Machine Learning
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Machine learning (ML) is the study of algorithms and statistical models that allow computer systems to perform tasks without explicit instructions by relying on patterns and inference. ML algorithms build mathematical models from sample "training data" to make predictions and decisions without being programmed for specific tasks. These models automatically analyze large datasets to identify the most relevant information, which can then be used to automatically make predictions and help people make faster, more accurate decisions.
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Types of machine learning
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models to make predictions, unsupervised learning finds patterns in unlabeled data through clustering, and reinforcement learning allows agents to learn behaviors through rewards and punishments from its environment.
Machine learning
This document provides an overview of machine learning. It discusses the history of machine learning beginning in 1957 with the first neural network. It defines machine learning as using algorithms and statistical models to perform tasks without explicit instructions by learning from patterns in data. Supervised learning uses labeled training data to guide model training, and is used for classification and regression problems. Unsupervised learning finds patterns in unlabeled data using clustering. Reinforcement learning involves an agent interacting with an environment and receiving rewards or penalties to learn the best outcomes. Popular machine learning software libraries and applications are also mentioned.
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This document discusses different machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled data to generate predictions, unsupervised learning finds patterns in unlabeled data through clustering and visualization, semi-supervised learning combines labeled and unlabeled data, and reinforcement learning uses rewards to learn behaviors. The document provides examples of applications for each type of learning such as price prediction, image clustering, and autonomous vehicles.
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The document provides an introduction to supervised machine learning, including definitions, techniques, and applications. It discusses how supervised machine learning involves training algorithms using labeled input data to make predictions on unlabeled data. Some common supervised learning algorithms mentioned are naive Bayes, decision trees, linear regression, support vector machines, and neural networks. Applications discussed include self-driving cars, online recommendations, fraud detection, and spam filtering. The key difference between supervised and unsupervised learning is that supervised learning uses labeled training data while unsupervised learning does not have pre-existing labels.
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Machine learning and types
Machine learning, types of machine learning, supervised, unsupervised and reinforcement machine learning
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- 1. PRESENTATION ON MACHINE LEARNING Presented By: Priyanka Kornule
- 2. • Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. • Machine learning algorithms build a mathematical model of sample data, known as “Training data", in order to make predictions or decisions without being explicitly programmed to perform the task. • These enable a computer program to automatically analyze a large body of data and decide what information is most relevant. This crystallized information can then be used to automatically make predictions or to help people make decisions faster and more accurately
- 5. Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
- 6. The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes. Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.
- 7. It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.