The top-down approach is about moving from a high-level solution to the nitty-gritty of an applied method. This is in stark contrast to the traditional pedagogical way of learning, where you first learn the theoretical principles, and then practice.
Conversely, a top-to-bottom approach would not require investing a significant amount of time in studying the theory before moving on to the practical aspects. Instead, it's about getting the proper tools (usually the libraries of your favorite programming language) and working on it before learning how everything works under the hood. Your ultimate goal is to solve a concrete task using the high-level tools and methods available to you.
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This approach is working great for many learners who decide against a traditional college degree. You are first exposed to what is Applied Machine Learning, excited about opportunities in this field, familiar with programming principles and the core libraries used. Once you go through this process, becoming more confident and motivated, only then do you get into the theory side and learn the mathematical concepts behind algorithms.
A great example of top-down in action is the The Machine Learning Crash Course created by Google. It gives you a hands-on approach to learning. Using real-world case scenarios, visualization and a ton of exercises, learners can enjoy the learning process without getting overwhelmed by complex mathematical concepts. It’s fast, it’s fun, it’s efficient and, most importantly, it’s a great way for self-taught learners to get familiar with machine learning.
Bottom-up approach to Machine Learning education
Alternatively, if you have the time to invest in studying, the necessary financial resources, patience, and the willingness to learn everything from the very basics, you should consider a bottom-up approach. This will help you build a strong foundation that will ensure a smooth immersion in the ML workforce.
Bottom-up is a traditional way of learning. First you learn the theoretical part, and only then gradually move on to the practical side. In the context of machine learning, this means:
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Possessing a solid mathematical background (including calculus, discrete mathematics, linear algebra, probability theory and statistics);
Gain proficiency in programming (typically, in Python);
Learning the fundamentals of backend/software development;
understanding databases, data querying and processing;
Mastering the principles of data scraping, cleaning, manipulation and exploration;
Only then can you move on to some kind of "introduction to machine learning" course that will teach you the basics of machine learning algorithms.
Does it take time? Yes, and many more. It will take at least 3 years.
But do you need a degree to get a job offer and start a career in machine learning? No, you can get offered a job without a college degree.
So what are the benefits of bottom-up ML education? Will you gain deeper knowledge? Sharpen the skills needed to accelerate your future career? Absolutely yes! Your knowledge and skills will be well rounded and complete. This will give a boost to your career.
Will the basics you learn now help you understand future advancements in ML? Yes! The machine learning field is changing rapidly. The solid foundation gained from a college education will help you stay on the edge of progress.
It can be very difficult to self-design a full time course that will cover all the aspects required to enter the field of machine learning. Without the proper background and the support of experienced staff, this may be impossible for some. This is why most learners who choose a bottom-up approach to learning stick to a traditional educational path and earn a degree.
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