Machine Learning PPT BY RAVINDRA SINGH KUSHWAHA B.TECH(IT) CHAUDHARY CHARAN SINGH UNIVERSITY, MEERUT
- 1. Machine Learning Submitted To Neelam Ma’m Assistant Prof. SCRIET, Meerut Submitted By Ravindra Singh Kushwaha SCRIET, Meerut IT 8th sem SCRIET, Meerut
- 2. Machine Learning “Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.” Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which 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. A machine has the ability to learn if it can improve its performance by gaining more data.
- 4. How does Machine Learning work A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.
- 6. Features of Machine Learning Machine learning uses data to detect various patterns in a given dataset. It can learn from past data and improve automatically. It is a data-driven technology. Machine learning is much similar to data mining as it also deals with the huge amount of the data.
- 7. Need for Machine Learning The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.
- 8. Importance of Machine Learning Rapid increment in the production of data Solving complex problems, which are difficult for a human Decision making in various sector including finance Finding hidden patterns and extracting useful information from data.
- 10. Supervised Learning Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
- 11. How Supervised Learning Works?
- 13. Regression Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc. Below are some popular Regression algorithms which come under supervised learning: Linear Regression Regression Trees Non-Linear Regression Bayesian Linear Regression Polynomial Regression
- 14. Classification Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc. Spam Filtering, Random Forest Decision Trees Logistic Regression Support vector Machines
- 15. Unsupervised Machine Learning Unsupervised learning is a type of machine learning in which models are trained using unlabelled dataset and are allowed to act on that data without any supervision. Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format.
- 16. Working of Unsupervised Learning
- 18. Clustering Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
- 19. Association An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset. Association rule makes marketing strategy more effective. Such as people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.
- 20. Reinforcement Learning Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance. The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.
- 21. Thank You