What is Machine Learning?



DEFINITION

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

There are basically two types of machine learning algorithms/models—

(A) Supervised Machine Learning
(B) Unsupervised Machine Learning

Supervised Machine Learning

Supervised Machine Learning (Supervised ML) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.


Source: https://medium.com/@himanshuit3036/supervised-learning-methods-using-python-bb85b8c4e0b7

Following are some popular algorithms of supervised ML—

Linear Regression
Logistic Regression
K-NN Algorithm
Naive Bayes Theorem
Linear Support Vector Machines
Non-Linear Support Vector Machines
Decision Trees
Random Forest

Unsupervised Machine Learning (Supervised ML)

Unsupervised machine learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. In contrast to supervised learning that usually makes use of human-labeled data, unsupervised learning, also known as self-organization allows for modeling of probability densities over inputs.

Following figure shows the workflow of unsupervised machine learning—


Source: https://www.iotforall.com/machine-learning-crash-course-unsupervised-learning/

Following are some unsupervised ML algorithms—

(A)Clustering

hierarchical clustering
k-means
mixture models
DBSCAN

OPTICS algorithm

(B) Anomaly detection

Local Outlier Factor

(C) Neural Networks

Autoencoders
Deep Belief Nets
Hebbian Learning
Generative adversarial networks
Self-organizing map

(D) Approaches for learning latent variable models such as

Expectation–maximization algorithm (EM)
Method of moments
Blind signal separation techniques
Principal component analysis
Independent component analysis
Non-negative matrix factorization
Singular value decomposition

What is Ensemble Learning?



DEFINITION

Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.)

Following figure shows the typical workflow of ensemble learning—



Types of Ensemble Learning—

Bayes optimal classifier
Bootstrap aggregating (bagging)
Boosting
Bayesian model averaging
Bayesian model combination
Bucket of models
Stacking

About Us



Dr. AMITA SHARMA

Post Doc from Erasmus University, the Netherlands, PhD, MBA
Assistant Professor
Institute of Agri-Business Management
Swami Keshwanand Rajasthan Agricultural University
Bikaner (Raj) India
Visit the blog : www.thinkingai.in


ARUN KUMAR SHARMA

Machine Learning Enthusiast, Hobbyist, writer, blogger and S&M Training Professional
Certified Business Analytics Professional
Certified in Predictive Analytics from IIMx Bangalore
Certified in Macroeconomic Forecasting from IMFx
Certified in Text Analytics from openSAP

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