Introduction to Machine Learning.pptx
- 1. REGRESSION IN MACHINE LEARNING Dr. Amanpreet Kaur Associate Professor, Chitkara University, Punjab
- 2. AGENDA Introduction to Machine Learning Key Terminology Areas of Growth Applications Types of Machine Learning
- 3. INTRODUCTION TO MACHINE LEARNING Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to “learn” to calculate payroll Learning is used when: • Human expertise does not exist (navigating on Mars), • Humans are unable to explain their expertise (speech recognition) • Solution changes in time (routing on a computer network) • Solution needs to be adapted to particular cases (user biometrics) 3
- 4. • Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which • Machine learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. • Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. makes it more similar to humans: The ability to learn. • Machine learning is actively being used today, perhaps in many more places than one would expect. 4 INTRODUCTION TO MACHINE LEARNING
- 5. • Traditional Programming 5 Data Program Computer Output • Machine Learning Computer Data Output Program INTRODUCTION TO MACHINE LEARNING
- 6. KEY TERMINOLOGY Labels A label is the thing we're predicting—the y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything. Features A feature is an input variable—the x variable in simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as: x1,x2,...xN Models A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with "spam". 6
- 7. GROWTH OF MACHINE LEARNING Machine learning is preferred approach to – Speech recognition, Natural language processing – Computer vision – Medical outcomes analysis – Robot control – Computational biology This trend is accelerating – Improved machine learning algorithms – Improved data capture, networking, faster computers – Software too complex to write by hand – New sensors / IO devices 7
- 8. APPLICATIONS • Web search • Computational biology • Finance • E-commerce • Space exploration • Robotics • Information extraction • Social networks • Debugging software 8
- 9. HOW MACHINE LEARNING WORKS ? 9 https://www.guru99.com/machine-learning-tutorial.html
- 10. LEARNING PHASE In Learning Phase, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem. The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model. 10
- 12. • When the model is built, it is possible to test how powerful it is on never-seen- before data. • The new data are transformed into a features vector, go through the model and give a prediction. • This is all the beautiful part of machine learning.There is no need to update the rules or train again the model. • You can use the model previously trained to make inference on new data. 12 Inference Phase
- 13. TYPES OF MACHINE LEARNING (ML) 13
- 14. SUPERVISED MACHINE LEARNING • The process of algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. • The possible outcomes are already known and training data is also labeled with correct answers. • The algorithm generates a function that maps inputs to desired outputs. • One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. 14
- 15. Suppose we have input variables x and an output variable y and we applied an algorithm to learn the mapping function from the input to output such as − Y = F(X) Now, the main goal is to approximate the mapping function so well that when we have new input data (x), we can predict the output variable (Y) for that data. Mainly supervised leaning problems can be divided into the following two kinds of problems − 1. Classification − A problem is called classification problem when we have the categorized output such as “black”, “teaching”, “non-teaching”, etc. 2. Regression − A problem is called regression problem when we have the real value output such as “distance”, “kilogram”, etc.Decision tree, random forest, knn, logistic regression are the examples of supervised machine learning algorithms. 15 SUPERVISED MACHINE LEARNING
- 16. • This algorithms do not have any supervisor to provide any sort of guidance. • That is why unsupervised machine learning algorithms are closely aligned with what some call true artificial intelligence • we have input variable x, then there will be no corresponding output variables as there is in supervised learning algorithms. • In unsupervised learning there will be no correct answer and no teacher for the guidance. Algorithms help to discover interesting patterns in data. 16 Unsupervised Machine Learning
- 17. Unsupervised learning problems can be divided into the following two kinds of problem − 1. Clustering − In clustering problems, we need to discover the inherent groupings in the data. For example, grouping customers by their purchasing behavior. 2. Association − A problem is called association problem because such kinds of problem require discovering the rules that describe large portions of our data. For example, finding the customers who buy both x and y. 17 Unsupervised Machine Learning
- 18. REINFORCEMENT MACHINE LEARNING • Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty. • The agent learns automatically using feedbacks without any labeled data, unlike supervised learning. • There is no labeled data, so the agent is bound to learn by its experience only. RL solves a specific type of problem where decision making is sequential, and the goal is long-term, such as game-playing, robotics, etc. • The agent interacts with the environment and explores it by itself. The primary goal of an agent in reinforcement learning is to improve the performance by getting the maximum positive rewards. 18
- 19. REINFORCEMENT MACHINE LEARNING • Agent learns from trail and error • Environment where the Agents moves. • Actions where all possible steps that the agent can take. • States where current condition returned by the environment • Reward where an instant return from the environment. 19
- 20. ISSUES IN MACHINE LEARNING • Understanding Which Processes Need Automation • Beginning Without Good Data • Inadequate Infrastructure • Implementation • Lack of Skilled Resources 20
- 21. APPLICATIONS OF MACHINE LEARNING • Virtual Personal Assistants • Predictions while Commuting • Videos Surveillance • Social Media Services • Email Spam and Malware Filtering • Online Customer Support • Search Engine Result Refining • Product Recommendations • Online Fraud Detection 21
- 22. STEPS OF MACHINE LEARNING PROCESS 22
- 23. Building Machine Learning applications is an iterative process that involves a sequence of steps. To build an ML application, follow these general steps: • Frame the core ML problem(s) in terms of what is observed and what answer you want the model to predict. • Collect, clean, and prepare data to make it suitable for consumption by ML model training algorithms. Visualize and analyze the data to run sanity checks to validate the quality of the data and to understand the data. • Often, the raw data (input variables) and answer (target) are not represented in a way that can be used to train a highly predictive model. Therefore, you typically should attempt to construct more predictive input representations or features from the raw variables. • Feed the resulting features to the learning algorithm to build models and evaluate the quality of the models on data that was held out from model building. • Use the model to generate predictions of the target answer for new data instances. 23 STEPS OF MACHINE LEARNING PROCESS
- 24. REFERENCES E Books- Peter Harrington “Machine Learning In Action”, DreamTech Press Ethem Alpaydın, “Introduction to Machine Learning”, MIT Press Video Links- https://www.youtube.com/watch?v=BRMS3T11Cdw&list=PL3pGy4 HtqwD2a57wl7Cl7tmfxfk7JWJ9Y https://www.youtube.com/watch?v=EWmCkVfPnJ8&list=PL3pGy4H tqwD2a57wl7Cl7tmfxfk7JWJ9Y&index=3 24