How to Break into Machine Learning
- Start Small and Learn on the Job: Any candidate wishing to start his or her career in Machine Learning can adopt the strategy of joining a Machine Learning team or company as an engineer. In this way, you can have a hands-on learning in the job. There are some companies which invest in your learning capabilities and teach you.
- Machine Learning Certification: Some companies require you to have prior experience in Machine Learning. Generally, only a fundamental and working knowledge is sufficient. Machine Learning certification of some sort is always helpful to have. Familiarity with basic data structures, algorithms, etc will help you smoothly enter this field.
- Invest in Yourself: Self-learning at home is also very useful. There are some short-term programs where they teach you applied Machine Learning. Things like how to apply Machine Learning in the real world and the use of the associated tools along with the basic concepts of Machine Learning are taught.
- Employ Online Resources: This requires commitment and time which people may be unable to devote. In this case, there are materials available online. Andrew Banks on Coursera or books like Learning From Data are very useful. Build some basic models by yourself by taking up some real-world data sets and using some open source tools.
Helpful Machine Learning Certifications and Education
A proper degree or a Masters or PhD in machine Learning signifies your understanding of the subject. To land a machine-learning job the interviewer is rarely bothered about your certificates. Instead, they care about whether you know your subject and its core concepts.
Instead of chasing certificates, invest time in brushing up your fundamentals.
Best Job Search Strategy to Adopt
- Develop Expertise – Have some level of expertise in the subject of Machine Learning.
- Develop Contacts – Nurture some contacts who could connect you with appropriate and relevant job openings. Go on sites like LinkedIn and give a shout out to all your connections.
Important Tip to Make Your Machine Learning Resume Stand Out
Be thorough in what you know and do well anything that you undertake.
- If you are writing code, do it well with a genuine interest of building a great system.
- If you are managing a project where your technical contribution is less, then make sure you are doing that job right.
- If you are taking any small learning courses make sure you do it to understand and learn and not for the sake of certification only.
Do not put information on your resume with the sole intention of making your resume stand out. When you cannot back up your information with the right knowledge and answers it looks superficial and does not deceive anybody.
Instead, be upfront about your actual skillsets and qualification and convince the company to give you chance in Machine Learning based on your overall general aptitude.
Be ready to back all the skills highlighted on your Machine Learning Resume.
Important Skills of a Machine Learning Engineer
- Problem Formulation Skill: Machine Learning has different paradigms with which to solve problems. Supervised learning, unsupervised learning and reinforcement learning are some different techniques with which you can formulate a problem. Each of these paradigms work well with certain kind of problems. This skill comes with experience. Problem formulation is one of the key skills in solving a Machine Learning problem.
- Understanding Algorithms: Understand different algorithms like logistic regression, deep neural networks, etc. Each algorithm is better suited for certain kind of problem. Even when you have chosen a particular algorithm understand the nitty-gritty of it to suit best your particular problem. Know your fundamentals and algorithms.
- Coding: Ability to write code well is a very key skill to have. Being able to write code and experiment and deliver things end to end is very important. This implementation skill is much sought after.
Blogs to Follow to keep Abreast of Data Science Development
- Blogs of most big companies doing AI research – Google, LinkedIn, Facebook, etc
- Read associated papers which give an insight into some of the more cutting-edge work
- Read papers in the reference sections of the main paper. This gives deep and broad knowledge of recent advancements.
Importance of Math in Machine Learning
The field of math has become very important in Machine Learning. Linear algebra in Machine Learning is basically dependent on how you operate on matrices. Most algorithms are optimized based on matrix manipulation and linear algebra comes in very handy.
Optimization techniques, differential calculus is also useful because you need to use a bunch of differentiation in your algorithms.
Even if you do not a background of maths, you can still manage If you have very good intuitive understanding of the algorithms and are able to fill in the blocks mentally with some mathematical equations and the tools and technologies available.
Best Problem Formulation in Machine Learning
- Practice till you perfect
- Read blogs
- Learn from similar case studies
- Start-ups doing deep technical work provide a good insight
- Study how big companies handle these problems and the approach adopted by them
- Get into the mainstream and plunge into the work and start doing and solving problems to learn
- Take random data sets and try to formulate the same as a Machine Learning problem.
All the above steps will give you clarity from which you can learn and improve.