About Machine Learning

Machine Learning in the application of artificial intelligence. It is the process of using mathematical models of data to help a computer learn without direct instructions. It allows software applications to become more accurate at predicting outcomes.

Machine Learning algorithms use historical data as input to predict new output values.

A machine has the ability to learn if it can improve its performance by gaining more data.

The term machine learning was first introduced by Arthur Samuel in the year 1959.

There are four types of Machine Learning :

Supervised Learning : This is also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.
Unsupervised Learning : Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a compact internal representation of its world and then generate imaginative content from it.
Semi-supervised Learning : Semi-supervised machine learning is a combination of supervised and unsupervised learning. It uses a small amount of labeled data and a large amount of unlabeled data, which provides the benefits of both unsupervised and supervised learning.
Reinforcement Learning : Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

Find your answer on Machine Learning

1. How are Artificial Intelligence and Machine Learning related?

Artificial Intelligence (AI) started as a subfield of computer science with the focus on solving tasks that humans can but computers can’t do (for instance, image recognition). AI can be approached in many ways, for example, writing a computer program that implements a set of rules devised by domain experts. Now, hand-crafting rules can be very laborious and time consuming.

2. What are the origins of machine learning?

Machine learning was first conceived from the mathematical modeling of neural networks. A paper by logician Walter Pitts and neuroscientist Warren McCulloch, published in 1943, attempted to mathematically map out thought processes and decision making in human cognition.

3. How do I learn machine learning on my own?

If you are intends to learn machine learning on their own, they need to be relentless and have to start thinking about them round the clock. Reading books, articles, and blogs, and keeping books related to ML always by one’s side to get the basics right, is also essential.