A complete daily plan for studying to become a machine learning engineer.
Inspired by Coding Interview University.
This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.
My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the Math for the beginner. This approach is unconventional because it’s the top-down and results-first approach designed for software engineers.
Please, feel free to make any contributions you feel will make it better.
I'm following this plan to prepare for my near-future job: Machine learning engineer. I've been building native mobile applications (Android/iOS/Blackberry) since 2011. I have a Software Engineering degree, not a Computer Science degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university. Think about my interest in machine learning: - Can I learn and get a job in Machine Learning without studying CS Master and PhD? - "You can, but it is far more difficult than when I got into the field." Drac Smith - How do I get a job in Machine Learning as a software programmer who self-studies Machine Learning, but never has a chance to use it at work? - "I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems." Ross C. Taylor - What skills are needed for machine learning jobs? - "First, you need to have a decent CS/Math background. ML is an advanced topic so most textbooks assume that you have that background. Second, machine learning is a very general topic with many sub-specialties requiring unique skills. You may want to browse the curriculum of an MS program in Machine Learning to see the course, curriculum and textbook." Uri - "Probability, distributed computing, and Statistics." Hydrangea
I find myself in times of trouble.
AFAIK, There are two sides to machine learning: - Practical Machine Learning: This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill-defined questions. It’s the mess of reality. - Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
I think the best way for practice-focused methodology is something like 'practice — learning — practice', that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly.
It's a long plan. It's going to take me years. If you are familiar with a lot of this already it will take you a lot less time.
Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using Github's special markdown flavor, including tasks lists to check progress.
I'm a Vietnamese Software Engineer who is really passionate and wants to work in the USA.
How much did I work during this plan? Roughly 4 hours/night after a long, hard day at work.
I'm on the journey.
| | |:---:| | USA as heck |
I get discouraged from books and courses that tell me as soon as I open them that multivariate calculus, inferential statistics and linear algebra are prerequisites. I still don’t know how to get started…
Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.
This short section consists of prerequisites/interesting info I wanted to learn before getting started on the daily plan.
Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day.
Each day I take one subject from the list below, read it cover to cover, take notes, do the exercises and write an implementation in Python or R.