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10 狠狠撸 Intro to ML

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Nitesh Surana
Nitesh Surana

First 狠狠撸share Presentation A very very small introduction to Machine Learning and providing a very small overview of the context. Curated from the Internet and other sources.

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Introduction to Machine Learning NITESH SURANA INFORMATION TECHNOLOGY SECTION - A , ROLL NO. -55 DATE - 30.03.2017
What do you mean?
Formally… ? “A computer program is said to learn from experience (E) with some class of tasks (T) and a performance measure (P) if its performance at tasks in T as measured by P improves with E” ? Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, it allows computers to find hidden insights without being explicitly programmed where to look.
Machine Learning ? Supervised ? Classification ? Regression ? Unsupervised ? Reinforcement ? Semi-Supervised
Supervised Learning ? Model preparation using training data ? If predictions are wrong, they are corrected ? The training process continues until the model achieves a desired level of accuracy on the training data. ? Some types – Classification, Regression
Unsupervised Learning ? It is used against data that has no historical labels. ? The system is not told the "right answer." The algorithm must figure out what is being shown. ? A model is prepared by deducing structures present in the input data.
Where is ML?
10 狠狠撸 Intro to ML
10 狠狠撸 Intro to ML
10 狠狠撸 Intro to ML

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10 狠狠撸 Intro to ML

  • 1. Introduction to Machine Learning NITESH SURANA INFORMATION TECHNOLOGY SECTION - A , ROLL NO. -55 DATE - 30.03.2017
  • 2. What do you mean?
  • 3. Formally… ? “A computer program is said to learn from experience (E) with some class of tasks (T) and a performance measure (P) if its performance at tasks in T as measured by P improves with E” ? Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, it allows computers to find hidden insights without being explicitly programmed where to look.
  • 4. Machine Learning ? Supervised ? Classification ? Regression ? Unsupervised ? Reinforcement ? Semi-Supervised
  • 5. Supervised Learning ? Model preparation using training data ? If predictions are wrong, they are corrected ? The training process continues until the model achieves a desired level of accuracy on the training data. ? Some types – Classification, Regression
  • 6. Unsupervised Learning ? It is used against data that has no historical labels. ? The system is not told the "right answer." The algorithm must figure out what is being shown. ? A model is prepared by deducing structures present in the input data.