Unit 1 Introduction to Machine Learning Concept
- 1. UNIT 1 INTRODUCTION TO ML (MACHINE LEARNING) PREPARED BY: PROF. ARVIND MENIYA
- 2. UNIT 1 Introduction to ML - By Arvind Meniya 2 What is Machine Learning (ML) ? • Machine Learning is a subset of artificial intelligence that is mainly concerned with the development of algorithms. • These algorithms allow a computer to learn from the data and past experiences on their own. • The term machine learning was first introduced by Arthur Samuel in 1959. • We can define it in a summarized way as: Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.
- 3. UNIT 1 Introduction to ML - By Arvind Meniya 3 What is Machine Learning (ML) ? • Machine learning constructs or uses the algorithms that learn from historical data. • The more we will provide the information, the higher will be the performance. • A machine has the ability to learn if it can improve its performance by gaining more data. • The first step in any project is defining your problem. • Even if the most powerful algorithm is used, the results will be meaningless if the wrong problem is solved.
- 4. UNIT 1 Introduction to ML - By Arvind Meniya 4 How ML Works? • The basic machine learning process can be divided into three parts. 1. Data Input: Past data or information is utilized as a basis for future decision-making 2. Abstraction: The input data is represented in a broader way through the underlying algorithm 3. Generalization: The abstracted representation is generalized to form a framework for making decisions
- 5. UNIT 1 Introduction to ML - By Arvind Meniya 5 How ML Works? General Diagram How does ML Works – Step by Step
- 6. UNIT 1 Introduction to ML - By Arvind Meniya 6
- 7. UNIT 1 Introduction to ML - By Arvind Meniya 7 Supervised • In Supervised Learning, the machine learns under supervision. • It contains a model that is able to predict with the help of a labeled dataset. • A labeled dataset is one where you already know the target answer. • Supervised learning can be further divided into two types: • Classification • Regression
- 8. UNIT 1 Introduction to ML - By Arvind Meniya 8 Supervised Classification Classification is used when the output variable is categorical i.e. with 2 or more classes. For example, yes or no, male or female, true or false, etc.
- 9. UNIT 1 Introduction to ML - By Arvind Meniya 9 Supervised Regression Regression is used when the output variable is a real or continuous value. In this case, there is a relationship between two or more variables i.e., a change in one variable is associated with a change in the other variable. For example, salary based on work experience or weight based on height, etc.
- 10. UNIT 1 Introduction to ML - By Arvind Meniya 10 Real life Application of Supervised Learning Risk Assessment Supervised learning is used to assess the risk in financial services or insurance domains. Image Classification Image classification is one of the key use cases of demonstrating supervised machine learning. For example, Facebook can recognize your friend in a picture from an album of tagged photos. Fraud Detection To identify whether the transactions made by the user are authentic or not. Visual Recognition The ability of a machine learning model to identify objects, places, people, actions, and images.