The Scope And Future Of Machine Learning And Expert Tips.

The Scope And Future Of Machine Learning And Expert Tips.

sample ML ROBO

The Scope And Future Of Machine Learning And Expert Tips.

The scope and future of machine learning are rapidly growing. One who gets int the field can reach the pinnacle of success very quickly. The power and simplicity of deep learning technology are great.
According to one of the professors of Bennet University Dr. Deepak Garg, Maths is a required subject for machine learning followed by statistics. At first, he thought to be expert in deep learning technology he has to learn too many things like linear algebra, calculus, probability, and data structures and API`s. 

[must read:]Machine Learning Course and Who Can Learn.

Later in his journey to he realized that how easy it is to get into this field and kickstart his career in Machine Learning with ample amount of online courses on udemy and Udacity etc. Very little maths is required to expert these technologies. Further added that the size of the code might surprise you if compared with the classical computer science application code. And it should amaze you indeed what you have to do is, just giving the inputs data set to the computer and providing some output samples and using an ML model, that all, the computer will find the desired output for a situation.

From this, you can realize that how machine learning is helpful in business decision making and financial analysis and what not. Many startups are not hiring Deep Learning Machine Learning experts because they want to make the user search and recommendation experience much advance and more accurate in some sense.

Here is a business case I found on

A Business Case

Let us now see an interesting example published by McKinsey differentiating the two algorithms: 
Case: Understand the risk level of customers churns over a period of time for a Telecom company Data Available: Two Drivers – A & B What McKinsey shows next is an absolute delight! Just stare at the below graph to understand the difference between a statistical model and a Machine Learning algorithm.
ML example

What did you observe from the above graph?  The statistical model is all about getting a simple formulation of a frontier in a classification model problem. Here we see a non-linear boundary which to some extent separates risky people from non-risky people. But when we see the contours generated by Machine Learning algorithm, we witness that statistical modeling is no way comparable to the problem in hand to the Machine Learning algorithm. The contours of machine learning seem to capture all patterns beyond any boundaries of linearity or even continuity of the boundaries. This is what Machine Learning can do for you.
Users don't have bang their head on the desk to worry about the inner detail of the code as you might do if you are a c programmer and creating a linked list maybe. You have to merely worry about the input sets, output sets, and the appropriate machine learning model.Worried about the future so let me make it very clear to you about it Don't panic look at the below graph, that shows the growth of Machine learning.

ML graph

[must read:] Step By Step Tips On How To Learn Maths For Machine Learning

Machine learning expert will be the most searched profession of 2018. The growth will maintain its pace for next 20 years. The biggest achievement of would be in health care and space sectors.
doctor in ML

Think of it like the doctor has given the symptoms of the patient and the symptom shows is its cancer and the on some cured output samples the robots do the treatment. Yeah, it's not that easy like I am saying here tho, but one day I think it will be there. And in space, as you might be aware of the news that Google machine learning helper NASA to find another similar solar system like us far away bit might be the star wars galaxy maybe. 
death star

So the growth is there in this field but. One Abraham Lincoln said "I would take time to sharpen my AXE before cutting the tree. " yes it was a short story but I just highlighted the moral here that you have prepared your self before getting into exploring deep Learning.