Who can pursue ML?
We would say the recipe for becoming a Machine Learning (ML) Expert is focused on learning. And good Artificial Intelligence training from a reputed IT training institute. There is no single ingredient to master in ML. It’s a mix of computer science, statistics, and mathematics that can help you to be a part of the exciting field like Machine Learning. But, is the knowledge of all three mandatory to become an ML genius? Let’s dig deep and check out.
What is Machine Learning?
Machine Learning is essentially a branch of Artificial Intelligence (AI). What makes ML so interesting is the fact that it is completely contrary to what we have been doing until now.
What is traditional computing?
There is data, we set the rule and let the machine do the calculation and display is output.
What happens in ML?
There is data, we let the machines set and check the rule. The latter part happens through a learning process that the machines undergo to follow the rule and produce results.
There are three types in which machine undergoes learning:
- Supervised Learning – This involves teaching a machine with training sets which include target patterns. The data is labeled. This can be achieved through classification and regression.
- Unsupervised learning – This method involves teaching a machine with training sets that do not include target output. The data is unlabeled. Clustering techniques are used to achieve this.
- Reinforcement Learning – There is no data involved here. With the help of trial and error, the machine learns the correct thinking process through a sequence of decisions.
We recommend the Artificial Intelligence course from H2K Infosys, where the training happens through live sessions held by industry experts with training experience.
Who should pursue the ML course?
Technically, it was the computer scientists who coined the term ‘Machine Learning’. They developed the algorithms using programming languages. However, that is only a part of the game.
The computer science people cannot differentiate between the good algorithm and an algorithm that is a good fit. All they know is that the algorithm is capable of making good predictions. But, are the predictions accurate for every case?
Statisticians, on the other hand, are more adept at making interpretations. They understand why the predictions are not accurate all the time. They understand how data is collected and how sample bias can affect the results. They have a good understanding of things like not always the rows in the dataset are independent. The know which data can be discarded and which can be rounded off with minimally affecting the results.
That said, there are instances of Biology students who had made a mark in the field of ML.
So, net-net we would like to conclude that with a good understanding of high-school mathematics/statistics and basic programming skills coupled with a passion to learn can help you become a Machine Learning expert.
Check out our AI courses at www.h2kinfosys.com. We begin with brushing up your statistics skills before venturing into Python programming, moving to ML, and Deep Learning concepts in our 40-hour program.