How To Become A Machine Learning Engineer

We will walk you through all aspects of machine learning from simple linear regressions to the latest neural networks, and you will learn not only how to use them but also how to build them from scratch.

Big part of this path is oriented on Computer Vision(CV), because it’s the fastest way to get global knowledge, and experience from CV can be simply transferred to any ML area.

We will use TensorFlow as an ML framework, as it is the most promising and production ready.

Learning will be better if you work on theoretical and practical materials at the same time to get practical experience on learned material.

Also if you want to compete with other people solving real life problems I would recommend you register on Kaggle, and it could be a good addition to your resume.

Requirement:Python. You may not be a guru, basic knowledge will do. For anything else there are manuals)

1. Courses:

1.1 Practical Machine Learning by Johns Hopkins University

1.2 Machine Learning by Stanford UniversityThese first two will teach you basic things about Data Science and machine learning and will prepare you for real hard stuff)

1.3 CS231n: Convolutional Neural Networks for Visual Recognition 2017 (2016)That’s where the party’s starting, it’s one of the best courses that you can find on the Internet about ML & CV. It will not only show you how deep is the rabbit hole, but also will give you good base for further investigation.

1.4* Deep Learning by GoogleOptional course. You can take only practical part from it.

1.5* CS224d: Deep Learning for Natural Language ProcessingOptional course for those who want to work with Natural Language Processing. And yeah, it is also great)

1.6* Deep Learning bookGood handbook which covers many aspects of ML

2. Practical part:

This list consist of many tutorials and projects, that you should try, understand how they work, and think how you can improve them. This list is created to increase your expertise and interest in ML, so don’t be afraid if some of the tasks are hard for you, you can return to them when you are ready.

2.1. Simple practical course on Tensorflow from Kadenze2.1. Tensorflow cookbook2.2. Tensorflow-101 tutorial set

2.3. Fast Style Transfer NetworkThis will show how you can use neural network to transfer styles from famous paintings to any photo.

2.4 Image segmentation

2.5 Object detection with SSDOne of the fastest (and also simpler) models for object detection.

2.6 Fast Mask RCNN for object detection and segmentation

2.7 Reinforcement learningVery useful thing especially if you want to build a robot or next Dota AI)

2.8. Magenta project from Google Brain teamProject that aims for creating compelling art and music with the help of neural networks. And results are remarkable.

2.9 Deep Bilateral Learning for Real-Time Image EnhancementNew awesome algorithm of photo enhancement from Google

2.10 Self driving-car projectWant to make you car fully automatic? — that’s a good starting point)

3. FAQ

**What to do if you are stuck?**First you must understand that ML it not something that 100% precise — most of the cases are just a good guess and tons of tuning iterations. So to come up with some personal idea is very hard in most cases, because of the price of model training. So don’t try to figure out solution by yourself — search for papers, projects, people that can help you. The faster you get experience, the better.Some websites that can help you: http://www.gitxiv.com/, https://arxiv.org/, https://stackoverflow.com

Why papers do not fully cover the problem or wrong in some places?It’s pity to say, but not all tech guys want to open their work on public, but all of them need publications to get grants and fame. So some of them publish just a part of the material, or make mistakes in formulas. That’s why always better to search for the code and not for the paper. Better think about papers like evidence that this problem was solved.

**Where can I find fresh materials?**I use this two websites http://www.gitxiv.com/, https://arxiv.org/. First one finds not only a paper, but also a code for it, so it is more practical.

**Should I use Cloud or PC/Laptop for computing?**Cloud is the best fit for intense computing of production models. For learning and tests it is much cheaper to use PC/Laptop with CUDA graphic card. For example, I train all models on my laptop with GTX GeForce 960M with 690 CUDA Cores.Of course, if you have grants for cloud or free money for it, you can use it.

**How can I improve tuning of hyperparameters of the models?**The main problem in training is time. You can’t just sit and watch the training stats. For this reason I would recommend you to use Grid Search. Basically, just create the sets of hyperparameters and model architecture and then run them one after another in stream, saving results. Thus you can run training at night and compare results the next day, finding the most promising one.You can look how this was done in sklearn library: http://scikit-learn.org/stable/modules/grid_search.html

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