Linear Regression, Machine learning term
•
3 likes•653 views
This document discusses machine learning and linear regression. It provides an overview of supervised and unsupervised learning, with supervised learning using labeled training data to teach a computer a task. Linear regression is described as a method for modeling the linear relationship between a dependent variable and one or more independent variables. The goal of linear regression is to minimize a cost function that measures the difference between predicted and actual values by using a gradient descent algorithm to iteratively update the model's parameters.
Report
Share
Report
Share
Recommended
Introduction to Machine learning ppt
Introduction to Machine Learning : Machine Learning (ML) is a type of Intelligence (AI) that allows Software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning Algorithms use historical data as input to predict new output values.
Machine Learning Unit 4 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University AI Track. This is the ppt made by me for teaching ML in October 2020
What is Machine Learning
Machine learning is a method of data analysis that automates analytical model building. It allows systems to learn from data, identify patterns and make decisions with minimal human involvement. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Presentation on supervised learning
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Machine Learning
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
Supervised and unsupervised learning
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
Machine learning and linear regression programming
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Generative Adversarial Networks : Basic architecture and variants
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Recommended
Introduction to Machine learning ppt
Introduction to Machine Learning : Machine Learning (ML) is a type of Intelligence (AI) that allows Software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning Algorithms use historical data as input to predict new output values.
Machine Learning Unit 4 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University AI Track. This is the ppt made by me for teaching ML in October 2020
What is Machine Learning
Machine learning is a method of data analysis that automates analytical model building. It allows systems to learn from data, identify patterns and make decisions with minimal human involvement. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Presentation on supervised learning
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Machine Learning
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
Supervised and unsupervised learning
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
Machine learning and linear regression programming
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Generative Adversarial Networks : Basic architecture and variants
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Machine learning (ML) and natural language processing (NLP)
Short introduction on natural language processing (NLP) and machine learning (ML). Speaks about sub-areas of artificial inteligence and then mainly focuses on the sub-areas of machine learning and natural language processing. Explains the process of data mining from high perspective
Classification Based Machine Learning Algorithms
This slide focuses on working procedure of some famous classification based machine learning algorithms
Introduction to Machine Learning
This document provides an introduction to machine learning. It discusses how children learn through explanations from parents, examples, and reinforcement learning. It then defines machine learning as programs that improve in performance on tasks through experience processing. The document outlines typical machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type of learning and discusses evaluation methods for supervised learning models.
Introduction to-machine-learning
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
ML Basics
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University AI Track. This is the ppt made by me for teaching ML in August 2020
Machine Learning presentation.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Lecture 2: Preliminaries (Understanding and Preprocessing data)
This document provides an overview of key concepts in machine learning and data preprocessing. It discusses raw data and feature representation, including concepts, instances, attributes, and features. It also includes digressions on statistics such as measures of central tendency, normalization, and data visualization techniques. The document is a lecture on preliminaries for machine learning and language technology.
Unsupervised learning clustering
This document discusses unsupervised learning and clustering. It defines unsupervised learning as modeling the underlying structure or distribution of input data without corresponding output variables. Clustering is described as organizing unlabeled data into groups of similar items called clusters. The document focuses on k-means clustering, describing it as a method that partitions data into k clusters by minimizing distances between points and cluster centers. It provides details on the k-means algorithm and gives examples of its steps. Strengths and weaknesses of k-means clustering are also summarized.
Simple overview of machine learning
Machine learning is a branch of artificial intelligence concerned with algorithms that allow computers to learn from data without being explicitly programmed. The document discusses the history and evolution of machine learning from early work in the 1950s to recent advances. It covers different types of learning including supervised, unsupervised, semi-supervised, and reinforcement learning. Various machine learning algorithms and techniques are presented, such as decision trees, clustering, dimensionality reduction, and ensemble methods. Issues in machine learning like problem representation and theoretical limits are also addressed.
Learning On The Border:Active Learning in Imbalanced classification Data
This paper proposes using active learning to address the problem of class imbalance in machine learning classification tasks. The key ideas are:
1) Active learning selects the most informative examples to label, which tend to be instances closest to the decision boundary. This helps provide a more balanced sample to the learner.
2) An online support vector machine (SVM) algorithm is used to allow efficient integration of newly labeled examples without retraining on the entire dataset.
3) Early stopping criteria based on support vectors are introduced to determine when enough examples have been labeled.
Empirical results on imbalanced datasets demonstrate that the active learning approach leads to improved classification performance compared to traditional supervised learning methods.
Understanding Basics of Machine Learning
This document provides an overview of machine learning concepts including:
1. It defines data science and machine learning, distinguishing machine learning's focus on letting systems learn from data rather than being explicitly programmed.
2. It describes the two main areas of machine learning - supervised learning which uses labeled examples to predict outcomes, and unsupervised learning which finds patterns in unlabeled data.
3. It outlines the typical machine learning process of obtaining data, cleaning and transforming it, applying mathematical models, and using the resulting models to make predictions. Popular models like decision trees, neural networks, and support vector machines are also briefly introduced.
Learning machine learning with Yellowbrick
Yellowbrick is an open source Python library that provides visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process. For teachers and students of machine learning, Yellowbrick can be used as a framework for teaching and understanding a large variety of algorithms and methods.
Terminology Machine Learning
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
15857 cse422 unsupervised-learning
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
Understanding the Machine Learning Algorithms
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
Machine Learning and Real-World Applications
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Induction, Induction pipeline, Training set, test set, development set, Parameters, Hyperparameters, Accuracy, precision, recall, f-measure, Confusion matrix, Crossvalidation, Leave one out, Stratification
Intro to Machine Learning for non-Data Scientists
The document provides an overview of machine learning concepts including definitions, algorithms, and the machine learning pipeline. It discusses supervised and unsupervised learning algorithms like classification, regression, and clustering. It also describes steps in the machine learning pipeline such as data preparation, algorithm selection, model building, evaluation, and prediction. Examples of applications like spam filtering and recommendations are provided. The agenda outlines an introduction to machine learning algorithms and their implementation for different use cases.
Unit 3 – AIML.pptx
This document provides an overview of machine learning topics including linear regression, linear classification models, decision trees, random forests, supervised learning, unsupervised learning, reinforcement learning, and regression analysis. It defines machine learning, describes how machines learn through training, validation and application phases, and lists applications of machine learning such as risk assessment and fraud detection. It also explains key machine learning algorithms and techniques including linear regression, naive bayes, support vector machines, decision trees, gradient descent, least squares, multiple linear regression, bayesian linear regression, and types of machine learning models.
lec1.ppt
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
More Related Content
What's hot
Machine learning (ML) and natural language processing (NLP)
Short introduction on natural language processing (NLP) and machine learning (ML). Speaks about sub-areas of artificial inteligence and then mainly focuses on the sub-areas of machine learning and natural language processing. Explains the process of data mining from high perspective
Classification Based Machine Learning Algorithms
This slide focuses on working procedure of some famous classification based machine learning algorithms
Introduction to Machine Learning
This document provides an introduction to machine learning. It discusses how children learn through explanations from parents, examples, and reinforcement learning. It then defines machine learning as programs that improve in performance on tasks through experience processing. The document outlines typical machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type of learning and discusses evaluation methods for supervised learning models.
Introduction to-machine-learning
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
ML Basics
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University AI Track. This is the ppt made by me for teaching ML in August 2020
Machine Learning presentation.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Lecture 2: Preliminaries (Understanding and Preprocessing data)
This document provides an overview of key concepts in machine learning and data preprocessing. It discusses raw data and feature representation, including concepts, instances, attributes, and features. It also includes digressions on statistics such as measures of central tendency, normalization, and data visualization techniques. The document is a lecture on preliminaries for machine learning and language technology.
Unsupervised learning clustering
This document discusses unsupervised learning and clustering. It defines unsupervised learning as modeling the underlying structure or distribution of input data without corresponding output variables. Clustering is described as organizing unlabeled data into groups of similar items called clusters. The document focuses on k-means clustering, describing it as a method that partitions data into k clusters by minimizing distances between points and cluster centers. It provides details on the k-means algorithm and gives examples of its steps. Strengths and weaknesses of k-means clustering are also summarized.
Simple overview of machine learning
Machine learning is a branch of artificial intelligence concerned with algorithms that allow computers to learn from data without being explicitly programmed. The document discusses the history and evolution of machine learning from early work in the 1950s to recent advances. It covers different types of learning including supervised, unsupervised, semi-supervised, and reinforcement learning. Various machine learning algorithms and techniques are presented, such as decision trees, clustering, dimensionality reduction, and ensemble methods. Issues in machine learning like problem representation and theoretical limits are also addressed.
Learning On The Border:Active Learning in Imbalanced classification Data
This paper proposes using active learning to address the problem of class imbalance in machine learning classification tasks. The key ideas are:
1) Active learning selects the most informative examples to label, which tend to be instances closest to the decision boundary. This helps provide a more balanced sample to the learner.
2) An online support vector machine (SVM) algorithm is used to allow efficient integration of newly labeled examples without retraining on the entire dataset.
3) Early stopping criteria based on support vectors are introduced to determine when enough examples have been labeled.
Empirical results on imbalanced datasets demonstrate that the active learning approach leads to improved classification performance compared to traditional supervised learning methods.
Understanding Basics of Machine Learning
This document provides an overview of machine learning concepts including:
1. It defines data science and machine learning, distinguishing machine learning's focus on letting systems learn from data rather than being explicitly programmed.
2. It describes the two main areas of machine learning - supervised learning which uses labeled examples to predict outcomes, and unsupervised learning which finds patterns in unlabeled data.
3. It outlines the typical machine learning process of obtaining data, cleaning and transforming it, applying mathematical models, and using the resulting models to make predictions. Popular models like decision trees, neural networks, and support vector machines are also briefly introduced.
Learning machine learning with Yellowbrick
Yellowbrick is an open source Python library that provides visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process. For teachers and students of machine learning, Yellowbrick can be used as a framework for teaching and understanding a large variety of algorithms and methods.
Terminology Machine Learning
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
15857 cse422 unsupervised-learning
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
Understanding the Machine Learning Algorithms
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
Machine Learning and Real-World Applications
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Induction, Induction pipeline, Training set, test set, development set, Parameters, Hyperparameters, Accuracy, precision, recall, f-measure, Confusion matrix, Crossvalidation, Leave one out, Stratification
Intro to Machine Learning for non-Data Scientists
The document provides an overview of machine learning concepts including definitions, algorithms, and the machine learning pipeline. It discusses supervised and unsupervised learning algorithms like classification, regression, and clustering. It also describes steps in the machine learning pipeline such as data preparation, algorithm selection, model building, evaluation, and prediction. Examples of applications like spam filtering and recommendations are provided. The agenda outlines an introduction to machine learning algorithms and their implementation for different use cases.
What's hot (20)
Machine learning (ML) and natural language processing (NLP)
Machine learning (ML) and natural language processing (NLP)
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Machine Learning Unit 1 Semester 3 MSc IT Part 2 Mumbai University
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Lecture 2 Basic Concepts in Machine Learning for Language Technology
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Learning On The Border:Active Learning in Imbalanced classification Data
Learning On The Border:Active Learning in Imbalanced classification Data
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Similar to Linear Regression, Machine learning term
Unit 3 – AIML.pptx
This document provides an overview of machine learning topics including linear regression, linear classification models, decision trees, random forests, supervised learning, unsupervised learning, reinforcement learning, and regression analysis. It defines machine learning, describes how machines learn through training, validation and application phases, and lists applications of machine learning such as risk assessment and fraud detection. It also explains key machine learning algorithms and techniques including linear regression, naive bayes, support vector machines, decision trees, gradient descent, least squares, multiple linear regression, bayesian linear regression, and types of machine learning models.
lec1.ppt
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
Lecture 1
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
Artificial Intelligence Approaches
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
ML SFCSE.pptx
This document provides an introduction to machine learning for data science. It discusses the applications and foundations of data science, including statistics, linear algebra, computer science, and programming. It then describes machine learning, including the three main categories of supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms covered include logistic regression, decision trees, random forests, k-nearest neighbors, and support vector machines. Unsupervised learning methods discussed are principal component analysis and cluster analysis.
Lecture 5 machine learning updated
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
Intro to machine learning
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Machine learning
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
PREDICT THE FUTURE , MACHINE LEARNING & BIG DATA
Scopri come utilizzare Azure Machine Learning, un servizio cloud che consente alle aziende, università, centri di ricerca e sviluppatori di incorporare e sfrutturare nelle loro applicazioni funzionalità di apprendimento automatico e analisi predittiva su enormi set di dati. Tramite Azure ML Studio possiamo creare, testare, attuare e gestire soluzioni di analisi predittiva e apprendimento automatico nel cloud tramite un qualunque web browser. Durante la sessione si darà un saggio attraverso un esempio di analisi predittiva sul Flight Delay.
Net campus2015 antimomusone
The document provides an overview of Azure Machine Learning and discusses machine learning concepts. It begins with introducing the speaker and providing an agenda. It then defines machine learning and contrasts traditional programming with machine learning. Different types of learning methods like supervised and unsupervised learning are also introduced. Finally, it demonstrates the Azure Machine Learning workflow and some common machine learning algorithms available in Azure.
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.Week_1 Machine Learning introduction.pptx
This document provides information about the Machine Learning course EC-452 offered in Fall 2023. It is a 3 credit elective course that can be taken by DE-42 (Electrical) students in their 7th semester. Assessment will include a midterm exam, final exam, quizzes and assignments. Topics that will be covered include introduction to machine learning, applications of machine learning, common understanding of machine learning concepts and jargon, supervised learning workflow and notation, data representation, hypothesis space, classes of machine learning algorithms, and algorithm categorization schemes.
Introduction to Machine Learning
This document provides an introduction to machine learning, including definitions, types of machine learning problems, common algorithms, and typical machine learning processes. It defines machine learning as a type of artificial intelligence that enables computers to learn without being explicitly programmed. The three main types of machine learning problems are supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning. Common machine learning algorithms and examples of their applications are also discussed. The document concludes with an overview of typical machine learning processes such as selecting and preparing data, developing and evaluating models, and interpreting results.
unit 1.2 supervised learning.pptx
Supervised learning is a fundamental concept in machine learning, where a computer algorithm learns from labeled data to make predictions or decisions. It is a type of machine learning paradigm that involves training a model on a dataset where both the input data and the corresponding desired output (or target) are provided. The goal of supervised learning is to learn a mapping or relationship between inputs and outputs so that the model can make accurate predictions on new, unseen data.v
Building High Available and Scalable Machine Learning Applications
This document discusses building high available and scalable machine learning products. It begins with an introduction to data-driven products and machine learning concepts like supervised and unsupervised learning. It then discusses six key challenges in building machine learning products at iyzico: 1) models need testing on real data before production, 2) response times must be under 0.1 seconds, 3) data is dynamic, 4) high availability and fail fast is required, 5) continuous delivery of machine learning models, and 6) simulating aggregated features from batch data. It provides examples of techniques used at iyzico to address these challenges like Spark for predictions, schemaless databases, circuit breakers, devops for machine learning, and Redis for
Machine learning
This document provides an overview of machine learning techniques using R. It discusses regression, classification, linear models, decision trees, neural networks, genetic algorithms, support vector machines, and ensembling methods. Evaluation metrics and algorithms like lm(), rpart(), nnet(), ksvm(), and ga() are presented for different machine learning tasks. The document also compares inductive learning, analytical learning, and explanation-based learning approaches.
Unit-V Machine Learning.ppt
Machine learning, deep learning, and artificial intelligence are summarized. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. Deep learning uses neural networks with many layers to learn representations of data with multiple levels of abstraction. Artificial intelligence is the broader field of building intelligent machines that can think and act like humans. Supervised, unsupervised, semi-supervised and reinforcement learning techniques are described along with common applications such as classification, clustering, recommendation systems, and game playing.
4.1.pptx
Supervised learning is the most common form of machine learning where algorithms learn from labeled examples in the training data. The goal is to predict the correct labels for new unseen data based on patterns learned from the labeled training data. Supervised learning problems are classified as either classification, where the goal is to assign class labels, or regression, where the goal is to predict a continuous value. Learning curves can help identify if a model is underfitting or overfitting the data by examining the training and validation error over time.
in5490-classification (1).pptx
This document provides an overview of machine learning techniques for classification with imbalanced data. It discusses challenges with imbalanced datasets like most classifiers being biased towards the majority class. It then summarizes techniques for dealing with imbalanced data, including random over/under sampling, SMOTE, cost-sensitive classification, and collecting more data. [/SUMMARY]
Similar to Linear Regression, Machine learning term (20)
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...
Building High Available and Scalable Machine Learning Applications
Building High Available and Scalable Machine Learning Applications
Recently uploaded
socradar-q1-2024-aviation-industry-report.pdf
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Presentation from Ghazal Aakel and Eric Jochum at the GSD Community Stage Meetup on June 6th, 2024
Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
Charla impartida en el evento de "KuberTENes Birthday Bash Guadalajara" para celebrar el 10mo. aniversario de Kubernetes #kuberTENes #celebr8k8s #k8s
Artificia Intellicence and XPath Extension Functions
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Empowering Growth with Best Software Development Company in Noida - Deuglo
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Transform Your Communication with Cloud-Based IVR Solutions
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
https://2024.springio.net/sessions/automated-software-refactoring-with-openrewrite-and-generative-ai/
E-commerce Development Services- Hornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Unveiling the Advantages of Agile Software Development.pdf
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Using Xen Hypervisor for Functional Safety
An update on making Xen hypervisor functionally safe and enhancing its usage in automotive and industrial use cases
WWDC 2024 Keynote Review: For CocoaCoders Austin
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国纽约州立大学奥尔巴尼分校毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
UI5con 2024 - Bring Your Own Design System
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
Using Query Store in Azure PostgreSQL to Understand Query Performance
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Microservice Teams - How the cloud changes the way we work
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Recently uploaded (20)
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Empowering Growth with Best Software Development Company in Noida - Deuglo
Empowering Growth with Best Software Development Company in Noida - Deuglo
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
Microservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we work
Linear Regression, Machine learning term
- 1. S.Rulez Study 1 Machine Learning / Linear Regression
- 2. Machine Learning • Limitations of explicit programming • Spam filter: many rules • Automatic driving: too many rules • Machine learning "Field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel (1959)
- 3. Supervised/Unsupervised learning • Supervised learning • Teach the computer how to do something, then let it use it;s new found knowledge to do it • Unsupervised learning • Let the computer learn how to do something, and use this to determine structure and patterns in data
- 4. Supervised learning • Given the “right answer” for each example in the data. • learning with labeled examples - training set
- 5. Unsupervised learning • In unsupervised learning, we get unlabeled data • Just told - here is a data set, can you structure it • To cluster data into to groups - clustering algorithm • Example of clustering algorithm • Google news • Genomics • Organize computer clusters • Social network analysis - Customer data
- 6. Types of supervised learning • Predicting final exam score based on time spent • regression • Pass/non-pass based on time spent • binary classification • Letter grade (A, B, C, E and F) based on time spent • multi-label classification
- 7. Regression • a statistical process for estimating the relationships among variables. • 관찰된 연속형 변수들에 대해 두 변수 사이의 모형을 구한 뒤 적합도를 측 정해 내는 분석 방법
- 8. Linear Regression • 종속 변수 y와 한 개 이상의 독립 변수 X와의 선형 상관 관계를 모델링 • 독립 변수는 입력값이나 원인, 종속 변수는 결과물이나 효과를 뜻함
- 11. Hypothesis • It is like your prediction machine, throw in an x value, get a putative y value
- 12. Cost function • How fit the line to our (training) data • 기대치와 실제 값의 차이를 구하면 되나, 계산의 편의를 위해 차이의 제곱을 구함 • 각 값의 차이의 제곱의 합을 나누어 평균을 구한다.
- 15. What cost(W) looks like?
- 16. How to minimize cost?
- 17. Gradient descent algorithm • Minimize cost function • Gradient descent is used many minimization problems • For a given cost function, cost (W, b), it will find W, b to minimize cost • It can be applied to more general function: cost (w1, w2, …)
- 19. Formal definition Gradient descent algorithm