The document discusses machine learning, defining it as a field enabling computers to learn without explicit programming, exemplified by features like Facebook's facial recognition. It outlines the history of machine learning, key components in developing algorithms, and the pros and cons of its applications, highlighting the importance and growth of this domain. The conclusion emphasizes the significance of machine learning in current and future technological advancements.

1. Seminar on
MACHINELEARNING
-By:
Hitesh
B.E.(ECE)-4th Year
(SG16522)

2. CONTENTS
• Definition
• History
• Major Components
• Types
• Pros and Cons
• Applications

3. DEFINITION
• Machine Learning is a field of
study that
gives computers the capability to
learn without being explicitly
programmed.
• Think of Facebook’s
Facial Recognition
Feature which
prompts you to
tag photos
whenever
you upload a photo.

4. HISTORY

5. MACHINE LEARNING TIMELINE
• Neural Networks were first introduced as a
concept in a research paper in the year 1943.
• In the early days, machine learning was
somewhat slow due to the high cost of
computing.

6. Major Components
• To build a Machine Learning Algorithm,
following steps are considered:

7. TYPES

8. PROS AND CONS
PROS
• Easily Identify trends and
patterns.
• No human intervention
needed.
• Continuous Improvement
• Handling Multi-dimensional
and Multi-variety data
• Wide Applications
CONS
• Data Acquisition
• Time and resources
• Interpretation of Results
• High error susceptibility
• Difficult to debug

9. APPLICATIONS

10. CONCLUSION
• We are entering into the age of Machine
Learning, thus, making this domain impossible
to ignore and a lot to explore.
-THANKS