Machine Learning as a domain is currently the hottest domain in the market. Big tech companies like Google, Facebook, Microsoft, Amazon, and Apple are investing heavily in it. The market is huge and the salaries are much higher than those who are in traditional Web Development or App Development.
The reason for the higher salaries of Machine Learning Engineers is simply due to the fact that there is a huge shortage of outstanding Machine Learning Engineers. While there are thousands of online resources that claim to help you become a Data Scientist, most of them are focused on the very basics. Today, in order to differentiate yourself, you not only have to learn the basics but also develop a deep specialization in the domain.
I am enlisting here some of the best resources for you to get started with Machine Learning and build a successful career in it. Let's begin?
- Prerequisites: Machine Learning requires not only basic programming knowledge, but also you should be comfortable with the concepts taught in a typical Linear Algebra and a Calculus course. Why? Because in Machine Learning, the data is represented in the form of matrices. And therefore, you should be comfortable with the most common matrix operations like addition, subtraction, multiplication, etc. Also, some algorithms require knowledge of Eigenvalues and Eigenvectors - yeah, the scariest part! Talking about Calculus, you'd be doing some fancy differentiation operations on matrices and so, you should have a solid understanding of core Calculus fundamentals. So, if you have not taken a course on Linear Algebra and Calculus, better do it before you start with ML or else, the best you'd learn is from sklearn.linear_model import linearregression.
- Phase 1: as a beginner in Machine Learning, you should focus on building a solid foundation in the most basic Machine Learning Algorithms. The most important algorithms include Linear Regression, Logistic Regression, Support Vector Machines, and Neural Networks. All of these algorithms are covered in the excellent course by Andrew Ng on Coursera. The upside of the course is that it teaches you the fundamentals in a highly intuitive way. The downside is that it uses Octave as a programming language. Octave isn't a Machine Learning industry standard. Python is.
- Phase 2: once you are through with the basics of Machine Learning algorithms, your focus should be on the implementation. Remember, knowing the theory is good. Knowing the theory and being able to implement it is better. Udacity has a great course on Machine Learning. The course focuses on the implementation of various Machine Learning algorithms in Python.
- Phase 3: now that you are comfortable with the basic algorithms as well as their implementation, won't it be cool to implement some projects which you can showcase on your resume? Eduonix has a fantastic course on Learn Machine Learning by Building Projects. The course focuses on writing actual code and building some great projects which you can put as a part of your resume. You can talk about these courses in your job interviews. The impact of talking about a project would be far more than the impact you'd create by talking about just the vanilla courses. Moreover, you can showcase these projects on your personal web-page as well.
To conclude, follow a step-by-step methodology to learn the domain of Machine Learning. Do not skip the steps. Follow the sequence in order to get the best output and you surely would succeed.
Here are some great additional resources:
- How do I learn Machine Learning?
- What are the best online courses for Machine Learning?
- Tensorflow for Machine Learning
- How would I attempt to learn Machine Learning?
- How did Aman Goel start his career in Machine Learning?
All the best!