This document provides an introduction to machine learning, defining it as a method of data analysis that enables computers to learn from experience and improve performance without explicit programming. It discusses various types of machine learning, including supervised, unsupervised, and reinforcement learning, with a focus on the training and accuracy of predictive models. The text emphasizes the differences between supervised learning, which uses labeled data, and unsupervised learning, which analyzes unlabeled data to discover patterns.

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.

7. Where is ML?