Machine Learning and its types

2. CONTENT

3. WHAT IS AI ?
Artificial intelligence, refers to the
simulation of human intelligence in
machines that are programmed to think
and learn like humans.
It encompasses a wide range of techniques
and technologies aimed at enabling
computers to perform tasks that typically
require human intelligence, such as
understanding natural language,
recognizing patterns, making decisions,
and solving problems.

4. WHAT IS ML?
MACHINE LEARNING IS A SUBSET OF ARTIFICIAL
INTELLIGENCE (AI) THAT FOCUSES ON DEVELOPING
ALGORITHMS AND MODELS THAT ALLOW COMPUTERS
TO LEARN FROM DATA AND MAKE PREDICTIONS OR
DECISIONS WITHOUT BEING EXPLICITLY PROGRAMMED
TO PERFORM SPECIFIC TASKS.
IN TRADITIONAL PROGRAMMING, HUMANS WRITE CODE
TO INSTRUCT A COMPUTER ON HOW TO PERFORM A
TASK. HOWEVER, IN MACHINE LEARNING, THE
COMPUTER LEARNS TO PERFORM TASKS BY ANALYSING
AND INTERPRETING DATA.

5. Machine learning algorithms are trained to find
relationships and patterns in data. They use
historical data as input to make predictions, classify
information, cluster data points, reduce
dimensionality and even help generate new
content, as demonstrated by new ML-fueled
applications such as ChatGPT, Dall-E 2 and GitHub
Copilot.

6. ML
Process
The Machine Learning process involves building a
Predictive model that can be used to find a solution for a
Problem Statement
Step 1: Problem Definition
Step 2: Data Collection
Step 3: Preparing the Data
Step 4: Exploratory Data Analysis
Step 5: Building the Machine Learning Model
Step 6: Model evaluation and optimization
Step 7: Predictions

7. TYPES OF MACHINE
LEARNING

8. SUPERVISED
LEARNING

9. Supervised machine learning is a type of
machine learning where the algorithm learns
from labeled data, which means it is provided
with input-output pairs during the training
process.
The goal is to learn a mapping from input
variables to output variables, allowing the
algorithm to make predictions or decisions
when presented with new, unseen data
This learning model takes direct feedback to
check whether it is producing correct output
or not.

10. In supervised learning, each example in the
training dataset consists of an input and a
corresponding output label.
The input is typically represented as a feature
vector, where each feature provides some
information about the example.
The output label is the target variable that the
algorithm aims to predict.
LABELED DATA

12. TRAINING PROCESS
During the training phase, the algorithm is
presented with a dataset containing input
output pairs.
It learns from the examples by adjusting its
internal parameters to minimize the error
between its predictions and the true labels.
The learning algorithm iteratively improves its
performance through processes like gradient
descent, where it updates its parameters in the
direction that reduces the prediction error.

13. • The ultimate goal of supervised learning is to generalize well to unseen
data.
• A model that performs well on the training data but poorly on new, unseen
data is said to overfit.
• Overfitting occurs when the model captures noise in the training data
rather than the underlying pattern.
• Techniques such as cross-validation, regularization, and early stopping are
used to prevent overfitting and encourage better generalization.
Classification: Email spam detection, sentiment analysis, image
recognition. Regression: Stock price prediction, house price estimation,
demand forecasting.

14. Linear Regression: Simple and widely used for
regression tasks. Logistic Regression: Used for binary
classification problems. Decision Trees: Versatile for
both classification and regression tasks. Support
Vector Machines (SVM): Effective for classification
tasks, especially when dealing with high- dimensional
data. Neural Networks: Deep learning models capable
of learning complex patterns from large datasets.
COMMON ALGORITHMS

16. Unsupervised learning is a
type of machine learning
where the model learns
patterns from unlabeled
data without explicit
guidance.
Purpose: Uncover hidden
patterns, group similar data
points, and reduce the
dimensionality of data.

17. CLUSTERING
ASSOCIATION
Clustering is the method of dividing the objects
into clusters that are similar between them and
are dissimilar to the objects belonging to
another cluster. For example, finding out which
customers made similar product purchases.
Association is a rule-based machine learning
to discover the probability of the co-
occurrence of items in a collection. For
example, finding out which products were
purchased together.

19. Customer Segmentation: Divide customers into
groups based on their purchasing behavior.
Image and Text Analysis: Group similar images or
classify text documents without labeled data.
Recommendation Systems: Recommend products
or content based on user behavior and preferences.
Applications of Unsupervised
Learning

20. • Subjectivity in evaluation: Unlike supervised learning, where performance can be
objectively measured against labeled data, evaluating the performance of
unsupervised learning algorithms often relies on subjective measures such as
clustering coherence or visual inspection. This subjectivity can make it challenging to
compare different algorithms or determine the optimal solution.
• Difficulty in interpreting results: Unsupervised learning algorithms often provide
clusters, patterns, or associations in the data without explicit explanations. Interpreting
these results can be challenging, especially in high-dimensional or complex datasets,
leading to potential misinterpretation or misunderstanding of the underlying structure.
Challenges and
Limitations

21. REINFORCMENT
LEARNING

22. REINFORCMENT LEARNING
Reinforcement learning in
machine learning is like a trial-
and-error learning process.
•It’s similar to training a pet:
when the pet does something
good, it gets a treat, and when it
does something bad, it doesn’t.
Over time, the pet learns to
repeat the good behaviors to get
more treats.

23. FUNDAMENTALS OF
REINFORCEMENT LEARNNG
•In reinforcement learning, an AI agent (like a robot or
software) learns to make decisions by performing actions
and getting rewards or penalties based on the results.
The agent isn’t told what to do but instead learns from its
experiences, trying to get as many rewards as possible.
• The balance between exploration(seeking new
knowledge) exploitation(using existing knowledge) is a
key challenge in reinforcement learning.Strategies such
as epsilon-greedy and UCB adress this trade-off.

24. Deep reinforcement learning combines
deep learning with reinforcement
learning,enabling the handling of
complex,Q- networks and policy
gradients are popular approaches in
this domain
Reinforcement learning has
revolutionized robotics by enabling
autonomous decision-making in
dynamic environments. From robotic
arm control to autonomous
navigation,RL has diverse applications in
this field.
DEEP REINFORCEMENT LEARNING

25. Applications of Reinforcement
• Autonomous Vehicles: Guides decisions
for self-driving cars, including lane changes
and obstacle avoidance.
• Robotics: Used in motion control for
navigating and manipulating objects.
• Game Playing: Excels in training AI for
complex games like Go and chess.
• Natural Language Processing (NLP):
Applies to text summarization and question-
answering for human-like text generation.
• Personalized Recommendations:
Enhances user experience through tailored
content recommendations.

26. Pros Cons
1.Complex problem solving:
It can solve very complex
problem
2.Error correction :
Capable of correcting errors
during training.
3.Performance maximization :
Intended to maximizing
performance within a specific
context.
1.Maintenance cost :
High maintenance cost due to
complexity.
2.Complexity for simple
problems not preferable for
solving simple problems.