This document provides an overview of machine learning. It defines machine learning as the study of algorithms that improve their performance at some task with experience by optimizing a performance criterion using example data or past experience. It discusses how machine learning works and some real-life applications such as spam email detection, loan approvals, and recommendation systems. It also covers the types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

1. MACHINE LEARNING

2. PRESENTED BY:-
GAURAV JOSHI

4. MACHINE LEARNING
Machine Learning:-
Study of algorithms that
improve their performance
at some task
with experience
Optimize a performance criterion using example data
or past experience.

5. HOW MACHINE LEARNING WORKS

6. Computer
Data
Program
Output
Computer
Data
Outputt
Program
TRADITIONAL PROGRAMMING
MACHINE LEARNING

7. REAL LIFE APPLICATIONS

8. SPAM EMAIL DETECTION

9. LOAN APPROVAL IN BANKS

10. RECOMMENDATION SYSTEMS

11. WHY NOW?
Concept of Machine Learning was emerged in early 1960s.
THEN WHY IT IS TRENDING NOW?
WHY NOT EARLIER?

12. WE DON’T HAVE SUFFICIENT DATA

13. TODAY’S SCENARIO

14. GROWTH OF MACHINE LEARING
Machine learning is preferred approach to:-
• Speech recognition
• Natural language processing
• Computer vision
• Medical outcomes analysis
• Robot control
• Computational biology

15. TYPES OF ALGORITHMS
There some variations of how to define the types of
Machine Learning Algorithms but commonly they can
be divided into categories:-
• Supervised learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning

16. SUPERVISED LEARNING
Supervised learning is the Data mining task of
inferring a function from labeled training data.The
training data consist of a set of training examples.
In supervised learning, each example is a pair
consisting of an input object (typically a vector) and a
desired output value.

17. Works with
Labeled Data

18. UNSUPERVISED LEARNING
Unsupervised Learning is a class of Machine
Learning techniques to find the patterns in data. The
data given to unsupervised algorithm are not
labelled, which means only the input variables(X) are
given with no corresponding output variables.

20. SEMISUPERVISED LEARNING
Semi-supervised learning is a class of machine
learning tasks and techniques that also make use of
unlabeled data for training – typically a small amount
of labeled data with a large amount of unlabeled
data.

21. REINFORCEMENT LEARNING
Reinforcement Learning is a type of Machine
Learning, and thereby also a branch of Artificial
Intelligence. It allows machines and software agents
to automatically determine the ideal behaviour within
a specific context, in order to maximize its
performance.

23. DECISION TREE(SUPERVISED)

24. KNN(K Nearest Neighbour)