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Machine Learning

Machine Learning

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Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to makes actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.

A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the user's historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.

Machine learning is also used for a variety of task like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.

  1. Machine Learning vs. Traditional Programming
  2. How does Machine learning work?
    • Inferring
  3. Machine learning Algorithms and where they are used?
  4. Supervised learning
    • What is Supervised Machine Learning?
    • How Supervised Learning Works
    • Types of Supervised Machine Learning Algorithms
    • Challenges in Supervised machine learning
    • Advantages of Supervised Learning:
    • Disadvantages of Supervised Learning
    • Best practices for Supervised Learning
  5. Unsupervised learning
    • What is Unsupervised Learning?
    • Example of Unsupervised Machine Learning
    • Why Unsupervised Learning?
    • Types of Unsupervised Learning
    • Applications of unsupervised machine learning
    • Disadvantages of Unsupervised Learning
    • Supervised vs. Unsupervised Learning
  6. How to choose Machine Learning Algorithm
  7. Challenges and Limitations of Machine learning
  8. Application of Machine learning
  9. Why is Machine Learning important?



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