Machine Learning Professionals Need Degree!(?)
During the past few years, we’ve been experiencing an upward trend in talent acquisition in the field of machine learning. Though this field has traditionally been considered as something that only institutions working with huge amount of resources could utilize, wide implementation of machine learning today has transformed the scenario completely. From e-commerce to software product to different business landscapes – machine learning is being implemented to a great extent. As a result, there’s a huge demand of machine learning professionals across industries, throughout the globe.
This may make you interested in charting a career in machine learning and that’s completely logical. It’s difficult to find a field creating more buzz nowadays than this one. Before we begin discussing about different learning avenues like machine learning bootcamps, machine learning certification etc, it’s important to understand that role of a machine learning professional isn’t a pure academic one. You don’t necessarily need to have an enviable academic or research background.
Let’s have a look at the key skillsets required to become a machine learning professional.
1- Major skillsets
1.1- Programming languages
Programming languages like Java, R, C++, Python etc are immensely crucial for any machine learning professional. For example, Hadoop is Java-based, R works great for plots and statistics, C++ can help in speeding the code up etc. Whether you obtain your knowledge by pursuing machine learning degree or machine learning certification or through other avenues, you need to be able to apply, adapt, or address them appropriately throughout your work.
1.2- Data modeling and evaluation
Data modeling aims to find useful patterns like clusters, correlations etc, and/or predicting properties of instances that are unseen previously like regression, classification, anomaly detection etc. Continuous evaluation of effectiveness of a given model is a crucial part of this estimation process. You’ll need to select an appropriate error/accuracy measure as well as an evaluation strategy based on your task at hand. Often, iterative learning algorithms make use of the resulting errors directly to modify the model. So, you’ve to have a robust understanding of these measures for applying them to standard algorithms.
1.3- Applied mathematics and algorithms
Having a solid understanding of algorithm theory and the way algorithm works is important for any machine learning professional. You’ll need to understand subjects like convex optimization, gradient descent, partial differential equations, and quadratic programming, among others. So, it’s imperative to hone your skills in these fields. However, well begun is half done and you might learn machine learning in Silicon Valley.
1.4- Probability and statistics
Probabilities and techniques derived from them occupy a huge part of machine learning algorithms when it comes to dealing with uncertainties in the real world. Statistics, which is closely related to it, provides various measures, distributions and analysis methods, which are necessary for developing and validating models from observed data. So, a good understanding of these is required.
1.5- Advanced signal processing techniques
Feature extraction plays one of the most crucial roles in the machine learning landscape. Different types of problems need different solutions and advanced signal processing algorithms like shearlets, wavelets, bandlets, contourlets etc can help you in that to a great extent. Learning all these stuff will help you climb the ladder up fast.
1.6- Software engineering
As a machine learning professional, you’ll be required to understand the working methods of different pieces when combined together, and ways to communicate with them using database queries, library calls etc. To allow your algorithms scale well with increasing amount of data while avoiding bottlenecks, careful system design might be required as well. So, it makes sense to learn software engineering and system design best practices like version control, documentation, requirements analysis etc.
2- Is it a must to have a degree?
It mayn’t be possible for every machine learning aspirant to necessarily have a university degree. Though it surely helps to get a robust foundation in data structures, algorithms, and computer science fundamentals, you mayn’t be able to solve problems using your own brains. Many degree providers teach a variety of common cookie-cutter algorithms, many of which aren’t commonly used in today’s programming languages and thus, mayn’t be much useful. Finding performant solutions is one of biggest goals of studying algorithms and data structures, and it mayn’t be useful to rely entirely on textbooks whose first editions came to the market years ago.
When it comes to getting a job in the field of machine learning, less expensive and faster options like a machine learning certification can be a feasible and effective option instead of a degree. Today, employers actually want to know about your expertise based on proof of skills, not pieces of paper.
Apart from these, average college tuition fee for getting a university degree mayn’t be affordable for every aspirant. Also, it’s important to keep in mind that every school doesn’t offer high-caliber courses that can make someone fully equipped for today’s machine learning landscape, and most of these courses can be taken online at a much lower cost.
3- Ways to master machine learning without a degree
Before delving deeper into the process, it’s advisable that if you find an actually useful university program at a minimum cost, you shouldn’t miss out on that. Unfortunately, that isn’t a common case, which compels us to chalk out different ways to become skilled at machine learning.
3.1- Learn online
If you belong to the self-directed and self-motivated group, or don’t want to shell out a substantial amount of money for a degree, online training should be your best bet. There’re lots of online resources available at very little costs that can help you get a solid understanding of machine learning fundamentals, which will pave the road for your future progress.
3.2- Go for a machine learning bootcamp
Bootcamp can be a great resource to master machine learning. It’ll not only cost you significantly less, but will also help in addressing those specific areas that you actually need to learn, within a much shorter duration. However, before enrolling in a bootcamp, make sure that it offers both adequate amount of study and practice to help you have robust understanding in the domain.
3.3- Go for a machine learning certification
Machine learning landscape requires an increasingly changing and diverse set of skills, and certification programs can help you learn and hone your skills throughout your career. A majority of machine learning certifications are designed based on actual needs of the students. In addition, traditional degrees have often been criticized for failing to generate graduates with workforce-ready, tangible skills. Certification programs help students to gear up for employment by aligning their skills that are actually needed by employers. While certification programs aren’t designed as a replacement for a degree, they allow students to learn the required skills by investing a smaller amount of money and time. And that’s why it mayn’t be absolutely required to have a degree to become a master in machine learning.
3.4- Find a good mentor
Obtaining a machine learning certification is just the initial step of your learning trajectory. The fast paced machine learning field is going through rapid changes and this is where it makes sense to have a strong mentor by your side, throughout your learning journey. You don’t necessarily need to know someone personally as the interaction can be done online very well.
3.5- Explore all avenues available
Once you’ve enrolled in a machine learning certification or have started reading relevant books, it’s time to explore other ways to solidify your learning. For example, find a study group and start some pair programming to stretch your wings. Subscribe to useful blogs that help you stay on top of the current trends and what’s coming next in your field. It’s important to understand that different types of learning avenues help you in gaining expertise in some specific areas. For instance, you mayn’t be able to get adequate practice from books; video lessons need a significant amount of practice to learn; and exercises usually focus on very specific concepts, among others. Ideally, you should mix all the avenues and try to learn from multiple mediums.
If you want to become a specialist in machine learning, obtaining a degree can really make a difference, apart from the pay being better than average. In that scenario, attending a bootcamp or getting a machine learning certification may fall short in helping you.
Assuming you’re not taking the traditional degree route, networking plays an immensely crucial role in extending your reach, both in terms of knowledge and opportunities to get hired. Unlike a good university where you’re likely to meet like-minded people, chances are you’ll have to work to build your network when you take other avenues. Staying up to date with the upcoming trends is equally important as well. You’ve to be aware of the news related to developments, theory, algorithms, and tools in the domain. Online community goes through rapid changes. So, it’s good to be prepared and cultivate these changes. Spending some quality time online also makes a lot of sense. A significant number of great machine learning books are available online for free that can help you become a master in machine learning.
You can always get a good grasp of machine learning without having a degree, provided you religiously explore all the available avenues. In addition, since machine learning needs innate curiosity, if you have it, you’re already a natural candidate to excel in this field. However, you should improve skills with a successful course such as Magnimind Academy.
. . .
To learn more about machine learning requirements, click here and read our another article.