Machine Learning jobs are in high demand. Number show that between 2018 and 2019, machine learning job roles grabbed the top spot with the largest number of job postings (75%) followed by Deep Learning Engineers (61%) and Data Scientists (58%). Machine Learning Engineers also have the highest paying job with a take-home salary of $142,000 and upwards.
The average salary in India for ML engineers is ₹9.5 lakh per annum. With the AI market maturing, we are seeing a growth in differentiated data science jobs with demand for ML and Deep Learning Engineers soaring.
But despite the soaring popularity of Machine Learning engineers, there is a lack of insight on how to choose a course that complements existing skills and strikes a fine balance between statistical and mathematical concepts and practical knowledge. Also, machine learning courses aren’t just about fundamentals and Capstone projects. At the end of the day, candidates should be able to answer what the course helped them achieve — high paying job, skill upgradation or both.
AIM spoke to Sumeet Bansal, CEO & Co-founder, AnalytixLabs to understand what are the factors professionals should watch out for before choosing a machine learning course.
“Machine Learning is as much of an art as it is a science. So experiential learning is very important, hence choosing a course that enables you to apply classroom learning is very important. There are a number of online resources and MOOCs available out in the market. With new developments in the space of ML & AI, these courses have a limited shelf-life. They outdate very fast and also end up overloading aspirants with befuddling information rather than imparting practical knowledge. Investing in a structured program, with meticulously crafted and frequently updated curriculum, under the mentorship of seasoned practitioners is totally worth it for most individuals,” he said.
In this article, AIM lists down top factors professionals should weigh before choosing a Machine Learning course. From dabbling into machine learning the self-paced way to enrolling in a professional machine learning course, AIM gives pointers on how best to choose machine learning course that can improve the overall employability and land you a high-paying job.
Are You Learning ML The Difficult Way?
- You may waste a lot of time searching and stitching together learning materials/resources that can complement your work/educational background
- There are many online courses around the same topic but it’s hard to find those that pack a project-based learning experience
- Good Machine Learning courses aren’t just about the content and materials but also about teaching methodology and mentorship
- Given the high dropout rate in online courses, mentorship plays a pivotal role in helping one stay motivated, committed and on-track
Machine Learning Is A Vast Field. Here’s The Fastest Way To Learning ML
How To Choose A Machine Learning course that lands you a job
1. Choose a course as per your educational/work background
One of the best ways to approach machine learning roles is by leveraging your existing skill-set based on your work background. Since ML is a vast field, you should choose a course that augments your current skill-set. For example, if someone is already working in the analytics field, s/he will already be familiar with some fundamentals as compared to a beginner who will have a different set of needs.
There are a core set of ML techniques that are essential and take care of most of the industry applications. Whether it is a beginner-level course or intermediate-level course, any course must help to learn these techniques in- depth and their impact such as feature engineering, model regularization, hyper-parameter tuning and cross-validation along with typical list of supervised, unsupervised and regression based methods. Some advanced applications like image processing, building image classifiers and chatbots are too specific and can be taken up for the second stage of learning.
Tip: One should stay clear of courses that have a long list of fancy techniques without enough depth for important concepts.
2. Choose your projects wisely
Building ML models is an art in itself and one must be able to strike a balance between practical business application, statistical and mathematical robustness. “There are several nuances to how to tune and regulate a model. Often we have come across who claim to know ML but people can not even differentiate between a statistical and ML models,” says Sumeet Bansal Co-founder & CEO of AnalytixLabs. Rigorous hands-on practice through assignments and projects can give one strong practical exposure and also prepare for job interviews with confidence. One should choose the projects wisely. We are seeing an increased uptake of machine learning in domains like Banking, Insurance, Telecom, Online Retail and e-commerce where most job opportunities lie.
Tip: Build a strong portfolio of projects that cover these key domains like Banking, Insurance, Telecom, Online Retail & E-commerce
3. Mentorship & Guidance from industry experts play a crucial role
Usually, in self-paced learning models, students are heavily invested in just understanding concepts and definitions. Mentorship and guidance from industry experts and practitioners can help in applying machine learning end-to-end on problems but also in interview preparation and job support. In addition to this, online courses should provide an experiential learning environment along with a good ecosystem, peer network that can help students connect outside class hours for doubt resolution, guidance and networking opportunities.
Tip: Success of any online course is heavily tied to time invested in self-study and practice outside regular class hours. In the case of online machine learning courses, regular assignments and projects give one a strong practical exposure that can help them face job interviews with confidence.
4. What will you do after the machine learning course?
So you finished your ML course and would now like to narrow down on options based on your particular interest or job opportunities that abound in that area. There are specialized fields in Machine Learning — Computer Vision, Natural Language Processing (NLP), Reinforcement Learning or Recommender Systems. Given your interests, you must have chosen your projects in one or more of these areas — but while doing projects, also focus on their usability and how they can be leveraged by a prospective employer. Based on your specialization you can become a Computer Vision Engineer, NLP Specialist, Machine Learning Engineer and Algorithm Engineer. With the popularity of facial recognition software, speech to text solutions increasing, recruiters increasingly demand talent that has experience in these areas.
Tip: During your learning journey, it is advisable to have a roadmap to map your learning outcomes and also gauge your capabilities. This can prove valuable when choosing a specific field in ML as a career pathway.
5. Before you invest in a low-priced MOOC, think hard about your learning outcome
It can be overwhelming to choose from the sheer number of online resources available. But the biggest factor you should weigh in before signing up for the machine learning course is how well it fits in your career journey. Also, discover why you want to learn machine learning and whether it fits into your overall career objective. Programmers and engineers may already be familiar with the core concepts, but a novice will lack any understanding of algorithms, processes, problems and tools.
Tip: Machine Learning is a buzzing field that is inspiring professionals to push boundaries and develop cutting-edge solutions. But you should pursue an online course only if it is aligned with your career objectives.
Investing in online machine learning courses is a career-defining moments for professionals who are looking to add critical skills to their resume. Not only is it important to choose wisely, depending on the relevancy of curriculum, teaching pedagogy along with mentorship can help guide ML aspirants to the next stage of their career.