With evolving technologies, machines have also started to become smart. The idea of inducing intelligence in machines has drastically changed the perspective of growing industries. Our everyday world runs on machines built with artificial intelligence which gives machines the ability to learn without explicit programming. The algorithm plays a key role in making the machine learn on its own with greater computational decisions and accuracy.

History of ML:

Earlier, when the computers were still evolving, it took a very long time for computing basic calculations and other arithmetic operations. When Alan Turing stated his theory about the Turing machine, the thought of inducing intelligence into the machine has arisen. Ever since then, there has been a lot of research and development in this field. In 2014, AI experts thought that it might take several years for the computers to take over humans, but DeepMind proved them wrong by beating the world’s best Go player.

What is ML?:

The study of mathematics, algorithms and computational abilities of a machine to produce productive decisions together computes machine learning. Here, the computer performs tasks which are trained by humans daily where more amount of information is fed into the algorithms to produce better output results.
This field is closely related to optimization of the algorithm with more amount of results from different forms of learning techniques such as supervised, unsupervised, semi-supervised and reinforcement.

Supervised Learning:
ML supervisd
Supervised Learning

Here, the model is trained with a different set of training examples with the unique features of the images being labeled explicitly by humans. Without the intervention of explicit human training, the algorithm cannot proceed to further steps.

The information is grouped according to similarities, patterns and differences after the model are trained. Some of the examples of Supervised learning algorithms are Classification and Regression. In classification, an output variable is a number which is assigned to different categories. In regression, the output variable is assigned with a specific value or weight for a given category.

Unsupervised Learning:
Unsupervised Learning

Here, the model learns on its own without any explicit guidance and group the information based on similarities, patterns, and differences. Here, the training is done based on generative learning models which use a retrieval-based approach. Here, the unlabeled, categorized data is taken and the model extracts different features from the data based on the grouped information.

Different algorithms that are used in unsupervised learning are Association and Clustering. In Clustering, the data is grouped inherently by repetitive behavior of the user and the outputs are generated accordingly. In association, a set of rules are generated based on the user’s traits and behaviors and these rules are again used to classify the unknown data.

Semi-supervised Learning:

The disadvantages of supervised and unsupervised learning are covered up in semi-supervised learning. While in supervised learning, it is a tedious task to hire a data engineer to train a large data set, on the other hand, the application spectrum is limited in unsupervised learning. A semi-supervised algorithm assumes continuity assumption, cluster assumption, and manifold assumption. Some of the practical applications of semi-supervised learning are speech analysis, internet content classification, and protein sequence classification.

Reinforcement Learning:

Reinforcement learning is about how the decisions are made sequentially over a given set of inputs. It generates the next set of outputs from previous inputs which is used to give the labels to sequences. Some of the examples are AlphaGo, chess games where machines learn from the inputs.

Its role and payout:

These days, Machine learning has its applications in almost all industries. Its uses are much needed to automate most of the necessities across a variety of industries. While in Manufacturing, it is used for condition monitoring and predictive maintenance, and retain it is used for cross channel marketing and selling goods based on recommended inputs.

It also has a wide range of applications in healthcare and life-sciences were many numbers of diseases are identified and presented with the most suitable solutions. In the travel industry, it is very difficult to monitor and update the prices according to the money value in the market.

Hence, various machine learning tools are used to monitor and maintain the prices of different packages and services offered. In a financial company, machine learning is used for risk analysis and regulation of money. In Automobiles and other industries, ML tools are used for supply chain optimization.


An average pay package for a Machine Learning Engineer is dependent mostly on the years of experience and also the efficiency of solving a particular problem with the most optimized solution. Different companies offer different pay packages for various roles in Machine Learning. These roles are:

I. Machine Learning Engineer
II. Data Analyst
III. Data Scientist
IV. Qualitative Expert
V. Domain Expert

Machine Learning is the most demanding field these days. These skills can be learned with a laptop at home with minimum requirements. Many educators like Siraj Raval have started online free courses to spread amazing innovations and technologies related to Artificial Intelligence. One must have strong potential and zeal to learn and succeed in this field.