An Introduction to Machine Learning Algorithms

1. AN INTRODUCTION TO  MACHINE LEARNING ALGORITHMS B Y : C R E D I B L L

2. WHAT ARE MACHINE LEARNING ALGORITHMS Machine Learning is the hottest trend in the IT industry, Machine Learning is quite helpful in making predictions based on past collection of data. Behind the scene, things work with help of complex algorithms that make it happen! Machine learning algorithms can be classified in two different ways.       1. Grouping of algorithms by learning style.      2. Grouping of algorithms by similarity of functions.

3. 1.Algorithms by Learning Style Supervised Learning Unsupervised Learning Semi-Supervised Learning

4. Supervised Learning Supervised learning is a method by which machines learn from data sets which are fed to them. These data sets include characteristics, patterns, dimensions or physical situations. Supervised learning is a popular technology mostly used to create real-life scenarios. In supervised learning a machine is given data for training and machine classifies the data on a different set of rules. This process is used to accomplish complex real-life tasks such as to detect a credit card fraud or may be to classify a disease after complex calculations.

5. Unsupervised Learning In Unsupervised learning, machine approaches towards problems with little or no idea what final results should be like. In unsupervised learning there is no past data available, here data is classified on different characteristics such as shape, size, colours and other characteristics. The system will look for different data patterns in data sets and group them based on those attributes.

6. Semi-Supervised learning In Semi-supervised learning input data is a mixture of labelled and unlabeled data. There is a desired prediction problem but the model must learn the different structures to organize the data and to make predictions based on the learning. These kinds of algorithms work very well when the machine is fed up with a small number of labelled data-sets and a large number of unlabeled data-sets.

7. 2. Grouping of Algorithms with similarities. Regression Algorithm Instance-based Algorithm Regularization Algorithm Decision Tree Algorithm Bayesian Algorithm Clustering Algorithm

8. Regression Algorithm Regression methods have opted into statistical machine learning. This process is a little bit confusing because we can use regression to refer to the class of problem and also the class of algorithm. Types of Regression Algorithm Stepwise Regression Linear Regression Logistic Regression Ordinary Least Squares Regression (OLSR)

9. Instance based Algorithm Instance based algorithm build up a database of example data and compare new data using a similarity measure in order to find the best match. Types Instance based  Algorithm k-Nearest Neighbor (kNN) Learning Vector Quantization (LVQ) Self-Organizing Map (SOM) Locally Weighted Learning (LWL)

10. Regularization Algorithm  Instance-based algorithm build up a database of example data and compare new data using a similarity measure in order to find the best match. Types Regularization Algorithm Ridge Regression Least Absolute Shrinkage and Selection Operator (LASSO) Elastic Net Least-Angle Regression (LARS)

11. Decision Tree Algorithm  In Decision tree algorithms a model of decisions is made Decision tree methods construct a model of decisions made based on the actual values of attributes in the data. Types Decision Tree  Algorithm Classification and Regression Tree (CART) Iterative Dichotomiser 3 (ID3) Chi-squared Automatic Interaction Detection (CHAID) Decision Stump M5 Conditional Decision Trees

12. Bayesian Algorithm In this method, the Bayesian theorem is applied to problems such as classification and regression. Types of Bayesian Algorithm Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Averaged One-Dependence Estimators (AODE) Bayesian Belief Network (BBN) Bayesian Network (BN)

13. Clustering Algorithm Clustering methods are typically organized by the modeling approaches such as centroid-based and hierarchal. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality. Types of Clustering Algorithm k-Means k-Medians Expectation Maximisation (EM) Hierarchical Clustering

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