Machine learning is the study of computer algorithms that can automatically improve their capabilities through experience. The study is part of artificial intelligence (AI.)
They can find hidden patterns in data, predict outcomes, and make decisions on behalf of us – with full autonomy and speedup.
In the second decade of the 21st century, it is obvious that machine learning (ML) is the future of technology. Companies in every industry are utilizing it to optimize their operations.
Thus, although the subject is still nascent, it’s not too early to start learning it. There are already thousands of use cases for machine learning, and even more are being developed right now, including self-driving cars, chatbots, and facial recognition software.
If you are interested in a lucrative tech career, your path is in machine learning. According to Indeed, machine learning engineers in the United States earn $151,223 on average, probably one of the country’s highest-earning jobs.
Furthermore, the career prospects are excellent. As more companies transform and use more machine learning and artificial intelligence in their businesses, they all need machine learning specialists to assist them.
Currently, it is possible to get started with machine learning by taking online courses. There are many resources online to learn machine learning, but only some are of high quality.
In this article, I will present to you the ten best machine learning courses that you can learn comfortably anywhere and anytime. I hope you enjoy it and master the subject in no time!
Things You Should Know
Q1: What are the prerequisites?
A: You need to understand programming languages. Python is an optimal choice. Though you don’t need to be an expert programmer, you should have the ability to code in Python and some of its libraries: NumPy and Pandas. You will need both for data preprocessing.
A good understanding of basic data science concepts, statistics for data science, linear algebra, and college-level calculus would be beneficial, especially if you want to drill deep into the theoretical part.
Some courses may do not have these requirements. However, I still insist you have all the prerequisites because it will help you learn faster and smoother.
Q2: How should I start learning machine learning?
A: Please take a look at the prerequisites. If you lack any knowledge, take an online course on it. After completing those courses, you can pick some in this post to start your machine learning journey.
Q3: How do you pick machine learning courses?
A: I only pick paid machine learning courses that satisfy all conditions. (Free courses at the end of the article may not fulfill some of these)
- Accessible on credible learning platforms
- Provides excellent learning experience according to public reviews or my first-hand experiences
- Is value for money
- Offers significantly better experience than studying from books and documentations
- Flexibility (Able to learn on my devices, don’t have rigid pricing plans, etc.
Disclaimer: This post contains affiliate links. If you purchase any machine learning courses through my links, I will get a small commission from these platforms. I promise I will use this income to create better content for all visitors.
Best Courses on Machine Learning
These courses will teach machine learning concepts and algorithms, creating excellent foundations for learners to proceed to advanced topics such as deep learning.
Some courses will include deep learning in their curriculum and “regular” machine learning, hence creating an all-in-one learning experience. However, the knowledge in these courses would not be enough for deep learning. You will have to find more deep learning courses elsewhere.
You will be using Python in the courses unless indicated otherwise.
1. Machine Learning by Andrew Ng
This Coursera course from Stanford University is unarguably one of the best machine learning courses available online. You will learn with Andrew Ng, a Stanford professor who is a leading researcher in machine learning and artificial intelligence and also a co-founder of Coursera.
Below are what you will learn from this course.
- Review of linear algebra concepts
- Linear regression with single and multiple variables (Short tutorial to Octave and MATLAB included)
- Logistic regression and regularization
- Neural networks (Representation and Learning)
- Best practices for machine learning applications
- Support Vector Machines (SVMs), a machine learning algorithm for classification
- Unsupervised learning (K-means clustering and principal component analysis)
- Anomaly detection and building recommender systems
- Machine learning for large data sets
At the end of the course, you will apply machine learning concepts you have learned throughout the curriculum to build a photo OCR that can recognize elements such as numbers and words in images.
You should spend approximately 60 hours on the course. Auditing the entire course is free. However, you might want to pay $79 to get the complete experience, so the instructor will grade your assignments and send you a certificate if you complete the entire course.
Comments and Tips
A few courses on the market allow students to learn with an expert of this caliber. I think it is a no-brainer to take this course or at least audit it.
From an overall view, you learn all basic machine learning concepts, a solid foundation for those who want to progress to deeper topics such as deep learning or reinforcement learning.
Though Andrew Ng will teach your theories and their applications, which is great, you should not expect to be an expert in machine learning after completing this single course.
The content inside is broad and not in-depth. You will need to take other courses if you are serious about learning machine learning.
Still, this course is excellent for every beginner to test the waters.
2. Machine Learning Specialization
For those who complete Andrew Ng’s course and would like to get more hands-on experience on machine learning, the University of Washington’s short specialization is a perfect follow-up.
This course will focus primarily on case studies. You will learn how to apply machine learning techniques by using Python. Thus, prepare to code extensively in this course
There are four minor courses in this specialization as follows.
1. Machine Learning Foundations – The first course will introduce you to machine learning tools that fit specific tasks and provide a brief overview of later courses.
2. Regression – You will start with the simplest machine learning models: Regression. You will use different types of regression models to predict house prices. The instructor will explain features, techniques, and how to implement them in Python.
3. Classification – This course will teach you about one of the most popular machine learning applications. You will create classifiers that can analyze sentiment and predict loan default by utilizing logistic regression, decision trees, and many more.
4. Clustering & Retrieval – The last course will guide you through machine learning algorithms that can create recommender systems (you have seen it in Amazon when you view any product.) You will learn many concepts and techniques here, including K-nearest neighbors, MapReduce, and many more.
The university suggests that you spend 3 hours a week for 7 months to complete the entire specialization. For the full experience, you will pay $49 per month. However, you are free to audit the curriculum, albeit with no graded assignments and completion certificates.
Comments and Tips
The approach of this specialization is fascinating to learn. The course will not focus on boring theories but will touch on machine learning tasks you are already familiar with.
You will also understand how data scientists utilize machine learning models to predict future data or events or even how a software engineer creates recommender systems for e-commerce sites and the like.
The great thing I like about this online machine learning course is that the instructor will provide you optional content in every module. These are in-depth knowledge that you can further study if you wish.
In my opinion, you should get a complete experience, as you will get feedback on your assignments and machine learning projects so that, as a beginner, you can ensure you are on the right track.
3. IBM Machine Learning Professional Certificate
IBM Machine Learning is an online comprehensive machine learning course taught by IBM experts. You will start from the beginning and proceed to step-by-step to more advanced content.
**Those who want to take this course should have a solid background in mathematics, including statistics, calculus, and linear algebra. **
This specialization comprises 6 minor courses as follows.
1. Exploratory Data Analysis for Machine Learning – This first course will review data analysis knowledge necessary for learning machine learning.
You will retrieve data from various databases such as SQL and NoSQL and clean any issues and problems associated with them.
2. Regression – You will get started with supervised learning by training linear regression models to predict various outcomes. Later on, you will learn several techniques and best practices that can improve your results.
3. Classification – You will move toward logistic regression models, decision trees, and many more. Your task here is training them to classify outcomes.
The instructor will also guide you through best practices and supplementary techniques, including train and test splits and the handling of unbalanced classes dataset.
4. Unsupervised Learning – You will learn about machine learning algorithms that allow your models to find insights from datasets without the specific target variable. Your instructor will also explain clustering and its problems in detail.
5. Deep Learning and Reinforcement Learning – IBM instructors will introduce you to these two advanced machine learning topics. You will understand their theoretical relationship to neural networks and real-world applications.
6.Specialized Models: Time Series and Survival Analysis – This final course will integrate machine learning with the time series model. You will understand how to adapt your machine learning models to be suitable for making predictions on datasets with a time component.
IBM suggests spending 3 hours per week for 6 months to complete the course. The pricing for this specialization is $39 per month.
Comments and Tips
This online course on Coursera is one of the top machine learning courses, especially if you have a Mathematics background. The theoretical part will be more focused than those two courses above.
Unlike other alternatives on Coursera above, this course is more comprehensive, as it discusses all current machine learning areas. However, the content will not be very in-depth.
If you own Coursera Plus (details below), it may be best if you take this specialization along with Andrew Ng or the University of Washington course.
If you want to enroll in two or more courses on Coursera, I suggest you subscribe to Coursera Plus.
At $399 per year ($33.25 per month,) you will get access to more than 3,000 courses for 1 year. In other words, you can enroll any course on Coursera for no extra costs. You can access all courses for one year, which is beneficial for those who have a tight schedule.
Apart from more courses and flexibility, the monthly payment is considerably lower than enrolling in each course one by one, normally at $39-$79 per month.
You can try Coursera Plus free for 14 days
4. Machine Learning by Columbia University
This edX course is another solid option to consider for those who seek an all-inclusive tutorial. The creator of this course is Columbia University. You will learn most of the machine learning concepts in one go (deep learning excluded.)
You need to understand mathematics indicated in the prerequisites (see above) to learn smoothly.
The course comprises two parts as follows.
The first part will discuss supervised learning, including regression and classification. The instructor will explain each technique and algorithm, such as linear/logistic regression, support vector machines, and classifiers, in detail.
The second part will tackle unsupervised learning and go in-depth on its three significant fundamental problems, including data clustering, matrix factorization, and sequential models.
In each part, you will learn machine learning from both probabilistic versus non-probabilistic viewpoints, along with techniques to optimize algorithms for improved results. In this part, you will learn to build recommendation engines as well.
Similar to Coursera’s online courses for machine learning, you can audit this course for free. However, if you want a certificate and feedback on your assignments, You have to pay a one-time $249.
To complete the entire course, you will have to spend 8-10 hours a week for 12 weeks. The schedule is quite strict, as this is an instructor-led course.
Comments and Tips
If you want to learn machine learning by heart, this course is one of the leading machine learning courses available since the instructor will help you develop a complete mathematical understanding of all algorithms.
Still, this approach may not be suitable for learners who want clear and simple instruction. Thus, I hope you consider what you really want from the course.
Identical to typical edX courses, you should expect a high workload. I don’t think all would be able to make it (spending 8-10 hours a week, which is not easy for those with full-time jobs.) Therefore, make sure you have sufficient time to learn before enrolling.
5. Data Science: Machine Learning and Predictions
This course is a lighter alternative compared to the above Columbia course. You will learn with UC Berkeley faculty members who are well-experienced in the field.
This course will focus primarily on regression and classification, which will help you create a model that offers the best predictions.
Below is what you will learn from the course:
- A deep dive on regression, including correlation, bootstrap method to quantify uncertainty
- K-nearest neighbor algorithm for classification
- How to test and optimize the efficiency of your models
- Applications to real-world scenarios, including medical diagnosis
This course is self-paced. Thus, you can create your own schedule to complete the course. However, UC Berkeley suggests spending 4-6 hours per week for 6 weeks.
Auditing the entire course is free. However, if you want a full experience, you have to pay a one-time $199.
Comments and Tips
If you want a machine learning course that focuses on predictions, this course is probably one of the best options.
You will learn many machine learning techniques that help you build better models without mathematics’s complexity, as you won’t need calculus or linear algebra to start learning.
Furthermore, you can also learn at your own pace. At 4-6 hours per week, the course’s workload is moderate and more manageable, which is excellent for those with a busy schedule.
Nevertheless, this UC Berkeley course lacks comprehensiveness of other training courses. You will have to find more to take if you want a deeper dive.
6. Machine Learning with Python: from Linear Models to Deep Learning
If you want a course that includes all current knowledge of machine learning, you might want to enroll in this in-depth tutorial from MIT.
You need knowledge in linear algebra, calculus, and statistics to take this course.
- Introduction to machine learning
- General machine learning concepts (Linear classifiers, regularization, gradient descent, over-fitting)
- Linear regression
- Recommender problems
- Non-linear classification and kernels
- Deep learning and neural networks
- Reinforcement learning and natural language processing
- and many more
In essence, you will learn how to turn datasets into automated predictions of future data by utilizing machine learning.
Besides the above content, you will complete as many as 3 machine learning projects, including creating an automatic review analyzer and digit recognition.
As an instructor-led course, your schedule will be tighter than a self-paced one. MIT expects students to spend 10-14 hours per week.
You can audit this course for free, while a complete experience will cost you $300. Still, if you enroll in the Micromaster program, which this course is a part of, you will get an extra discount.
Comments and Tips
The best thing about this tutorial is it covers all major concepts within a single machine learning course. If you manage to finish all of the course videos, readings, and assignments, you will have a strong foundation in the subject and be ready to develop sophisticated models of your own.
As usual, MIT courses are math-heavy. If you crave mathematical explanations of concepts, this is probably the best machine learning course for you in the market.
For those who dislike Maths, there are some better alternatives elsewhere.
7. Machine Learning A-Z™: Hands-On Python & R In Data Science
This Udemy course by Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience team is one of the few online courses that offer classes on machine learning for both Python and R.
The entire curriculum consists of 2 parts, a Python part and an R part. Course content for both is similar. The only difference is the programming language used. If you want to learn machine learning using Python, you can skip all of R’s parts with no issues.
Below is what you will learn
- Introduction to machine learning (A brief introduction to clarify basic concepts such as the difference among machine learning, deep learning and artificial intelligence)
- Data Preprocessing (Cleaning and dealing with missing data, Encoding categorical data and feature scaling)
- A deep dive on regression (Linear, Polynomial, Support Vector, Decision Tree and Random Forest)
- Logistic Regression and K-nearest neighbors
- Support Vector Machine (SVM), Decision Trees and Random Forests Classification
- Apriori (Association Rule Learning)
- Reinforcement Learning and Natural Language Processing (NLP)
- Deep Learning (Artificial/Convolutional neural networks)
- Dimensionality reduction
- How to choose the suitable models and optimize for optimal performance
The video length of the course (including Python and R) is 44 hours. Thus, each part will be 22 hours long. After purchasing, you will have lifetime access to the content.
As one of the most popular machine learning courses on Udemy, Machine Learning A-Z™ receives 4.5/5.0 stars from more than 744,000 students.
I have taken this course myself. Below are the pros and cons of it.
- One of the best online courses on machine learning for beginners
- Well-structured and concise
- Includes all areas of machine learning from basic regressions to deep learning
- Good supplementary resources
- Python and R options to choose – You can choose to complete either of them or finish both if you are interested.
- Always up-to-date (The instructors provide an update every 2-3 months.)
- Self-paced hence fits those with busy schedules.
- The instructor notes that you need only high-school mathematics for this course. I have to disagree strongly. The content is too sophisticated for most students to understand without computer science knowledge (able to code in Python or R) and basic data science. I survive before I took Python courses before.
- Sometimes, the instructor’s voice is slightly mumbled, so it isn’t always easy to understand.
Datacamp is an online platform that teaches data science and related applications, including machine learning. If you want to learn data science innovatively anytime, anywhere, I think you should consider Datacamp
The best thing about Datacamp is its interactive learning. You won’t learn much from boring videos. On the other hand, you will follow text instructions and complete tons of exercises online as below.
You can choose any learning path, including
- Machine Learning Fundamentals with Python
- Machine Learning Fundamentals with R
Each track comprises several courses. For example, the Python option will have 5 classes, which will take approximately 20 hours to complete.
In total, Datacamp has as many as 92 online courses for machine learning, along with extra 21 projects and hundreds of data-science-related courses.
Below are Datacamp’s subscription plans: The pricing is for annual plans.
- Standard – $12.42 per month
- Premium – $33.25 per month
A Standard plan will enable your access to most courses on the Datacamp platform (more than 335 of them).
However, you will not be able to complete 80 data science and machine learning projects and take Oracle, Tableau, and Power BI courses included in the Premium plan.
Comments and Tips
I am fond of Datacamp’s interactive learning. As the course is somewhat gamified (with hints and the Experience system), I can learn on their platform much longer than on video courses.
Another great thing is the platform is very user-friendly. You don’t need to install anything, as you will always learn and code on the platform that simulated real-world experience.
Thus, this advantage also enables you to learn anytime, anywhere. You can even use your smartphones and tablets to learn while you are on vacation.
However, you should not expect their machine learning courses to be very in-depth. You will be able to grasp basic and lower intermediate knowledge. Beyond that, you have to find online courses elsewhere.
For the pricing, the Standard plan is more than enough. I think Premium content is meh. Projects are not very in-depth, while their Tableau courses are just ok. Thus, I don’t think it is worth the price unless you buy it at a discount.
I suggest everyone tries Datacamp before deciding. You can access the first chapter of every machine learning course for free after creating an account.
Applied Machine Learning Courses
Unlike courses that I have recommended above, these courses are applied machine learning courses for specific industries, such as Financial Engineering, Management, and many more.
Not all these courses are for beginners. You may need prior machine learning skills.
Machine Learning and Finance – An excellent program on edX by the New York University. You will learn how to apply machine learning to the financial industry, such as creating better predictions for the stock price.
Auditing the course is totally free. A complete experience will cost $1,438.
Tiny Machine Learning – This course by Harvard provides insights into applying machine learning knowledge to small devices. You will design, build and deploy your tiny machine learning on your own.
You are free to audit the course, while the full experience costs $537.
Fintech: AI & Machine Learning in the Financial Industry – A course by UT Austin to educate learners how to apply machine learning to fintech areas, such as crowdfunding, Robo-advising and many more
This one-time pricing is $850.
Quantum Machine Learning – A course from University of Toronto that foresees how we will use quantum computers with machine learning
The course is free to audit, while the entire course costs $49.
Bayesian Machine Learning in Python: A/B Testing – Those in the marketing space would be familiar with A/B testing, which allows them to find landing pages or Facebook Ads that perform best. This course will provide insights into how it works and how to improve the performance even further.
You have to buy this Udemy course to access all of its content.
Free Machine Learning Courses
Below are free machine learning courses you can take online.
Introduction to Machine Learning – A free course by Udacity. You will learn all basic concepts of machine learning, starting from Naive Bayes to clustering.
This course is part of the Udacity nanodegree program, which is their comprehensive paid course. I don’t recommend this program because it is too pricey ($399 per month.)
Elements of AI – Supported by the Finnish government, Elements of AI teaches learners basic knowledge in machine learning and AI (Artificial Intelligence.)
Edureka Free Machine Learning Tutorial – Edureka is a platform that offers live machine learning class. However, the team allows you to access parts of their content for free.
Exclusive: Lambda School
If you are
- in the US
- want to learn both data science and machine learning from the very beginning
- interested in data-science-related careers such as data scientist, data analyst and machine learning engineer
your best bet is the Lambda School.
Lambda School provides an online machine learning Bootcamp, which you will learn live with the experts for 6 months or 12 months.
If you enroll full-time, you will spend 7 hours daily from Monday to Friday on training with experts. Thus, this program will be super intense, but you will master data science and machine learning in no time.
You don’t need any prerequisites to enroll in the program, as your instructors will teach you all the way from Python, linear algebra to machine learning.
After graduation, Lambda School will help you search for jobs. Thus, you can be confident they won’t leave you alone applying for jobs.
The best thing is you don’t need to pay upfront (thus, no use for student loans). The tuition is $0. You will pay 17% of your salary only if you make more than $50,000 per year from your new job, which is related to the knowledge you learned from Lambda School (data science and machine learning.)
The income share agreement will last for 24 months or end immediately if your payment to Lambda reaches $30,000.
For example, if your new job is a data scientist, and earn $70,000 per year, you will have to pay Lambda School 11,900 x 2 = $23,800 over 2 years.
However, if you are a machine learning specialist and earn $150,000 a year, you will have to pay Lambda School $30,000. However, the contract will terminate faster, as your payment already hits the ceiling.
In other words, how much you pay will depend on your compensation, but this will not exceed $30,000.
Furthermore, if you have financial difficulties, you can contact Lambda School for assistance.
Lambda School lets you try the program for 1 month. If you don’t want to continue, you will have no commitment at all.
Sound fair, isn’t it? Why don’t you try it now?
International learners can also enroll in Lambda School if they are interested. However, they have to pay $15,000 upfront as tuition. The income share agreement will not apply to them.
What Should I learn after Machine Learning
The optimal option is to learn deep learning. You can learn Pytorch or Tensorflow to train your artificial neural network or explore reinforcement learning, natural language processing, and computer vision.
If you complete all these, I think you are done with machine learning training. You can then start your own machine learning project or submit your application for tech jobs.