Machine learning is perhaps one of the most researched and in-demand tech domains in the world right now. For this reason, there are now many universities offering advanced courses on machine learning. But do you really need a master’s degree or a Ph.D. for it?
You do not need a master’s degree or a Ph.D. to start research or professional work in machine learning. While pursuing these advanced courses will definitely help you, they are not absolutely crucial. There are plenty of resources online to get started on your own and build a decent resume.
In this article, we will explore this subject in detail. We will also be sharing the basic outline of a curriculum you could follow to master machine learning on your own. Let’s get started!
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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What Makes You an Expert on Machine Learning?
There’s a popular misconception that machine learning can only truly be mastered by pursuing a master’s degree course or a Ph.D. on the subject. After all, it is one of the most advanced research topics in the tech industry right now. But far from it, anybody can pretty much learn and eventually master machine learning by following online courses.
You don’t become an expert on machine learning simply by earning an advanced degree on the subject. Sure, the master’s course or Ph.D. journey will give you a lot of knowledge on the subject and maybe even some more practical knowledge. But you become a machine learning expert when you start working on machine learning projects.
You will have to come in terms with the fact that a master’s degree or a Ph.D. will involve a lot of research work and project work as part of the curriculum. As such, someone with one of these is bound to start off with a lot more experience than someone who’s simply taken a bunch of online courses. It is for this reason we recommend that you work on a few machine learning projects of your own.
Most jobs will prefer someone with a degree as long as the one without the degree doesn’t have some practical advantages. A resume with a few big projects as part of its portfolio will definitely outshine one with just a degree in it.
To summarize, no, you don’t need a master’s degree or a Ph.D. for machine learning. As we shall see in the following section, it is totally possible to design your own detailed curriculum to master machine learning. You might even have some flexibility compared to an official degree.
Designing Your Own Machine Learning Curriculum
Now that we’ve established that you don’t necessarily need a master’s degree or a Ph.D. to be involved in machine learning, let’s discuss how you can go about learning and mastering this domain on your own.
Machine learning isn’t just about machine learning algorithms. If you want the same level of expertise as someone with a master’s degree or Ph.D., there is a specific set of skills you will need to master. We have divided these skills into five categories:
Computer Science
If you’re starting off on your journey into mastering machine learning, your best bet is to start with a fundamental computer science course. Computer science is, after all, at the foundation of machine learning and AI.
Harvard University offers a great course called ‘Introduction to Computer Science’ on edX. This course covers all you need to get started in computer science, from the basics on data structure and algorithms to programming. It also covers a comprehensive look into the domain and scope of computer science itself, including web development, security, and software engineering.
Programming
Once you’ve taken a basic course on computer science, the next step for you would be to master programming to a certain extent. You don’t need to become a wiz software engineer, but knowing your way around the domain of programming an application or software can be very helpful.
For machine learning, try mastering Python and R. The University of Michigan offers a great five-course specialization called ‘Python 3 Programming Specialization’. This course covers everything you need to learn about programming in Python (starting with variables, loops, and conditionals to class inheritance, lambda expressions, etc.) before you can proceed on to a more advanced course.
To learn R, John Hopkins University’s Mastering Software Development in R on Coursera is a great source. You can start from the very beginning and progress to a serious level.
Data Science and Machine Learning Basics
This is where the fun starts. You can now start focusing on learning data science skills and machine learning. Andrew Ng’s famous Machine Learning course on Coursera is a must take for anyone venturing into the domain of machine learning. Ng takes a unique approach with this course by starting with fundamentals and also teaching you some necessary Mathematics along the way.
For data science, the University of Michigan’s Applied Data Science with Python Specialization can serve as a natural succession to the Python 3 Programming Specialization we talked about earlier. This specialization has five courses and teaches you pretty much all you need to know to get started as a data scientist.
Since you are off to master machine learning, it might also help to learn the fundamentals of AI. MIT’s Prof. Patrick Henry Winston has a famous beginner’s Artificial Intelligence course on MIT’s OpenCourseWare. It covers the theoretical fundamentals of Artificial Intelligence.
Mathematics
In order to master machine learning, you will also need some mathematical background. You don’t have to take pure mathematical courses. Rather, it would help to take some data science or computer science-focused mathematical courses.
‘Introduction to Mathematical Thinking’ is a course by Standard available on Coursera that teaches you how to think like a mathematician (without actually having to become one). It covers topics such as Mathematical Logic, Real Analysis, and Number Theory.
Mathematics for machine learning is another course on Coursera that can be useful. Imperial College offers this course, and it covers a range of topics from multivariate calculus and linear algebra to eigenvalues and eigenvectors.
Advanced Machine Learning
Once you’ve covered all of the skills mentioned in the previous sections, it is now time to get into the depths of machine learning. Mastering the skills mentioned in this domain will make you qualified in machine learning as someone with a master’s degree or even a Ph.D.
Andrew Ng’s Deep Learning Specialization on Coursera serves as a natural succession to his beginner’s course on machine learning. This specialization is divided into five courses. The courses are:
- Sequence Models
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
- Neural Networks and Deep Learning
If you want to further your practical skillset, you might want to take ‘Getting Started with AWS Machine Learning’ by Amazon Web Services on Coursera. This course will teach you how to get started with machine learning in one of the most widely used cloud computing services, AWS.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
You don’t necessarily need a master’s degree or a Ph.D. to start working on machine learning. The structured courses could help, but they’re not a necessity. There are plenty of resources online for you to master this domain on your own.
In this article, we shared a curriculum you could follow to study machine learning on your own. If you can back that with a few research projects of your own, you can build a compelling resume.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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