Machine Learning- An Intro

Machine learning is a method of data analysis that finds hidden insights and patterns without being explicitly programmed on where to look.

Machine Learning Categorization

·      Text categorization, Ex: Sentiment Analysis, Spam filtering

·      Anomaly Detection, Ex: Risk & Fraud detection

·      Image processing, Ex: Face detection

·      Natural language processing, Ex: Sentiment Analysis, Feedback Mechanism

·      Segmentation, Ex: Customer, Market, Product

·      Enterprise Behavioral Science, Ex: Productivity, Product Buying

And so on…

Types of Machine Learning

• Supervised learning: The algorithm has training data with a known expected output. Example: Fraud detection, Churn Prediction

• Unsupervised learning: The algorithm identifies patterns in the data without being told the expected outcome. Example: Anomaly detection, Customer Segmentation, Product Recommendation

• Deep learning: The is based on the way the human brain process information and learns. If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. But with a deep learning model, the algorithms can determine on their own if a prediction is accurate or not

• Reinforcement learning: The algorithm learns from interactions with the environment. It uses trial-and-error and memorizes strategy for further improvement. Example: chess program.

Types of Machine Learning Algorithms

• Classification (Supervised Learning): A set of data is given, and your answer is one of the pieces of data. Example: Decision Trees, Logistic Regression, Naive Bayes, K Nearest Neighbors, Linear SVC (Support vector Classifier) etc.

• Regression (Supervised Learning): In regression problems we are trying to predict continuous valued output. Given a size of the house predict the price(real value). Example: Linear Regression, Regression Trees(e.g. Random Forest), Support Vector Regression (SVR) etc.

• Anomaly detection: Analyzes patterns. EXAMPLE: Seasonal trend decompositions, Clustering & regression, Exponential Smoothing etc.

• Clustering (Unsupervised Learning): Used if we need to know about structure; forms groups to interpret the data. Example: K-Means, Mean-Shift etc.

• Reinforcement: Used when a decision needs to be made based on past experience and the environment.