Introduction to Machine Learning

What is Machine learning?

The meaning of Machin Learning is machine learns itself with its experience. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. ML improve the performance of a computer program using sample data or past experience. 

We have to create a model and then we have to train the model which will provide the prediction on the basis of machine learning algorithms.

For eg: A robot learns with its past experience and improve itself.

 

Classification of Machine Learnings algorithms:

Supervised Learning:

Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. 

It is defined by its use of labelled datasets to train algorithms that to classify data or predict outcomes accurately.

For eg:  Supposed to identify the fruits in the basket we must have prior knowledge about the fruit's colour, shape and size of the fruit.

There are some important supervised learning algorithm:

·       Regression

·       Logistic Regression

·       Classification

·       Naive Bayes Classifiers

·       K-NN (k nearest neighbors)

·       Decision Trees

·       Support Vector Machine

Advantages:-

·       Supervised learning allows collecting data and produces data output from previous experiences.

·       Helps to optimize performance criteria with the help of experience.

·       Supervised machine learning helps to solve various types of real-world computation problems.

Disadvantages:-

·       Classifying big data can be challenging.

·       Training for supervised learning needs a lot of computation time. So, it requires a lot of time.

Unsupervised Learning:

It uses machine learning algorithms to analyse and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data.

Unsupervised learning algorithms include clustering, anomaly detection, neural networks, etc.

it resembles the methods humans use to figure out that certain objects or events are from the same class, such as by observing the degree of similarity between objects. Some recommendation systems that you find on the web in the form of marketing automation are based on this type of learning.

For eg: Suppose there are some animals like Cats and Dogs. There is a new born baby who have no idea about which animal is dog or cat. On the basis of pattern and similarities he can categorize these features of Dog and these features of cat. 

Unsupervised learning is classified into two categories of algorithms: 

1. Clustering   2. Association

Unsupervised Learning Algorithms:-

1.      Hierarchical clustering

2.      K-means clustering

3.      Principal Component Analysis

4.      Singular Value Decomposition

5.      Independent Component Analysis

Reinforcement learning:

Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. In the absence of a training dataset, it is bound to learn from its experience. 

 

Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.
Q-Learning is a reinforcement machine learning algorithm.

For eg: in chess game, a best possible move is considered.