Machine Learning

“A breakthrough in machine learning would be worth ten Microsoft” -Bill Gates

But what is machine learning and how it’s going to impact the global market? Let’s dig deep into this technology for the answers.

Along with the technological advancement and data generation, the exponential growth of machine learning is anticipated. The growth from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022 is expected in the global machine learning market. The Compound Annual Growth Rate CAGR) of 44.1% is expected.

What is machine learning?

The way humans learn facts and figures to develop and evolve our knowledge for subsequent application of that gain into productivity, machines can also be programmed to do the same with a similar approach.

Artificial intelligence encompasses the ability of a machine to learn and take independent decisions based on the algorithm and fed statistical data.

Machine learning is the component of Artificial intelligence that provides systems the ability to learn from data, patterns, algorithms, and experiences. Machine learning comprises the programs that allow data access and automated learning.

Machine learning simply is a component that trains the machine on how to learn.

Methods of Machine Learning:

A. Supervised learning

Task-driven
Uses labelled data for learning.
A training datasets providing the basic idea of problem and solution is designated to the algorithm. In the end, the algorithm gets the idea about the correlation between inputs and output and figures out how the data functions.

B. Unsupervised learning

Data-Driven
Un-labelled data is used.
The algorithm learns data functioning and the relationship between inputs and outputs in an abstract manner. Human efforts are not required to load the data.
It makes the algorithm to be versatile that can adapt to dynamic data with help of transitional hidden structures.
Offers post-placement development of the algorithm.

C. Reinforcement learning

Learning from errors
The algorithm learns by trial- and- error method. The favorable output gets reinforced whereas un- favorable are discouraged.
Reward feedback allows the algorithm to learn what is best and this is known as a reinforcement signal.

D. Semi-supervised machine learning algorithms

Uses both labelled and unlabelled data, usually, more of unlabelled data is
Helps the system to improve learning accuracy.
It is used when some labelled data is acquired by the algorithm else, unlabelled data doesn’t require supplementary sources to learn.

Applications of machine learning:

Quantitative enlargement of data along with growing complexity needs solutions for quick, reliable, and automated analysis of data. Machine learning can be a preferred tool for the task. Let’s jump to other applications of machine learning in a variety of niches.

1. Application in the healthcare sector

Diagnosis of a disease.
Radiographic image analysis
Clinical trial research
Drug manufacturing
Facial reconstruction

2. Application in Banking and finance services

Pattern recognition to create strategies for investment prediction in capital market
Digital assistance
Campaign management
Risk and fraud management
Customer segmentation

3. Applications in government setup and Defense

Operational analytics
Threat intelligence
Autonomous defense system

4. Transportation

Pattern analysis for routes
Potential problem analysis

5. Other applications include

Customer-specific pricing
Demand forecasting
Revenue estimation
Network security and optimization
Supply chain management

Advantages of machine learning:

Automated learning
Experience-based enhancement
Quantitatively enriched and dynamic large data processed can be in minutes.

Disadvantages of machine learning:

Error susceptibility
Massive data is required to train machines
Several resources and time are required.