Machine Learning

Machine learning:

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions,relying on patterns and inference instead.

It focuses on making predictions using computers.Machine learning algorithms are used where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

The name machine learning was coined in 1959 by Arthur Samuel,Tom M. Mitchell provided a widely quoted,more formal definition of the algorithms studied in the machine learning field:

"A computer program is said to learn from experience E with respect to some class of tasks T and 

performance measure P if its performance at tasks in T, as measured by P, improves with experience E."

This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.

This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".

In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

Choosing the right algorithm is both a combination of business need, specification, experimentation and time available.

Even the most experienced data scientists cannot tell you which algorithm will perform the best before experimenting with others. 

Types :

There are four types of machine learning algorithms:

    supervised, semi-supervised, unsupervised and reinforcement.

List of Common Machine Learning Algorithms:

Here is the list of commonly used machine learning algorithms.

These algorithms can be applied to almost any data problem:

1)Linear Regression

2)Logistic Regression

3)Decision Tree

4)SVM

5)Naive Bayes

6)kNN

7)K-Means

8)Random Forest

9)Dimensionality Reduction Algorithms

10)Gradient Boosting algorithms : i) GBM ,ii) XGBoost, iii)LightGBM , iv)CatBoost , v)Robotics

The most common and popular machine learning algorithms:

1)Naïve Bayes Classifier Algorithm (Supervised Learning - Classification)

2)K Means Clustering Algorithm (Unsupervised Learning - Clustering)

3)Linear Regression (Supervised Learning/Regression)

4)Logistic Regression (Supervised learning – Classification)

5)Support Vector Machine Algorithm (Supervised Learning - Classification)

6)Artificial Neural Networks (Reinforcement Learning)

7)Decision Trees (Supervised Learning – Classification/Regression)

8)Random Forests (Supervised Learning – Classification/Regression)

9)Nearest Neighbours (Supervised Learning)