Introduction to Artificial Intelligence And Machine Learning

Introduction to Machine Learning

1. Machine Learning

2. 2 Machine Learning and Artificial Intelligence

3. What is Machine learning ????

4. Machine Learning Definition: Machine Learning (ML) is a subset of artificial intelligence (AI) that involves the development of algorithms that allow computers to learn from and make decisions based on data. In simple words Machine Learning is like teaching computers to learn from experience, just like humans do. 4

5. Let us understand with an analogy Imagine you’re teaching a friend to recognize different types of fruits. You show them apples, bananas, and oranges, and they learn to tell them apart. Machine learning is similar, but instead of a person, we teach a computer. 5

6. Core Goal The goal of ML is to enable computers to improve their performance on tasks over time without being explicitly programmed.

7. Key Components 7 DATA: Raw information used to train the model. A LGORITHM : Mathematical models that process data. TRAINING: The process of feeding data to algorithms to help them learn patterns. MODEL: The output of the training process, used to make predictions or decisions

8. TYPES OF MACHINE LEARNING 8

9. Types of Machine learning 9 SUPERVISED LEARNING UN-SUPERVISED LEARNING REINFORCEMENT LEARNING

10. SUPERVISED LEARNING Supervised learning, also known as supervised machine learning, is a subcategory of  machine learning  and  artificial intelligence . It is defined by its use of labeled data sets to train algorithms that to classify data or predict outcomes accurately. In Simple Words, Learning With Labeled data Example: Predicating house prices based on Historical data 10

11. UN-SUPERVISED LEARNING Unsupervised learning, also known as  unsupervised machine learning , uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention. In Simple Words, Learning With unlabeled data Example: Clustering customers into different segment 11

12. Reinforcement LEARNING Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent takes actions in the environment, and based on these actions, it receives feedback in the form of rewards or penalties. The goal of the agent is to learn a policy that maximizes the cumulative reward over time. In Simple Words, Learning through trail and error Example: Training a robot to navigate a maze 12

13. Why do we use machine learning 13 Automation: Automates repetitive tasks, reducing the need for human intervention. Insights and Predictions: Analyzes vast amounts of data to uncover patterns and make predictions. Improvement Over Time: Models can improve their performance as they are exposed to more data.

14. BENEFITS OF MACHINE LEARNING 14 Efficiency: Processes large datasets quickly and accurately Scalability: Easily scales to handle increased amount of data Personalization: Enables personalized experiences in applications like recommendation systems ( eg. , Netflix, Amazon.)

15. Real-World Application 15 Healthcare: Disease prediction, personalized treatment plans. Finance: Fraud detection, algorithmic trading. Marketing: Customer segmentation, targeted advertising. Transportation: Autonomous vehicles, route optimization. Natural Language Processing (NLP): Chatbots, language translation.

16. Future of machine learning 16 Advancements: Continued improvement in algorithms and computing power Integration: Greater integration with IoT (Internet of Thing), big data, and cloud computing. Ethics and Regulation: Increased focus on ethical AI and regulatory frameworks.

17. CONCLUSION 17 ML is a powerful tool that enables computers to learn from data and make intelligent decisions. It is widely used across various industries to drive innovation and efficiency.

18. Thank you YUVA SAARATHII www.yuvasaarathi.com [email protected]