This document provides an introduction to machine learning, defining it as a branch of artificial intelligence that enables systems to learn from data without explicit programming. It explains the two main types of machine learning: supervised learning, where models learn from labeled data, and unsupervised learning, which identifies patterns in unlabeled data. The document also discusses the scope of machine learning and advises against its use in certain scenarios due to data limitations.

1. Machine Learning
By Sumit Kumar Pandey

2. Introduction
What is Machine Learning?
Using data to answer questions.
According to Wikipedia, “Machine learning is a field of artificial
intelligence that uses statistical techniques to give computer
systems the ability to "learn" (e.g., progressively improve
performance on a specific task) from data, without being
explicitly programmed.”

3. Computer
Data
Program
Output
ComputerData
Output
Program
Other programming vs Machine learning
Traditional programming
Machine learning

4. Applications of ML

5. Examples of ML

6. Types of Machine Learning

7. Supervised Learning
• Supervised learning is the machine learning task of
learning a function that maps an input to an
output based on example input-output pairs

8. Let’s look a bit into Supervised ML

9. Explanation of previous slide :
• We train the baby for these two example(training set) and explain
the labels that red colour spherical item is apple and yellow colour
cylindrical item is banana and for the third example red colour
cylindrical item we ask for what it is .
• From the previous experience that cylindrical item is banana baby
decide that it may be banana.

10. Unsupervised Learning
• Unsupervised learning is a branch of machine learning
that learns from test data that has not been labeled,
classified or categorized. Instead of responding to
feedback, unsupervised learning identifies commonalities
in the data and reacts based on the presence or absence
of such commonalities in each new piece of data.

11. Let’s look deeper into Unsupervised ML

12. Explanation of previous slide:
• In this we show the image of dogs and cats to baby but in that
there is no labels like red or spherical etc. just like in supervised
learning. So baby did not decide but it can analyze that 1,3,5 are
similar things and 2,4 are similar things and according to that baby
separated the similar items .
• The separation of the similar items is know as clustering . This is
mostly use in unsupervised learning.

13. Languages Used in Machine Learning

14. Scope of Machine Learning

15. Biggest Confusion: AI vs ML vs Deep Learning

16. Don’t use Machine learning if.....
Not enough data
Not quality labelled data
Don’t use for network traffic analysis
Not enough domain expertise to engineer features

17. Thank You
Sumit Kumar Pandey
B.Tech., CSE, 3rd Year
Gurukula kangri university