Machine Learning Algorithms | Machine Learning Tutorial

What is Algorithm ?

A tell a computer what it must do, you need a program. A program is nothing logic in some language's syntax.

A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.

This is a simple algorithm to print number from 1 to 20.

What is Machine Learning ?

Machine Learning is a type of artificial intelligence (AI) that provides computer with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

Type of Machine Learning :

Supervised Learning :

Supervised Learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make prediction. The training dataset includes input data and response values.

Let's take an example here. Say you are a teacher , and your way of teaching is,

To teach by example, i.e for every problem in their life you are providing solutions to them, this type type of learning is called supervised learning.

Unsupervised Learning :

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Example :

When your kids are taking decisions out of their own understanding , this type of learning would be Unsupervised Learning.

Reinforcement Learning :

Reinforcement Learning is an area of machine learning inspired by behaviorist psychology , concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward.

Example :

If a new situation comes up, the kid will take actions on his own i.e from his past experiences, but as a parent towards the end of an action you can tell him whether he did good or not.

Problem Solving Using Machine Learning (ML)

we take a top down approach to answer the same:

These are the 5 questions which can be answered in data science.

Classification Algorithms

Classification Algorithms are used to classify a record.

It is used for questions which can have only a limited number of answers.

For Example:

When you have only two choices, its called 2 class Classification, if you have more than 2 choices its called Multi Class Classification.

Types of Classification :

Logistic Regression :

Logistic Regression is used to predict the outcome of a dependent variable (target) which is categorical. The outcome should be discrete/ categorical such as:

Decision Tree :

Graphical representation of all the possible solutions to a decision.

Decisions are based on some conditions.

Decision made can be easily explained.

Random Forest :

Random Forest is the most used supervised machine learning algorithm for classification and regression

Random Forest are made out of decision trees.

KNN :

K Nearest Neighbour is a simple algorithm that stores all the available cases and classifies the new data or case based on a similarity measure.

Naive Bayes :

Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

Bayes' theorem is stated mathematically as the following equation:

Where A and B are events and P(B) ≠ 0

SVM :

Support Vector Machine is a discriminative classifier that is formally designed by a separative hyperplane.

It is a representation of examples as points in space that are mapped so that the points of different categories are separated by a gap as wide as possible.

Anomaly Detection Algorithms

It analyzes a certain pattern and alerts you whenever there is change in the pattern.

For Example :

In real life, your credit card company uses these anomaly detection algorithms, and flag any transaction, which is not usual as per your transaction history.

Regression Algorithms :

Regression Algorithms are used to calculate numeric values.

For example :

Clustering Algorithms :

It helps you understand the structure of a dataset.

These algorithms separates the data into groups or cluster, to ease out the interpretation of the data.

By understanding , how data is organized, you can better predict the behavior of a particular event

Reinforcement Algorithms

These algorithms were designed as to how brains of humans or rats respond to punishments and rewards, they learn from outcomes, and decide on next action.

They are good for systems which must make lot of small decisions without human guidance. For example :