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Machine Learning

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.

Current trends in ML

  • Critical component of Digital Transformation journey.
  • Confusion over ML / AI / Cognitive / Analytics / Predictive.
  • Comprehensive platform for all Analytics needs adopting polyglot analytics.
  • Data Science for everybody / Ease of use / Reducing Need for Training Data
  • Key towards automation of Data Science.
  • Real Time Integration with business processes to assist decision Making

Why Machine Learning is Important?

    Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

    All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Types of Machine Learning : Supervised learning

  • Data is with clearly defined output .
  • Task Driven, Direct feedback.
  • Prediction of future / outcome.
  • Training data includes desired outputs
  • Key towards automation of Data Science.
  • Predictive Models like Clustering Algorithms

Problems handled & Algorithms in Supervised Learning

  • Classification – Support Vector Machines.
  • Naïve Bayes, Nearest neighbor, etc.
  • Regression – Linear Regression, Decision.
  • Trees, Ensemble Methods, etc

Use Cases

  • Pattern Recognition.
  • Speech Recognition.
  • Market Segmentation.
  • Predict Fraud.

Types of Machine Learning : Unsupervised learning

  • Machine understands the data on its own.
  • Machine detects the patterns and structures.
  • No prediction.
  • Training data does not include desired outputs.

Problems handled & Algorithms in Unsupervised Learning

  • Clustering – K-Means Clustering.
  • Principal Component Analysis (PCA).
  • Singular Value Decomposition (SVD).
  • Hidden Markov model etc

Use Cases

  • Pattern Recognition.
  • Speech Recognition.
  • Market Segmentation.
  • Predict Fraud.