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Introduction to Machine Learning

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What is Machine Learning

  • Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision by itself without human intervention.
  • It describes how computer perform tasks on their own by previous experiences.
  • Therefore we can say in machine language artificial intelligence is generated on the basis of experience.

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Normal Computer vs ML

  • The difference between normal computer software and machine learning is that a human developer hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a large number of data.

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Some of the machine learning algorithms are:

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  • Neural Networks
  • Random Forests
  • Decision trees
  • Genetic algorithm
  • Radial basis function
  • Sigmoid

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Types of Machine Learning

There are three types of machine learning

    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning

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Machine Learning Uses:

  • Traffic prediction
  • Virtual Personal Assistant
  • Speech recognition
  • Email spam and malware filtering
  • Bioinformatics
  • Natural language processing

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Real Time Examples for ML

  • TRAFFIC PREDICTION
  • VIRTUAL PERSONAL ASSISTANT
  • ONLINE TRANSPORTATION
  • SOCIAL MEDIA SERVICES
  • EMAIL SPAM FILTERING
  • PRODUCT RECOMMENDATION
  • ONLINE FRAUD DETECTION

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Best Programming Languages for ML

Some of the best and most commonly used machine learning programs are

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      • Python,
      • java,
      • C,
      • C++,
      • Shell,
      • R,
      • JavaScript,
      • Scala,
      • Shell,
      • Julia

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Difference Between Machine Learning And Artificial Intelligence

  • Artificial Intelligence is a concept of creating intelligent machines that stimulates human behaviour whereas Machine learning is a subset of Artificial intelligence that allows machine to learn from data without being programmed.

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Advantages of Machine Learning

  • Fast, Accurate, Efficient.
  • Automation of most applications.
  • Wide range of real life applications.
  • Enhanced cyber security and spam detection.
  • No human Intervention is needed.
  • Handling multi dimensional data.

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Disadvantages of Machine Learning

  • It is very difficult to identify and rectify the errors.
  • Data Acquisition.
  • Interpretation of results Requires more time and space.

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Thank you

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17-07-2020