

Instructors for Data Science Specialization by Johns Hopkins Univ.

1. Instructor – Jeff Leek

Jeff Leek is working as an Assistant Professor in the field of Biostatistics at John Hopkins University. After completing his Ph.D. in Biostatistics, he has been awarded for his genomic data research studies that involve data analysis and statistical methods.

2. Instructor -Brian Caffo

Brian Caffo holds a Ph.D. degree, currently working as a professor in the Biostatistics Department at John Hopkins University. He works in Computational Statistics and Neuroinformatics domains. He has been awarded several times for his contributions.

3. Instructor -Roger D. Peng

Roger D. Peng is one of the leading researchers in the field of air pollution and health risk assessment. He also works with statistical methods for environmental data and his work has received a lot of attention in the form of accolades.

Students Review for Data Science Specialization by Johns Hopkins Univ.

As per Coursera student’s review for this Data Science Specialization, the course includes assignments and projects based on the R language very well. This course will help you in building your profile on Github. The capstone project based on Natural Language Processing, is really good. It will be helpful in completing the specialization if you are really interested in R language.

Conclusion

In this article, we did a round-up of some of the best Coursera courses for Machine Learning and Data Science. We hope it was beneficial for you and will help you make an appropriate choice of course on Coursera for your machine learning and data science journey.