Machine Learning Algorithims

Machine Learning (abbreviated as “ML”) Algorithms are programs (math and logic) that alter themselves to perform better as they are exposed to more information. The “learning” portion of ML implies that those projects change how they process information over time, much as humans change how they process data by learning. So a ML algorithm is a program with a particular way to adjusting its own parameters, given feedback on its past performance making forecasts/predictions about a dataset.

These algorithms can be mainly categorised as:

1. Supervised Learning: It is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

• a. Regression Algorithms: Regression Algorithms predict the output values based on input features from the data fed in the system. The go-to methodology is the algorithm builds a model on the features of training data and using the model to predict value for new data. Sub-types are:-
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
• b. Classification Algorithms: Classification Algorithm is a function that weighs the input features so that the output separates one class into positive values and the other into negative values. Classifier training is performed to identify the weights (and functions) that provide the most accurate and best separation of the two classes of data. Sub-types are:-
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification

2. Un-supervised Learning: The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. Algorithms are left to their own devises to discover and present the interesting structure in the data.

• a. Clustering Algorithms: Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. Sub-types are:-
  • K-Means Clustering
  • Hierarchical Clustering
• b. Association Rule Learning Algorithms: Association Rule Learning (ARL) is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Sub-types are:-
  • Apriori
  • Eclat

3. Reinforcement Learning Algorithms: Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment. This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task. Some sub-types are:

• a. Upper Confidence Bound (UCB)
• b. Thompson Sampling

4. Natural Language Processing: NLP is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyse large amounts of natural language data.

5. Dimensionality Reduction Algorithms Dimensionality reduction is the process of reducing the dimension of your feature set. It is bringing the number of columns down to say, twenty or converting the sphere to a circle in the two-dimensional space. Some sub-types are:

• a. Principal Component Analysis (PCA)
• b. Linear Discriminant Analysis (LDA)
• c. Kernel PCA