machine learning

1. Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur
Department of Computer Science & Engineering
Industry Training (ITR) Presentation
Topic – Machine Learning
Submitted by – Garvit Tamra
19ESKCS084
CSE
BG2
Submitted to – Ms. Abha Jain

2. Index
Page No. Topic
3 Introduction
4 Why Machine Learning is important
5 Applications of Machine Learning
6 Types of Machine Learning
11 My Project
13 Machine Learning as a process
14 Certificate
15 References

3. • Machine learning is programming computers to
optimize a performance criterion using example
data or past experience.
• Machine learning is an application of
artificial intelligence that involves algorithms
and data that automatically analyse and
make decision by itself without human
intervention.
• ML is a branch of artificial intelligence:
• Uses computing based systems to make sense out
of data
• Extracting patterns, fitting data to functions,
classifying data, etc
Introduction

4. Why MachineLearning is important
• Data is the lifeblood of all business.
Data-driven decisions increasingly make
the difference between keeping up with
competition or falling further behind.
Machine learning can be the key to
unlocking the value of corporate and
customer data and enacting decisions
that keep a company ahead of the
competition.

5. Applications
of Machine
learning

6. There are three types of machine learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning
Types of Machine Learning

7. As its name suggests, Supervised machine learning is based on supervision. It means in the
supervised learning technique, we train the machines using the "labelled" dataset, and based
on the training, the machine predicts the output. Here, the labelled data specifies that some of
the inputs are already mapped to the output. More preciously, we can say; first, we train the
machine with the input and corresponding output, and then we ask the machine to predict the
output using the test dataset.
Categories of Supervised Machine Learning
Supervised machine learning can be classified into two types of problems, which are given
below:
•Classification
•Regression
Supervised machine learning

8. Unsupervised Machine Learning
Unsupervised learning is different from the Supervised learning technique; as its name suggests,
there is no need for supervision. It means, in unsupervised machine learning, the machine is
trained using the unlabeled dataset, and the machine predicts the output without any
supervision.
Categories of Unsupervised Machine Learning
Unsupervised Learning can be further classified into two types, which are given below:
•Clustering
•Association

9. Reinforcement Learning
Reinforcement learning works on a feedback-based process, in which an AI agent (A
software component) automatically explore its surrounding by hitting & trail, taking action,
learning from experiences, and improving its performance.
Categories of Reinforcement Learning
Reinforcement learning is categorized mainly into two types of methods/algorithms:
•Positive Reinforcement Learning: Positive reinforcement learning specifies increasing the
tendency that the required behaviour would occur again by adding something. It enhances the
strength of the behaviour of the agent and positively impacts it.
•Negative Reinforcement Learning: Negative reinforcement learning works exactly opposite to
the positive RL. It increases the tendency that the specific behaviour would occur again by
avoiding the negative condition

10. 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.
Disadvantages of Machine Learning
• It is very difficult to identify and rectify the
errors.
• Data Acquisition.
• Interpretation of results Requires more time
and space.

11. My project- Movie recommendation system

14. •Certificate

15. References
www.uow.edu.au
www.thoughtco.com
www.socialmediatoday.com
www.w3schools.com
www.geeksforgeeks.org

16. Thank You