Introduction to Machine Learning

You use machine learning several times a day like Spam folder, facebook friend suggestions, google page rank etc.  It is a science of learning and extracting patterns from data to find some results.

Every time you use Facebook and recognize your friends' photograph. Its build in machine of intelligence. Just knowing the algorithms is not enough.

For more interesting things, you need to work with machine learning algorithms. It touch many segments of industry.

Growth of automation

  • Click stream data (Web Data) is used to mine the data.
  • Computational biology
  • Medical records
  • Field of engineering
  • Handwriting Recognition
  • Understanding images
  • Product recommendations
  • Spam filtering
  • Sentiment Analysis
  • Understanding Brain.
Supervised Learning

For every input, the learner is told the target and methodology by tester.

Different types of problems
  • Classification Problems
    • Spam Filtering
  • Regression Problems
    • Patient Diagnosis

Unsupervised Learning

For every input, learner produces best output/ solution and tester do not know the methodology but knows the target
Different types of problems
  • Clustering Problem
    • Social Network Analysis
    • Market Segmentation
    • Astronomical Data Analysis
    • Organize Computer Clusters
  • Cocktail Party Algorithm
    • Differentiate two different voices
Reinforcement Learning

For given input, learner produces output and gets continuous feedback to improve its output as the tester do not know the methodology


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