Introduction to machine learning

1. MACHINE LEARNING
By
Pooja Kumari

2. Introduction to Machine learning:
Machine learning is itself a type of artificial intelligence
Technique of training machines
Faster and better than an average human-being.
Machines can beat human champions in games.
Machines can be trained to perform human activities
Predicting outcomes without programming

3. Objectives of Machine Learning:
 To build algorithms that can receive input data and use statistics for
prediction of an output value.
 It provides the ability to automatically obtain deep insights
 It recognize unknown patterns, and create high performing predictive
models from data.

4. Benefits of Machine Learning:
 Powerful & Quicker Processing
 Better Decision Making & Prediction
 Accurate
 Affordable Data Management
 Inexpensive
 Analyzing Complex Big Data

6. Supervised learning:
 lesser amount of data
 clearly labelled data
 Direct feedback
 Predict outcome

7. Supervised learning:
label
label1
label3
label4
label5
labeled examples
examples

9. Unsupervised learning:
• No labelled data
• Better performance
• Large data sets.
• Hidden structure
• No feedback

10. Supervised Vs Unsupervised learning:

11. •A computer program will interact with a dynamic environment in
which it must perform a particular goal such as:
 playing a game with an opponent
 driving a car
• Provided feedback
• Decision process
• Rewards and punishments system.
Reinforcement learning:

12. THANKYOU