A Roadmap to Develop into a Machine Learning Engineer


As a Machine Learning Engineer, I remember feeling uncertain concerning the path ahead once I first started off. Back then, I had no concept that the title “Machine Learning Engineer” even existed once I was working on my Text-to-Speech study for my Bachelor’s thesis. But over time, it has turn out to be a preferred and sought-after title, particularly amongst computer science students. With this in mind, I made a decision to create a roadmap specifically for those all for pursuing a profession on this field. The roadmap starts with the fundamentals and steadily builds up skills, one layer at a time. It’s price noting that in today’s world and job market, having a Master’s degree is sort of a requirement to land a job on this sector, as it could possibly make an incredible difference.

  1. All of it starts with . Linear Algebra, Statistics and Probability are our first requirements before jumping into ML algorithms. It’s necessary to have a robust foundation in these areas since Machine Learning is predicated on statistical and probabilistic models.

2. Second skill set before you begin jumping into ML algorithms is programming. Machine learning involves a whole lot of mathematical and statistical concepts, so it’s essential to have a superb understanding of information structures and algorithms.

3. You’re trusting your math-related skills then we are able to begin to discuss Machine Learning and all of it starts with basic Machine Learning Algorithms. Furthermore, you’ll be able to start to accumulate skills in libraries like Pandas, scikit-learn, and Numpy at this point.

Also to any extent further, Kaggle and Huggingface are where you’re going to spend time constructing an understanding deeper and practising what you learn.

4.a In case your path lies in NLP

4.b In case your path lies in Computer Vision first you furthermore may have to program using opencv. It’s a vision library that’s easy to learn and use.

5. Finally, let’s not ignore deep learning algorithms. While it could possibly be combined with item 4, I prefer to maintain it as a separate item to emphasise its significance in modern machine-learning applications.

6. While learning is essential, it is usually obligatory to place that knowledge into practice and showcase your abilities to extend your probabilities of securing a job. With that in mind, I’d strongly advise participating in a contest on Kaggle and taking a task by joining Huggingface’s Discord.

7. There’s one thing that it is best to proceed even after you land a job following the most recent research in your field. And imagine it or not, this will not be easy to maintain up with the literature. There are a few places I personally used to follow:

  1. Papers with Code
  2. ArXiv
  3. I also profit from Huggingface’s blog and task section: https://huggingface.co/blog?tag=research
  4. One other resource is from CohereAI for me to follow: Best NLP Research Papers in 2022
  1. There’s a superb abstract roadmap to becoming an AI expert is as follows.
  2. If you happen to’re in search of The Top MLE Interview Questions. This blog post remains to be up-to-date.
  3. You will discover cheatsheets from Stanford and MIT in various languages including Turkish as follows: https://stanford.edu/~shervine/teaching/
  4. The Youtube channel StatQuest by Josh Kramer explains concepts in a highly simplified way. I discovered it hard to pay attention by myself however it’s different for everyone so I’m adding to this list.
  5. One other Youtube 3blue1brown it’s not limited with only stats, math, and ML world but all, that entertains the channel owner 🙂

Because of Güldeniz Bektaş, Muhammet Nusret Özateş, and Yavuz Kömeçoğlu for his or her contributions to this blog post.


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