Machine learning is one of those courses you can eternally get better at, but often seems hard when you start…
I’ve been there. I’ve so been there.
Now, I’m not saying Machine Learning is bad necessarily, but just that if you want to continue to push yourself in this industry…
You’ll need some good foundation since data science is more competitive now than it ever has been.
And I know how hard it is to just find the right platform for the best Machine Learning Courses.
But, what if I told you that I got what you are looking for?
Yes, you read that right.
Coursera is the platform!
After a long search and feeling like a lost sheep in the desert, I found not only Coursera but the best Coursera courses for Machine Learning.
And in this article, I have outlined some of these top Machine Learning courses just for you.
So yeah you can smile as you go through this article, and guess what?
The wait is finally over!
Before we get to the most exciting part, don’t forget to check out my previous article.
In these courses we will look at what each course teaches, the duration, and the number of students.
As you may have heard before Machine Learning is the science of getting computers to act without explicitly being programmed.
In the past decade, Machine learning has given us self-driving cars, practical speech recognition, effective web search, and vastly improved understanding of the human genome.
Did you know that you might be using Machine learning on a daily basis?
Well, Machine learning is so widespread today that you probably use it dozens of times a day without knowing it.
Many researchers also think it is the best way to make progress towards human-level AI.
So in this class on Coursera, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
But more importantly, you’ll learn about not only the theoretical underpinnings of learning…
But also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
Finally, this course teaches you about some Silicon Valley’s best practices in innovation as it pertains to Machine Learning and AI.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
The topics of this Coursera course include:
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications.
So that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Ratings: 4.9 stars ( 144, 934 ratings).
Students Enrolled: 3, 487, 538.
Duration: 54 hours.
Subtitles: Chinese (Simplified), English, Hebrew, Spanish, Hindi, Japanese.
Let’s face the facts, AI is not only for engineers.
If you want your organization to become better at using AI, this is the course to tell everyone…
And that’s why it’s even appearing here as one of the best Machine Learning courses on Coursera.
In this Coursera course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can and cannot do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- Also how to navigate ethical and societal discussions surrounding AI
Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
Rating: 4.8 stars ( 24, 713 ratings).
Students Enrolled: 479, 624.
Duration: 6 hours.
Subtitles: Chinese (Traditional), Arabic, French, Chinese (Simplified), Vietnamese, German, Thai, English, Spanish, Japanese.
In this course on Coursera, you will learn how to build a successful machine learning project.
If you aspire to be a technical leader in AI and know-how to set the direction for your team’s work, this best selling Machine Learning course will show you how.
You must note that much of the content on this course has never been taught elsewhere; this is because most of it is drawn from the instructors’ experience building and shipping many deep learning products.
When you take this course, you’ll find out that it has two “flight simulators” that let you practice decision-making as a machine learning project leader.
This provides “industry experience” that you might otherwise get only after years of ML work experience.
And after 2 weeks of learning, you will:
- Understand how to diagnose errors in a machine learning system, and
- Be able to prioritize the most promising directions for reducing error
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
If you have ever wasted months or years through not understanding a similar course before, I recommend that you try out this two-week course on Coursera.
This is a standalone course, and you can take this so long as you have basic machine learning knowledge.
It is the third course in the Deep Learning Specialization.
Rating: 4.8 (42, 458 ratings).
Students Enrolled: 261, 960.
Duration: 5 hours.
Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, Turkish, English, Spanish.
The instructor introduces you to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
This course on Coursera starts with a discussion of how machine learning is different from descriptive statistics and introduces the scikit learn toolkit through a tutorial.
The issue of dimensionality of data is also discussed in this course and the task of clustering data.
But that’s not all, you will also tackle how to evaluate those clusters.
Supervised approaches for creating predictive models will be described by the instructor…
And you will be able to apply the scikit learn predictive modeling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting).
This best selling course on Coursera ends with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.
By the end of this course, you will be able to identify the difference between a classification and clustering technique.
You will also have the ability to identify which technique they need to apply for
- A particular dataset and need
- Engineer features to meet that need
- Write python code to carry out an analysis
Note: this course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python, and before Applied Text Mining in Python and Applied Social Analysis in Python.
Rating: 4.6 (6, 277).
Students Enrolled: 184, 990.
Duration: 34 hours.
Subtitles: English, Korean.
This course Coursera dives into the basics of machine learning using an approachable, and well-known programming language, Python.
In this course, you will review two main components:
First, you will learn about the purpose of Machine Learning and where it applies to the real world.
Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
By just putting in a few hours a week for the next few weeks, this best Machine Learning course on Coursera will give you:
- New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
- New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
- And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon the successful completion of the course.
Rating: 4.7 stars (9,449).
Students Enrolled: 155, 034.
Duration: 22 hours.
Subtitles: English, Vietnamese.
This 2-week accelerated on-demand course on Coursera introduces you to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP).
It provides a quick overview of the Google Cloud Platform and a deeper dive into the data processing capabilities.
At the end of this course, you will be able to:
- Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
- Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
- Employ BigQuery and Cloud Datalab to carry out interactive data analysis
- Choose between Cloud SQL, BigTable, and Datastore
- Train and use a neural network using TensorFlow
- Choose between different data processing products on the Google Cloud Platform
Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:
A common query language such as SQL, Extract, transform, load activities, Data modeling…
Machine learning statistics, Programming in Python, and Google Account Notes.
Rating: 4.6 stars ( 11, 399).
Students Enrolled: 147, 218.
Duration: 12 hours.
Machine learning (ML) is one of the fastest-growing areas in technology and a highly sought after skill set in today’s job market.
The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years.
Yet it’s estimated that currently there are 300,000 AI engineers worldwide, but millions are still needed.
This means that there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications. No matter what your skill levels are.
Learning the foundations of ML now will help you keep pace with this growth, expand your skills, and even help advance your career.
This course on Coursera will teach you how to get started with AWS Machine Learning.
The key topics of this course include:
- Machine Learning on AWS
- Computer Vision on AWS
- Natural Language Processing (NLP) on AWS
Each topic consists of several modules deep-diving into a variety of ML concepts, AWS services as well as insights experts to put the concepts into practice.
Rating: 4.5 stars (4, 204).
Students Enrolled: 107, 723.
Duration: 8 hours.
This Machine Learning course on Coursera is intended to be an introduction to machine learning for non-technical business professionals.
There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background.
For reasons that are covered in this course, that’s not the case.
In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.
By the end of this course, you will have learned how to:
- Formulate machine learning solutions to real-world problems
- Identify whether the data you have is sufficient for ML
- Carry a project through various ML phases including training, evaluation, and deployment
- Perform AI responsibly and avoid reinforcing existing bias
- Discover ML use cases
- Be successful at ML
You’ll need a desktop web browser to run this course’s interactive labs via Qwiklabs and Google Cloud Platform.
Rating: 4.6 stars (3, 035).
Students Enrolled: 87, 089.
Duration: 12 hours.
What is machine learning, and what kinds of problems can it solve?
Google thinks about machine learning slightly differently for being about logic, rather than just data.
This course teaches you about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.
Then, the instructors discuss the five phases of converting a candidate use case to be driven by machine learning and consider why it is important the phases not be skipped.
The instructors end with a recognition of the biases that machine learning can amplify and how to recognize this.
Rating: 4.6 stars (5, 925).
Students Enrolled: 75, 580.
Subtitles: French, Portuguese (Brazilian), German, English, Spanish, Japanese.
In the first course of this specialization, you will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization.
One of the best ways to review something is to work with the concepts and technologies that you have learned.
So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform
Basic SQL, familiarity with Python and TensorFlow
Rating: 4.5 stars (1, 356).
Students Enrolled: 36, 287.
Duration: 13 hours.
Machine Learning, often called Artificial Intelligence or AI, and it is one of the most exciting areas of technology at the moment.
We see daily news stories that herald breakthroughs in facial recognition technology, self-driving cars, or computers that can have a conversation just like a real person.
In this course on Coursera, you will learn to understand the basic idea of machine learning, even if you don’t have any background in math or programming.
Not only that, but you will also get hands-on and use user-friendly tools developed at Goldsmiths…
The University of London to actually do a machine learning project: training a computer to recognize images.
This course is another best Coursera course for Machine learning because it is for a lot of different people.
It is always better to start with the high-level concepts before the technical details, but it is also great if your role is non-technical.
You really need to understand this technology, and this course is a great place to get that understanding.
You might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment.
Whoever you are, this course will guide you through your first machine learning project.
Notice: this course is designed to introduce you to Machine Learning without needing any programming.
That means that it doesn’t cover the programming based machine learning tools like python and TensorFlow.
Rating: 4,.7 (1, 608)
Students Enrolled: 49, 205.
Duration: 22 hours.
Machine Learning technology is set to revolutionize almost any area of human life and work.
And so will affect all our lives, and so you are likely to want to find out more about it.
Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it.
While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming…
I believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should.
The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms.
Have you ever taken any of these Machine learning courses on Coursera before?
If yes, please share your experience in the comments.