Machine learning is the intersection of statistics and computer science that allows systems to answer questions by learning from available data rather than through explicit programming. A machine learning model is trained on sample data to learn patterns and make predictions on new data. The accuracy of a machine learning model depends on the quality and quantity of training data as well as the robustness of the model. Machine learning is used in applications like speech recognition, fraud detection, spam filtering, search engines, and facial recognition. More data leads to stronger machine learning models that can tackle increasingly complex problems such as medical diagnosis, game playing, and self-driving vehicles.

1. Machine Learning
Presented By
J.Sajitha Burvin
Pg scholar

2. Introduction to machine learning

3. Cont..
• Machine Learning: Statistics+Computer science
• Definition:
Answering question with available information.
Available Information:->Training Data
Answering Questions:->Predictions
Machine
Learning Model
Training Data Predictions
Answering
Questions

4. Machine Learning Example
• Problem: Computer should identify dog vs.
human being
Training Data
Learns Feature
Find
Statistical
Patterns
Eg :Man has long
leg dog has tail etc..
Ml Model

5. Cont..
Accuracy Depends on:
1.Training Data
2.Robustness of ML model
Where is machine learning Model?
• Text to speech
• Credit Card Fraud Detection
• Spam Filter
• Google Search
• Facebook Face recognition
More Data
Stronger
ML model

6. Cont..
More complex problems
• Identify skin cancer
• Play board games
• Self driving car
Etc…