Introduction of machine learning

1. Prepared By :-
Arpit Kumar Sharma
AIETM , Jaipur
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2. Introduction of Machine Learning 2
•Machine learning is a tool for turning
information into knowledge

3. Types Of M.L. 3
•Supervised Learning
•Unsupervised Learning
•Semi-supervised Learning
•Reinforcement Learning

4. Terminology 4
•Dataset: A set of data examples,
that contain features important to
solving the problem.
•Features: Important pieces of data
that help us understand a problem.
•Model: The representation
(internal model) of a phenomenon
that a Machine Learning algorithm
has learnt. The model is the output
you get after training an algorithm..

5. Process 5
•Data Collection: Collect the data that the algorithm will learn
from.
•Data Preparation: Format and engineer the data into the optimal
format, extracting important features and performing
dimensionaility reduction.

6. Cont. 6
•Training: Also known as the fitting stage, this is where the
Machine Learning algorithm actually learns by showing it the
data that has been collected and prepared.
•Evaluation: Test the model to see how well it performs.
•Tuning: Fine tune the model to maximise it’s performance.

7. Supervised Learning 7

8. Types of Supervised Learning 8

9. Unsupervised Learning 9

10. Types of Unsupervised Learning 10
Clustering Association
Looking similarities in the Data Set. Finding a relationship between
data’s.
Eg : Bread , Toast, Cookies. Bread + Milk
Eg : Dark chocolate, White
chocolate
Kadhi+Kachori

11. Semi-Supervised Learning 11
Semi-supervised learning is a mix between
supervised and unsupervised approaches.

12. Reinforcement Learning 12

13. Application Of ML 13

14. Thank You... 14