When it comes to Machine Learning, there is a ton of information and resources available all across the web. And all of this, ages pretty quickly. To add to this, is a lot of technical talk going on and this is why anyone wanting to start off with Machine Learning can feel lost. It is simply not possible for anyone to understand what Machine Learning is about without going through the grind themselves. One must spend hours trying to understand the nuances of feature engineering, and the impact and importance it can have on models. In this article, we hope to give our readers the answer to the problem – explaining the learning path for an aspiring Machine Learning Expert.
Step A – Learning the basics of Python and/or R: There are several languages that have machine learning capabilities. As of now, Python and R are the most widely used languages and both have a pretty good support community. Even before stepping into the Machine Learning world, it is recommended for one to pick one of the two or even both the languages as a path. Focus on understanding the basics, data structure and the libraries of the languages. Some pick one of the two. But the advantage of learning both is being able to switch between the two as and when the need arises.
Step B – Learn inferential statistics and basic descriptive statistics: The statistics you may have leant is school/college maybe forgotten. It’s a good time for you to refresh and brush up on basics. If you want to get into serious machine learning development, you must understand inferential and descriptive statistics.
Step C – Data Cleaning/exploration/preparation: What makes a good machine learning professional stand out from the average lot is the quality of data cleaning and feature engineering that happens on any kind of original data. The more time one spends here learning, the stronger the foundation will be. This step helps build a structure around it. Both R and Python have their own data exploration methods.
Step D – Introduction to Machine Learning: It is only after 3 preliminary steps do your doors to Machine Learning actually open. In this step, you will have to learn basic algorithms, introduction to advanced topics like neural networks, recommendation system, and application of machine learning in huge databases with use of Map Reduce. You will be expected to go through mathematics and also theories behind Machine Learning like VC dimension. This is where you will use your programming knowledge. This is where you also start learning about the application of packages and libraries available in Python/R. Make sure to test out your skills and knowledge with assignments and projects which includes exploring the packages R and Python have to offer.
Step E- Advanced Machine Learning: Once you have learnt most of the machine learning techniques, it is time to start exploring advanced machine learning techniques in order to understand the different structures of data like Machine Learning with Big Data and Deep Learning. Here, you learn more about neural networks, pattern recognition and text mining using R and Python. Ensemble Modeling, another part of deep learning is something that can add power to models and needs learning. Data is growing at an exponential rate and drawing insights from it, identifying patterns in the available data set form the basics of Machine Learning. Some applications of Machine Learning algorithms like fraud detection, spam detection and web document classifications are examples of Machine Learning applications. This is also the stage where students get to learn about text mining and databases. This is where you learn about cleaning text data and building models on it.
