How Do I Learn Machine learning Algorithms In A Simple Way?

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What is Machine Learning?

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

Machine Learning as a domain/Technology is currently the hottest domain in the market. Many Big tech companies like Microsoft, Amazon, Google, Facebook, Oracle, SAP and Apple are investing heavily in it. The market in Machine Learning domain is huge and the salaries are much higher than those who are in traditional Web Development, Java Development, Full Stack Development or App Development.

Machine Learning Engineers are getting higher salaries due to the fact that there is a huge shortage of very good Machine Learning Professionals/Engineers. So you must be an Outstanding Machine Learning Engineer in order to grab those high Paying Jobs/Opportunities.

On Internet, You will find thousands of online resources that will help you to learn the basics of Machine Learning, But most of them are focused on the very basics.

But In order to differentiate yourself and to become outstanding Machine Learning Engineer you not only have to learn the basics but also develop a deep specialization in the domain.

Here is the list of some of the best resources which you can refer to get started with Machine Learning and become a outstanding and successful Machine Learning Engineer-

Prerequisites to Learn Machine Learning: 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.

Here are the recommended courses to Learn Mathematics required for Machine Learning-

Phase 1 of Learning Machine Learning: 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 of Learning Machine Learning: 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 of Learning Machine Learning: 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? Udemy 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.

Good Luck !!