But first Let me describe Machine Learning a little bit.

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Artificial Intelligence (AI) is everywhere. A possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is Machine Learning (ML).

Important Terms in Machine Learning

  • Supervised Learning Algorithms

    Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.

  • Unsupervised Learning Algorithms

    Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.

  • Semi-supervised Learning Algorithms

    Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.

  • Reinforcement Learning Algorithms

    Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.

  • Regression 

    A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is the Linear Regression. It tries to fit data with the best hyper-plane which goes through the points. It is a Supervised Technique.

  • Regression Algorithms

    • Simple Linear Regression
    • Simple Non-Linear Regression
    • Multiple Linear Regression
    • Multiple Non-Linear Regression
    • Polynomial Regression
    • Stepwise Regression
    • Ridge Regression
    • Lasso Regression

  • Classification

    Classification is a technique to categorize our data into a desired and distinct number of classes where we can assign label to each class. This comes under Supervised Learning.

    Applications of Classification are: speech recognition, handwriting recognition, biometric identification, document classification etc.

  • Classification Algorithms

    • Naïve Bayes
    • SGD (Stochastic Gradient Descent)
    • KNN
    • Decision Tree
    • Random Forest
    • SVM (Support Vector Machine)
  • Clustering

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.

  • Clustering Algorithms

    • K-Means Clustering
    • Mean-Shift Clustering
    • Fuzzy Clustering
    • Density-Based Spatial
    • Clustering of Applications with Noise (DBSCAN)
    • Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
    • Hierarchical Clustering