This document provides an overview of machine learning. It defines machine learning as a branch of artificial intelligence that allows computers to learn from data without being explicitly programmed. It discusses the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. It also outlines some popular machine learning algorithms and applications. Finally, it discusses the advantages and disadvantages of machine learning as well as its promising future and broad career opportunities.

1. MACHINE
LEARNING
PRESENTED BY :-
MANOJ KUMAR
7th Sem, Btech( CSE )

2. 1. What is Machine Learning
2. Types of Machine Learning
3. Application of Machine Learning
4. Advantages and Disadvantages of ML
5. Future of Machine Learning
6. Conclusion

3. What is Machine
Learning
A branch of artificial intelligence, concerned with the design and
development of algorithms that allow computers to “self-learn” from
training data and improve over time, without being explicitly programmed.
 The goal of machine leaning to create logic/rules to predict future
event/process.
 Machine learning is a method of analyzing data using an analytical model
that is built automatically, or ‘learned’, from training data.

4. • Some Examples
Image recognition
Speech recognition
Medical diagnosis
Statistical arbitrage
Predictive analytics
Extraction

5. • Types of Machine Learning
1. Supervised Learning
2.Unsupervised Learning
3.Reinforcement Learning

7. • Supervised learning is a type of machine learning that uses labeled data to
train machine learning models. In labeled data, the output is already known.
The model just needs to map the inputs to the respective outputs.
• Supervised Learning methods need external supervision to train machine
learning models.
• An example of supervised learning is to train a system that identifies the
image of an animal.
• You can see that we have our trained model that identifies the picture of a
cat.

8. Some Algorithms
• Linear Regression
• Logistic Regression
• Support Vector Machine
• K Nearest Neighbor
• Decision Tree
• Random Forest
• Naive Bayes

10. • Unsupervised learning is a type of machine learning that uses unlabeled
data to train machines. Unlabeled data doesn’t have a fixed output
variable. The model learns from the data, discovers the patterns and
features in the data, and returns the output.
• Unsupervised learning finds patterns and understands the trends in the
data to discover the output. So, the model tries to label the data based
on the features of the input data.
• The training process used in unsupervised learning techniques does not
need any supervision to build models. They learn on their own and
predict the output.
• Unsupervised learning is used for solving clustering and association
problems.

11. • Below is an example of an unsupervised learning technique that
uses the images of vehicles to classify if it’s a bus or a truck.
• The model learns by identifying the parts of a vehicle, such as a
length and width of the vehicle, the front, and rear end covers,
roof hoods, the types of wheels used, etc.
• Based on these features, the model classifies if the vehicle is a bus
or a truck.

12. Some Algorithms
•K Means Clusterin
•Hierarchical Clustering
•DBSCAN
•Principal Component Analysis

14. • Reinforcement Learning trains a machine to take suitable actions and
maximize its rewards in a particular situation. It uses an agent and an
environment to produce actions and rewards.
• The agent has a start and an end state. But, there might be different paths for
reaching the end state, like a maze. In this learning technique, there is no
predefined target variable.
• An example of reinforcement learning is to train a machine that can identify
the shape of an object, given a list of different objects. In the example shown,
the model tries to predict the shape of the object, which is a square in this case.

15. Some Algorithms
• Q-learning
• Sarsa
• Monte Carlo
• Deep Q network

19. • Easily identifies trends and patterns
• No human intervention needed (automation)
• Continuous Improvement
• Handling multi-dimensional and multi-variety data
• Wide Applications

21. • It is expensive
• Data Acquisition
• Errors are frequent and take a long time
• Needs more Time and Resources
• High error-susceptibility

22. Future Of Machine
Learning
• Machine Learning is one of the best career choices of the 21st century
because it is expanding across all fields such as banking and finance,
information technology, media & entertainment, gaming, and the
automotive industry.
• The scope of Machine Learning in India, as well as in other parts of the
world, is high in comparison to other career fields when it comes to job
opportunities. According to Gartner, there will be 2.3 million jobs in the
field of Artificial Intelligence and Machine Learning by 2022.
• According to Forbes, the average salary of a Machine Learning Engineer
in the United States is US$99,007. In India, it is ₹865,257.

23. Machine learning is the best and very high paying field . It’s
scope is very broad, in just 12 months of dedication and
consistency make your career in this field. All you need to
focus on your preparation.
Good things always take some Efforts !