Machine learning ppt slideshare download
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Machine learning ppt slideshare download.Best Machine Learning PPT – Free Download
Machine learning and data mining MACHINE LEARNING DATA MINING Focuses on prediction, based on known properties learned from the training data. Machine learning ppt. Apr. 21, • 0 likes • views. Report. Download Now Download. Download to read offline.
[Machine learning ppt
They might be real-valued numbers, discrete-valued numbers, or categorical values.
Artificial Intelligence And Machine Learning PowerPoint Presentation …
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Successfully reported this slideshow. Your SlideShare is downloading. Machine Learning. Darshan Ambhaikar. Next SlideShares. You are reading a preview. Activate your 30 day free trial to continue reading. Continue for Free. Upcoming SlideShare. Lecture 1: What is Machine Learning? Building a performing Machine Macbine model from A to Z. Embed Size px. Start on. Show related SlideShares at end.
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Artificial intelligence and machine learning. Recently uploaded Mafhine Learning 1. Presented By:- Powerpoint download for windows S. Ambhaikar Sinhgad Institute of Management Pune 2. Definition 2. What is machine learning 3. Traditional programming and machine learning 4. Why machine learning is important 5. Generalization 6. Machine learning and data mining 7. Algorithms 8. Decision tree learning 9. Other learning techniques Examples Applications Few quotes Question and answers 3.
Introduction Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Mitchell provided a widely quoted, more formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure По этой ссылке, if its performance at tasks in T, as measured by P, improves with experience E” Definition Machine Learning 5.
So What Is Machine Learning? Generalization Focuses on the discovery of previously unknown properties on the data. Performance is usually evaluated with respect to the ability to machine learning ppt slideshare download known knowledge. The key task is the discovery of previously unknown knowledge.
Evaluated with respect to known knowledge This is the algorithm part of the data mining process Application of algorithms to search for patterns and relationships that may exist in large databases. Algorithms Algorithm types Machine learning algorithms can be organized based on the desired outcome of the algorithm or the type of input available during training the machine 1.
Supervised learning algorithms are trained on labeled examples, i. Unsupervised learning algorithms operate dosnload unlabelled examples, i. Semi-supervised learning combines both labeled and unlabelled examples to generate an appropriate function or classifier. Reinforcement learning is concerned with how intelligent agents ought to act in sliddshare environment to maximize some notion of reward from sequence of actions Other algorithms are: Learning to learn Developmental learning Transduction slieshare.
The process continues till we reach a leaf node. Artificial neural networks 2. Inductive logic programming 3. Support vector machines 4.
Bayesian networks 5. Reinforcement learning 6. Association Rule learning 7. Clustering Machine learning ppt slideshare download learning techniques After learning, it machine learning ppt slideshare download then be used downlkad classify new email messages into read and unread folders. Prediction of financial indices from textual news streams In this application news streams from 20 high-quality sources are digitized and used as the input to a neural network predictor of a financial time machine learning ppt slideshare download High explosives detector for airport security checkpoints Oil pipeline defect recognition machine learning ppt slideshare download quantification Adaptive performance control of iterative linear solver windows 10 32 bits iso download – windows 10 32 bits iso download Applications of Machine Learning Machine perception Computer vision object recognition Natural language processing Syntactic pattern recognition Search engines Medical diagnosis Bioinformatics Brain-machine interfaces Cheminformatics Detecting credit card fraud Stock market analysis Classifying DNA sequences Sequence mining Speech and handwriting recognition Game playing Software engineering Adaptive websites Robot locomotion Computational advertising Computational finance Structural health monitoring opinion mining Affective computing Information retrieval Recommender systems Pedestrian Detection References:- 1.
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