MLUP_ppt_1_ for project it help students who are eligible

Machine learning using python project presentation

1. MACHINE LEARNING USING PYTHON TOPIC: APPLICATION AND ENVIRONMENTAL-SETUP Present By:- chandan tarei (230301120427) Program-B.tech Section- H Branch-CSE Accademic Year – 2024-28

2. CONTENTS INTRODUCTION: MACHINE LEARNING TYPES OF MACHINE LEARNING DIFFERENCE BETWEEN AI AND ML APPLICATION OF MACHINE LEARNING

3. Machine learning helps computers learn from data. It finds patterns in information. It can make predictions. It works automatically without exact instructions. It improves with experience. MACHINE LEARNING:-

4. TYPES OF machine learning:- There are three types of Machine Learning:- Supervised Learning Unsupervised Learning Reinforcement Learning

5. Supervised Learning :- Supervised Learning is a type of machine learning where the computer learns from labeled data. This means the data has inputs and the correct output. The model tries to learn the relationship between input and output so it can predict results for new data. Example :- Predicting house prices based on features like area, bedrooms, and location. Input: House details Output: Price

6. Unsupervised Learning is a type of machine learning where the computer learns from unlabeled data. This means the data has no correct answers or outputs. The model tries to find patterns, groups, or relationships in the data on its own. Example :- Grouping customers based on shopping behavior (customer segmentation). Input: Customer data Output: Groups or clusters of similar customers unSupervised Learning :-

7. REINFORCEMENT:- Reinforcement Learning is a type of machine learning where the computer learns by trial and error. The model takes actions in an environment and gets rewards or penalties. Over time, it learns the best actions to get the most reward. Example: A robot learning to walk: it tries different moves and learns which ones keep it balanced. A game AI learning to win by practicing many games.

8. Difference Between AI and ML

9. Healthcare: Predict diseases, diagnose using medical images, drug discovery. Finance: Detect fraud, predict stock prices, credit scoring. Education: Personalized learning, student performance prediction. Transportation: Self-driving cars, traffic prediction, route optimization. Social Media: Content recommendations, sentiment analysis, face recognition. APPLICATION OF MACHINE LEARNING

10. THANK YOU…