"Unveiling the Magic of Machine Learning: Join me for a concise yet insightful presentation on the captivating world of Machine Learning (ML). Discover how ML algorithms transform data into predictive models, driving smarter decisions. From regression to classification and beyond, we'll delve into the basics, demystify key concepts, and showcase real-world applications. Let's explore the algorithms shaping our digital landscape and understand how they're revolutionizing industries. Don't miss this opportunity to grasp the essence of ML in a nutshell!"

1. Machine
Learning
Name: Sk Samiul Islam
Roll No: ECE204034
Subcode: ECEUGPR01
Supervised by- Dr. Md. Abdul Alim Sheikh
Introduction to

2. Introduction

3. 3
Contents
 What is Machine Learning
 Why Machine Learning
 Types of Machine Learning Model
 Supervised Learning
 Unsupervised Learning
 Reinforcement Learning
 Application of Machine Learning
 Conclusion
 Resources

4. 4
What is Machine Learning?
 Machine learning is a type of artificial intelligence that allows
software applications to become more accurate at predicting
outcomes without being explicitly programmed to do so.
 Machine learning enables a machine to automatically learn
from data, improve performance from experiences, and predict
things without being explicitly programmed.
 Machine learning algorithms use historical data as input to
predict new output values.
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Why Machine Learning?
 Machine learning is important because it gives
enterprises a view of trends in customer behavior and
business operational patterns, as well as supports the
development of new products.
 Learning is used when:
 Human expertise does not exist (navigating on Mars),
 Humans are unable to explain their expertise (speech
recognition)
 Solution changes in time (routing on a computer network)
 Solution needs to be adapted to particular cases (user
biometrics)

6. 6
Types of Machine Learning Model
 Supervised Learning
 Classification(Discrete labels)
 Regression(Real value)
 Unsupervised Learning
 Semi-supervised Learning
 Reinforcement Learning

7. 7
Supervised Learning
 Supervised learning is the types of machine learning in
which machines are trained using well "labelled" training
data, and on basis of that data, machines predict the
output. The labelled data means some input data is already
tagged with the correct output.
 How it work
 In supervised learning, models
are trained using labelled dataset,
where the model learns about each
type of data. Once the training
process is completed, the model
is tested on the basis of test data
& then it predicts the output.

8. 8
Training & Testing Model
 Training is the process of the making the system able to learn.

9. 9
Types of Supervised Learning
 Regression
 Regression algorithms are used if there is a relationship between
the input variable and the output variable. It is used for the
prediction of continuous variables, such as Weather forecasting,
Market Trends, etc.
 Classification
 Classification algorithms are used when the output variable is
categorical, which means there are two classes such as Yes-No,
Male-Female, True-false, etc.
Regression
• Linear Regression
• Regression Trees
• Non-Linear Regression
• Polynomial Regression
Classification
• Random Forest
• Decision Trees
• Logistic Regression
• Support vector Machines

10. 10
Supervised Learning: Uses
 Prediction of future cases: Use the rule to predict the
output for future inputs
 Knowledge extraction: The rule is easy to understand
 Compression: The rule is simpler than the data it
explains
 Outlier detection: Exceptions that are not covered by the
rule, e.g., fraud

11. 11
Unsupervised Learning
 Unsupervised learning is a type of machine learning in
which models are trained using unlabeled dataset and are
allowed to act on that data without any supervision.
 How it work
 Unsupervised ML algorithms
do not require data to be labeled.
They sift through unlabeled data
to look for patterns that can be
used to group data points into
subsets. Most types of deep
learning, including neural
networks, are unsupervised
algorithms.

12. 12
Types of Unsupervised Learning
 Clustering
 Splitting the dataset into groups based on similarity.
 Association mining
 Identifying sets of items in a data set that frequently occur together.
 Unsupervised Learning algorithms
 K-means clustering
 KNN (k-nearest neighbors)
 Hierarchal clustering
 Anomaly detection
 Neural Networks
 Principle Component Analysis
 Independent Component Analysis

13. 13
Reinforcement Learning
 Reinforcement Learning is a feedback-based Machine learning
technique in which an agent learns to behave in an environment
by performing the actions and seeing the results of actions. For
each good action, the agent gets positive feedback, and for each
bad action, the agent gets negative feedback or penalty.
 How it work
 Reinforcement learning work
by programming an algorithm
with a distinct goal and a prescribed
set of rules for accomplishing that
goal.This algorithm received positive
rewards when that action goes to
the ultimate goal & avoid punishment
when it goes to the farther away from
the ultimate goal.

14. 14
Reinforcement Learning Example
 Robotics
 Robots can learn to perform tasks the physical world using this technique.
 Video gameplay
 Reinforcement learning has been used to teach bots to play a number of
video games.
 Resource management
 Given finite resources and a defined goal, reinforcement learning can help
enterprises plan out how to allocate resources.

15. 15
Application of Machine Learning

16. 16
Conclusion
 We have a simple overview of some technique and
algorithms in machine learning. Furthermore,there are
more and more techniques apply in machine learning as
a solution. In the future,machine learning will play as
important role in our daily life.

17. 17
Resources: Datasets
 UCI Repository:
https://archive.ics.uci.edu/ml/datasets.php
 Kaggle:
https://www.kaggle.com/datasets
 Google Dataset:
https://datasetsearch.research.google.com/

18. 18
Resources & Journals
 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of
statistical learning: Data mining, inference, and prediction. Springer.
 Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An
introduction. MIT Press.
 https://developers.google.com/machine-learning/crash-course/
 https://scikit-learn.org/

19. Thank
You
Sk Samiul Islam
sksamiul602@gmail.com