The document provides an introduction to machine learning, outlining its main classifications: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning includes regression and classification problems, while unsupervised learning identifies patterns in unlabeled data. Additionally, it contrasts machine learning with concepts like deep learning and data science, highlighting their respective roles in processing data and extracting insights.

1. Introduction to Machine learning
Name : Deepesh Yadav
Roll no. : 17ECTEC005
Branch : ECE
Subject : Industrial training

2. In general, any machine learning problem can be
assigned to one of the three broad classification
:-
● Supervised learning
● Unsupervised learning
● Reinforcement learning

3. Supervised Learning :-
1) Regression Problem(Pridicting continuous output)
● Pridicting prices of house
● Finding age of person
1) Classification Problem(Pridicting discrete output)
● Pridicting if an email is spam or not
● Pridicting if post on social meadia is hated or not

4. Unsupervised learning :-
Unsupervised learning is a type of machine learning that looks for previously
undetected patterns in a data set with no pre-existing labels and with a minimum
of human supervision. In contrast to supervised learning that usually makes use of
human-labeled data, unsupervised learning, also known as self-organization
allows for modeling of probability densities over inputs

5. Reinforcement learning:-
Reinforcement learning is an area of Machine Learning. It is about taking suitable
action to maximize reward in a particular situation. It is employed by various
software and machines to find the best possible behavior or path it should take in
a specific situation. Reinforcement learning differs from the supervised learning in
a way that in supervised learning the training data has the answer key with it so
the model is trained with the correct answer itself whereas in reinforcement
learning, there is no answer but the reinforcement agent decides what to do to
perform the given task. In the absence of a training dataset, it is bound to learn
from its experience.

6. AI vs ML vs Deep learning vs Data science :-

7. Deep learning/neural network :-
● Deep learning is an AI function that mimics the workings of the human brain
in processing data for use in detecting objects, recognizing speech,
translating languages, and making decisions.
● Deep learning AI is able to learn without human supervision, drawing from
data that is both unstructured and unlabeled.

8. Data science :-
Data science is the field of study that combines domain expertise, programming
skills, and knowledge of mathematics and statistics to extract meaningful
insights from data.In turn, these systems generate insights which analysts and
business users can translate into tangible business value.

9. Thank you