Machine learning is the study of computer algorithms that allow computer program to automatically improves through experience

1. Seminar Presentation
on
Introduction to Machine Learning
Submitted by:-
Salman Saifi
BCA 5th semester
180733106077
Submitted to:-
prof. Anuj pawar
Department of computer
application
Hi-tech Institute of Engineering & Technology, Ghaziabad
Session:- 2018-21

2. Machine Learning

3. Table of content
1. Introduction to machine learning
2. Types of machine learning
 Supervised machine learning
 Unsupervised machine learning
 Reinforcement machine learning
3. Why machine learning is important
4. Applications of machine learning
5. Conclusion

4. Introduction to Machine Learning
• Machine learning is the study
of computer algorithms that
allow computer programs to
automatically improves
through experience.
• Machine learning is an
application of artificial
intelligence (AI) that provides
systems the ability to
automatically learn and
improve from experience
without being explicitly
programmed.

5. AI vs MI vs Deep Learning

6. AI vs MI vs Deep Learning
• AI means getting a computer to mimic human behavior in
some way.
• Machine learning is a subset of AI, and it consists of the
techniques that enable computers to figure things out from
the data and deliver AI applications.
• Deep learning, meanwhile, is a subset of machine learning
that enables computers to solve more complex problems.

7. Types of Machine Learning

8. Supervised Machine Learning
• Supervised learning in simple language means training the machine
learning model just like a coach trains a batsman. In Supervised
Learning, the machine learns under the guidance of labeled data
i.e. known data. This known data is fed to the machine learning
model and is used to train it. Once the model is trained with a
known set of data, you can go ahead and feed unknown data to the
model to get a new response.

9. Unsupervised Machine Learning
• In unsupervised machine learning, there is no such provision
of labeled data. The training data is unknown or unlabeled.
This unknown data is fed to the machine learning model and is
used to train the model. The model tries to find patterns and
relationships in the dataset by creating clusters in it. The thing
to be noted here is that unsupervised learning is not able to
add labels to the clusters.

10. Reinforcement Machine Learning
• In this, the machine learns from a hit and trial method.
Whenever the model predicts or produces a result, it is
penalized if the prediction is wrong or rewarded if the
prediction is correct. Based on these actions the model trains
itself.

11. Why machine learning important?
• Machine learning is the core subarea of artificial intelligence. It
makes computers get into a self learning mode without explicit
programming. When feed new data, these computers learn, grow,
change, and develop by themselves.
• Machine learning is important because of its wide range of
applications and its incredible ability to adapt and provide solutions
to complex problems efficiently, effectively and quickly.

12. Applications of Machine Learning
• Online Fraud Detection
• Online Customer Support i.e. Chatbot
• Speech Recognition
• Product Recommendations
• Image Recognition
• Automatic Language Translate
• Virtual Personal Assistance
• Self Driving Car
• Email Spam and Malware Filtering

13. Conclusion
• As we move forward into the digital age, our Technology continues
to leaps and strides forward. This incredible form of Artificial
Intelligence is already being used in various industries and
professions. Form Marketing, to medicine and web security. This
Technology can improve our lives in several numerous ways.

14. Thank you