A Guide to learn Machine Learning

So, we were trying figure out how to pen the introduction for this blog when we realized that this pompous field of machine learning does not need much introduction. But as this age old custom of beginning blogs with introductions can never seize to exist, let us give you a brief description of what machine learning is!

Firstly, machine learning is not artificial intelligence. Artificial Intelligence is the phenomenal revolution in the world of technology and machine learning is the approach that aids and supports this phenomenon. Machine learning is the procedure that is adopted to inculcate intelligence into machines and thus promote Ai development.

Now, assuming that you are a complete beginner, we have put forth a detailed description on how to get well-equipped before beginning your learning journey, how to build the foundations and dig into the core concepts of machine learning and how to get industrial experience even before you become a part of the workforce.

So let’s begin!

Step 1: Get your weapons ready!
Yep, we are talking about prerequisites. It is important for a beginner to know, understand, learn and set a strong foundation in the prerequisites before getting to the core concepts of machine learning. Here, we have listed out all the areas that one needs to at the least be familiar with.

1. Programming:
The model building in machine learning requires one to have a good knowledge of programming languages such as Java and C++. But the most important languages that will aid a machine learning engineer in model building as well as data management and data visualization are R and Python. Hence one can simply get familiar with the former languages but the latter ones need to be well-versed with.

2. Database Management:
Machine learning models are trained, built and tested using data. Hence database management skills are highly essential to achieve accuracy. Here, the various tools that come into picture here are SQL, NoSQL and Excel as well.

3. Mathematics:
Mathematics is used in machine learning in two ways: One is in the usage and building of algorithms and models and the second is to make predictions and interpretations. Linear Algebra is used for the first purpose where as Calculus and Probability are used for the latter.

4. Statistics:
Statistics can provide information about the data and also about the results retrieved. Knowledge in statistics is also important to carry on model evaluation.
Hence, only if you know your stats will you be able to assess if you have your data and your model in the right form.

This completes the list of prerequisites that one needs to get familiar with in the first step of their learning journey.

Now, let’s move on to to next step!

Step 2: The waters have been tested, so dive deeper into the concepts of Machine Learning!

Understanding the core machine learning concepts is not a very easy task, whether it is Regression, Classification, clustering techniques or reinforcement learning. Hence it is important to choose an appropriate medium to learn all the concepts. We advice you to find a medium of learning that will brush you up with the theory and also give you enough practical experience on the implementation.

Another point that one needs to remember is that learning data management is also crucial as one needs to be able to handle the data that has been handed over to them before utilizing it to build the model.
Hence choose a program which is absolute with all the data management techniques as well as machine learning techniques.

Step 3: Learn to apply your skill before you apply for your job!
What recruiters look for in their prospective employees is their ability to apply skill in real time scenarios. Now, how does a beginner with no prior work experience prove this?

This is easier than you think.

Working on real-time projects is one important step that one should take to, first, assess and also improve their skill at a real-time level. Once enough confidence is established in this area, they can go one step further by signing up for machine learning challenges. These challenges are a great way to up one’s game.

Once you put your foot in the third step and believe that you are all set, it shall be pretty easy for you to prove that you have a better game than the others!
This brings us to the end of the guide.

Now that you have your complete guide to learn machine learning, we hope you have a smooth ride in your learning journey.
Good Luck!

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Dedeepya Bypuneedi

A Computer Science graduate by education and a content writer by profession. Currently fulfilling her zeal to write by putting pen to paper every time she comes across something that is interesting enough to let the world know