An basic introduction of Machine learning and a kick start to model building process using Linear Regression. Covers fundamentals of Data Science field called Machine Learning covering the fundamental topic of supervised learning method called linear regression. Importantly it covers this using R language and throws light on how to interpret linear regression results of a model. Interpretation of results , tuning and accuracy metrics like RMSE Root Mean Squared Error are covered here.
This document provides an introduction to machine learning, including definitions, key concepts, and algorithms. It defines machine learning as giving computers the ability to learn without being explicitly programmed. It distinguishes machine learning from artificial intelligence and describes supervised and unsupervised learning. Popular machine learning algorithms like naive Bayes, support vector machines, and decision trees are introduced. Python libraries for machine learning like scikit-learn are also mentioned.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
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 The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
This document provides an introduction to machine learning techniques presented by Dr. Radhey Shyam. It begins with definitions of machine learning and discusses when machine learning is applicable. The document then covers types of learning problems, designing learning systems, the history of machine learning, function representation techniques, search algorithms, and evaluation parameters. It also introduces several machine learning approaches and discusses common issues in machine learning.
This document provides an overview of machine learning. It begins by defining machine learning as improving performance on some task based on experience. Traditional programming is distinguished from machine learning by how the computer learns. Sample applications are discussed such as web search, computational biology, and robotics. Classic examples of machine learning tasks are discussed like playing checkers and recognizing handwritten words. The document then covers state of the art applications like autonomous vehicles, deep learning, and speech recognition. Different types of learning are introduced like supervised, unsupervised, and reinforcement learning. Finally, the document discusses designing a learning system by choosing the training experience, representation, learning algorithm, and evaluation method.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This document provides an introduction to machine learning, including definitions, key concepts, and algorithms. It defines machine learning as giving computers the ability to learn without being explicitly programmed. It distinguishes machine learning from artificial intelligence and describes supervised and unsupervised learning. Popular machine learning algorithms like naive Bayes, support vector machines, and decision trees are introduced. Python libraries for machine learning like scikit-learn are also mentioned.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
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.
This document provides an introduction to machine learning techniques presented by Dr. Radhey Shyam. It begins with definitions of machine learning and discusses when machine learning is applicable. The document then covers types of learning problems, designing learning systems, the history of machine learning, function representation techniques, search algorithms, and evaluation parameters. It also introduces several machine learning approaches and discusses common issues in machine learning.
This document provides an overview of machine learning. It begins by defining machine learning as improving performance on some task based on experience. Traditional programming is distinguished from machine learning by how the computer learns. Sample applications are discussed such as web search, computational biology, and robotics. Classic examples of machine learning tasks are discussed like playing checkers and recognizing handwritten words. The document then covers state of the art applications like autonomous vehicles, deep learning, and speech recognition. Different types of learning are introduced like supervised, unsupervised, and reinforcement learning. Finally, the document discusses designing a learning system by choosing the training experience, representation, learning algorithm, and evaluation method.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
1. Machine learning is a branch of artificial intelligence concerned with algorithms that allow computers to learn from data without being explicitly programmed.
2. A major focus is automatically learning patterns from training data to make intelligent decisions on new data. This is challenging since the set of all possible behaviors given all inputs is too large to observe completely.
3. Machine learning is applied in areas like search engines, medical diagnosis, stock market analysis, and game playing by developing algorithms that improve automatically through experience. Decision trees, Bayesian networks, and neural networks are common algorithms.
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Lecture #1: Introduction to machine learning (ML)butest
1. Machine learning (ML) is a subfield of artificial intelligence concerned with building computer programs that learn from data and improve their abilities to perform tasks.
2. ML programs build models from example data to predict future examples or describe relationships in the data. For example, an ML program given patient cases could predict diseases in new patients or describe relationships between diseases and symptoms.
3. There are different types of learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning (sequential decision making). The goal is to learn patterns in data and generalize to new examples.
Machine Learning and Real-World ApplicationsMachinePulse
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.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This document discusses machine learning and provides examples of common machine learning algorithms. It begins with definitions of machine learning and the machine learning process. It then describes four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and discusses five common algorithms - K-nearest neighbors, linear regression, decision trees, naive Bayes, and support vector machines. It concludes with an overview of a heart disease prediction mini-project using Python.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
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.
Introduction to machine learningunsupervised learningSardar Alam
The document provides an introduction to machine learning and discusses different types of machine learning algorithms including supervised and unsupervised learning. It provides examples of problems that could be addressed using supervised learning like regression to predict housing prices and classification to detect cancer. Unsupervised learning is used to discover hidden patterns in unlabeled data like grouping customer accounts or news articles.
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.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
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.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
This document provides an overview of machine learning. It defines machine learning as using algorithms to allow computers to learn from empirical data. The document outlines different machine learning models, algorithms, and techniques including supervised learning, unsupervised learning, performance factors, and applications. It concludes that machine learning will become increasingly important and be applied to more solutions.
Application of machine learning in industrial applicationsAnish Das
The group will present an introduction to machine learning, the basics of machine learning, and applications of machine learning in industry such as product categorization, improving the accuracy of inertial measurement units using supervised machine learning, data mining techniques, and machine learning for medical diagnosis. They will also discuss the future scope of machine learning.
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
1. Machine learning is a branch of artificial intelligence concerned with algorithms that allow computers to learn from data without being explicitly programmed.
2. A major focus is automatically learning patterns from training data to make intelligent decisions on new data. This is challenging since the set of all possible behaviors given all inputs is too large to observe completely.
3. Machine learning is applied in areas like search engines, medical diagnosis, stock market analysis, and game playing by developing algorithms that improve automatically through experience. Decision trees, Bayesian networks, and neural networks are common algorithms.
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Lecture #1: Introduction to machine learning (ML)butest
1. Machine learning (ML) is a subfield of artificial intelligence concerned with building computer programs that learn from data and improve their abilities to perform tasks.
2. ML programs build models from example data to predict future examples or describe relationships in the data. For example, an ML program given patient cases could predict diseases in new patients or describe relationships between diseases and symptoms.
3. There are different types of learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning (sequential decision making). The goal is to learn patterns in data and generalize to new examples.
Machine Learning and Real-World ApplicationsMachinePulse
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.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This document discusses machine learning and provides examples of common machine learning algorithms. It begins with definitions of machine learning and the machine learning process. It then describes four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and discusses five common algorithms - K-nearest neighbors, linear regression, decision trees, naive Bayes, and support vector machines. It concludes with an overview of a heart disease prediction mini-project using Python.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
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.
Introduction to machine learningunsupervised learningSardar Alam
The document provides an introduction to machine learning and discusses different types of machine learning algorithms including supervised and unsupervised learning. It provides examples of problems that could be addressed using supervised learning like regression to predict housing prices and classification to detect cancer. Unsupervised learning is used to discover hidden patterns in unlabeled data like grouping customer accounts or news articles.
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.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
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.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
This document provides an overview of machine learning. It defines machine learning as using algorithms to allow computers to learn from empirical data. The document outlines different machine learning models, algorithms, and techniques including supervised learning, unsupervised learning, performance factors, and applications. It concludes that machine learning will become increasingly important and be applied to more solutions.
Application of machine learning in industrial applicationsAnish Das
The group will present an introduction to machine learning, the basics of machine learning, and applications of machine learning in industry such as product categorization, improving the accuracy of inertial measurement units using supervised machine learning, data mining techniques, and machine learning for medical diagnosis. They will also discuss the future scope of machine learning.
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
This document provides an overview of machine learning concepts and techniques including linear regression, logistic regression, unsupervised learning, and k-means clustering. It discusses how machine learning involves using data to train models that can then be used to make predictions on new data. Key machine learning types covered are supervised learning (regression, classification), unsupervised learning (clustering), and reinforcement learning. Example machine learning applications are also mentioned such as spam filtering, recommender systems, and autonomous vehicles.
This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.
A business level introduction to Artificial Intelligence - Louis Dorard @ PAP...PAPIs.io
This document provides an overview of artificial intelligence and machine learning. It discusses how machine learning works using data and examples to build intelligence. Examples of everyday and business uses of machine learning are presented, such as predicting property prices, email spam detection, and demand forecasting. The document outlines the types of analytics that can be performed, from descriptive to predictive to prescriptive. It also discusses how machine learning models are developed and deployed through predictive APIs.
This document provides an overview of machine learning algorithms and their applications in the financial industry. It begins with brief introductions of the authors and their backgrounds in applying artificial intelligence to retail. It then covers key machine learning concepts like supervised and unsupervised learning as well as algorithms like logistic regression, decision trees, boosting and time series analysis. Examples are provided for how these techniques can be used for applications like predicting loan risk and intelligent loan applications. Overall, the document aims to give a high-level view of machine learning in finance through discussing algorithms and their uses in areas like risk analysis.
This document provides an introduction to machine learning, including definitions, types, and case studies. It begins with an agenda and overview of artificial intelligence applications. It then defines machine learning as a field that allows computers to learn without being explicitly programmed. The main types of machine learning are described as supervised, unsupervised, semi-supervised, and reinforcement learning. Example case studies on Netflix recommendations, cancer diagnosis, and Amazon inventory are outlined. The document concludes with tips on prerequisites and resources for studying machine learning, including mathematics, programming tools, and course recommendations.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Using Python library such as numpy, scipy and pandas to carry out supervised learning operations like Support vector machine, decision tree and K-nearest neighbor.
Ever wondered what factors influence house prices? This project explores the world of house price prediction using data science techniques. We delve into analyzing real estate data to build models that can estimate the value of a home. This can be a valuable tool for both buyers and sellers navigating the housing market. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more details
This project presents a machine learning approach to predicting house prices using a dataset containing various features such as the size of the house, number of bedrooms, location, and others. The project aims to build a predictive model that can accurately estimate the selling price of a house based on its features. The presentation covers data preprocessing steps, feature selection techniques, and the application of machine learning algorithms such as linear regression or decision trees. It also discusses model evaluation metrics and the potential impact of the model on the real estate industry. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting user demographics in social networks - Invited Talk at University ...Nikolaos Aletras
Automatically inferring user demographics in social networks is useful for both social science research and a range of downstream applications in marketing and politics. Our main hypothesis is that language use in social networks is indicative of user attributes. This talk presents recent work on inferring a new set of socioeconomic attributes, i.e. occupational class, income and socioeconomic class. We define a predictive task for each attribute where user-generated content is utilised to train supervised non-linear methods for classification and regression, i.e. Gaussian Processes. We show that our models achieve strong predictive accuracy in all of the three demographics while our analysis sheds light to factors that differentiate users between occupations, income level and socioeconomic classes.
Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
This document provides an introduction to analytics. It discusses how analytics uses data, information technology, statistical analysis and models to help managers make better decisions. Some potential applications of analytics discussed include pricing, customer segmentation, merchandising and location selection. The document also discusses descriptive, predictive and prescriptive analytics and some common analytics tools and challenges. It provides an overview of how analytics can be used to solve business problems.
Market Basket Analysis Revisited using SQL Pattern Matching Shankar Somayajula
This document provides an overview of market basket analysis and pattern matching. It discusses typical use cases for pattern matching in various industries. It then covers the basics of traditional market basket analysis (MBA) including common metrics like support, confidence and lift. The document proposes ways to revisit MBA, including augmenting transaction data with tags and analyzing rules using additional metrics and sequential patterns. It outlines considerations for the design of an MBA system and the typical architecture/process flow.
This document provides an overview of data analytics and time series analysis. It discusses the importance of topics like data science, analytics, and analysis. It covers key concepts in data analytics including data types, storage, visualization, and big data. It also discusses machine learning, predictive modeling techniques, and the typical lifecycle of a machine learning project including obtaining data, data cleaning, exploration, modeling, and interpreting results. The goal is to understand and discover useful insights from data to support decision making.
Similar to Introduction to machine learning and model building using linear regression (20)
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
2. Introducing the Speaker
• Girish Gore : 10+Years of Experience in Data Analytics / Data Science
• B.E. Computer Science fromVIT Pune , M.S. from BITS Pilani
• SpentTime on Data Products Mainly In companies like
• Cognizant (InnovationsGroup)
• SAS (Pricing & Revenue Management)
• VuClip (Video Entertainment)
• Shoptimize (E-Commerce)
• Worked in fields like
• Text Mining
• Forecasting and Optimization
• Recommender Systems
4. UnderstandingTerminologies
Artificial Intelligence
AI involves machines that can perform tasks that are characteristic of human
intelligence.
Machine Learning
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.
Deep Learning
Deep Learning is an attempt to mimic the workings of the brain. Deep
Learning is one of many approaches to machine learning
6. Traditional Programming vs Machine Learning
• If Programming automates processes ,
Machine Learning automates Program
generation i.e. Automation.
• Data and output is run on the computer to
create a program.This program can be used
in traditional programming
7. What is Machine Learning ?
• Machine Learning is
• study of algorithms that
• improve their performance at a particular task
• with experience ( previous data , output)
• Optimize a performance criterion using example data or past experience
• Role of Computer Science : Efficient Algorithms
• Solve the optimization problem
• Represent and Evaluate the model for inference
8. Why are we here Now !!! GoogleTrends !!
• Exponential increase in Data generation , accumulation
• Increasing computational power
• Growing progress in available algorithms and Research
• Software becoming too complex to write by hand
9. Common Applications of Machine Learning
• Web search: ranking page based on what you are most likely to click on.
• Finance: decide who to send what credit card offers to. Evaluation of risk on credit
offers. How to decide where to invest money.
• E-commerce: Predicting customer churn.Whether or not a transaction is fraudulent.
• Robotics: how to handle uncertainty in new environments.Autonomous. Self-driving car.
• Information extraction:Ask questions over databases across the web.
• Social networks: Data on relationships and preferences. Machine learning to extract value
from data.
• Debugging: Use in computer science especially in Labor intensive processes like
debugging. Could suggest where the bug could be
• Gaming, IBMWatson
10. Types Of Machine Learning
• Learning Associations
• Supervised Learning
• Regression
• Classification
• Un Supervised Learning
• Reinforcement Learning
• Semi supervised Learning
• Training data includes a few desired outputs. Between supervised and un supervised
11. Learning Associations
• Market Basket analysis:
P (Y | X ) probability that somebody who buys X also buys Y where X and Y
are products/services.
Example: P ( diaper| beer ) = 0.7
TransactionID BasketItems
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper,Coke
12. Learning Associations
• Support : The probability of the customer buying diaper and beer together
among all sales transactions (Higher support the better)
• Confidence : Suppose that if a customer pick up diaper. How he/she is likely
to buy beer? (Closer to 1 better)
• Lift : Lift is a true comparison between naive model and our model,
meaning that how more likely a customer buy both, compared to buy
separately? (Lift > 1)
13. Supervised Learning
• Supervised Learning is a Machine Learning task of inferring a generalized function
from labelled training data. Training data includes desired outputs.
Example: Spam Detection , Credit Scoring , Face Detection
• In Supervised Learning for spam detection we have
• Email Contents with Labels marking Spam or Non Spam
• Task is to label newer emails
• Main two types of Supervised Learning Problems
• Regression
• Classification
14. Supervised Learning
• Regression Problems
• Maps input data to a continuous prediction variable
• Example: Predicting Retail house prices (Price as continues variable)
• Classification Problems
• Maps input data to a set of predefined classes
• Example: Benign or MalignantTumours
15. Regression : House Price Prediction
• We have historic data about size of house and the price for last 1 year
• Task is to predict the Price of House given its size
•Model Derivation:
Price = Slope of Line * Size + Constant
16. Classification : Credit Scoring
We have labelled data of low and high risk customers.
Task is differentiating between low-risk and high-risk customers from their
income and savings.
Model Derivation:
IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
17. Un Supervised Learning
• Training data does not include desired output.
Task is to find hidden structure in unlabeled data
• CommonApproaches to Un Supervised Learning
• Clustering or Segmentation ( Customer Segmentation)
• Dimensionality Reduction ( PCA (Principal ComponentAnalysis) , SVD
(SingularValue Decomposition))
• Summarization
18. Un Supervised Learning
• Customer Segmentation: Help marketers discover distinct groups in their customer bases,
and then use this knowledge to develop targeted marketing programs.
• The clustering algorithm
forms 3 different groups of
customers to target.
19. Reinforcement Learning
• Learning from interaction with the environment to achieve a goal.
Rewards from a sequence of actions.
• Every Action has either a
• Reward OR
• Observation
• Examples
• Self Driving Cars
• Recommender Systems
•Stanford Research Link
https://www.cs.utexas.edu/~eladlieb/RLRG.html
22. Linear Regression
22
• In statistics, linear regression is an approach for modeling the
relationship between a scalar dependent variable y and one or more
explanatory variables (or independent variables) denoted X
• The case of one explanatory variable is called simple linear
regression
• For more than one explanatory
variable, the process is
called multiple linear regression
https://en.wikipedia.org/wiki/Linear_regression
23. From School Book :
Linear Equations
Y
Y = mX + b
b = Y-intercept
X
Change
in Y
Change in X
m = Slope
24. Linear Regression : A Common Example
24
Ohm’s Law:
• In physics, it is observed that the relationship between Voltage (V), Current (I)
and Resistance (R) is a linear relationship expressedas
V = I * R
I = V / R
• In a circuit board for a given Resistance R,
as you increase the VoltageV,
the Current I increases proprotionately
http://www.electronics-tutorials.ws/dccircuits/dcp_1.html
25. Sample Monthly Income-Expense Data of a Household
25
Monthly Income
(in Rs.)
Monthly Expense
(in Rs.)
5,000 8,000
6,000 7,000
10,000 4,500
10,000 2,000
12,500 12,000
14,000 8,000
15,000 16,000
18,000 20,000
19,000 9,000
20,000 9,000
20,000 18,000
22,000 25,000
23,400 5,000
24,000 10,500
24,000 10,000
We have to find the relationship between Income and Expenses
of a household
y = 0.3008x + 6319.1
R² = 0.4215
0
40000
30000
20000
10000
50000
60000
MonthlyExpense
Monthly Income
Income Vs. Expense
26. Line of Best Fit
26
0
10000
20000
30000
40000
50000
60000
MonthlyExpense
Monthly Income
IncomeVs.Expense
Which of these lines best
describe the relationship
between Household Income
and Expenses ?
27. 27
0
10000
20000
30000
40000
50000
60000
MonthlyExpense
Monthly Income
Income Vs. Expense
The Line of Best Fit will be the
one where Sum of Square of
Error (SSE) term will be
nique)
sample
on
)
)
get
Xi
X
b =
)2
ii
i i i i
nX -(
X Y
21
minimum (OLSTech
Err or (em = ym - ym)
Yi(hat) = bo + b1Xi isthe
regression equati
SSE = ei(hat
2 (1)
)
= (Yi -Y(i(hat))2 (2
= (Yi - bo - b1Xi)2 (3
Using calculus we
Error (en)
Yi -b1
bo =
n
n XY -
Line of Best Fit
28. Least Squares
• ‘Best Fit’ Means Difference Between ActualYValues & PredictedYValues is
a Minimum. But Positive Differences Off-Set Negative ones. So square
errors!
• LS Minimizes the Sum of the Squared Differences (errors) (SSE)
n
i
i
n
i
ii YY
1
2
1
2
ˆˆ
29. Simple Linear Regression in R
29
### CODE SNIPPET ###
?cars
# Investigating the basics of the data set
str(cars)
attributes(cars)
30. Examining the data
30
### CODE SNIPPET ###
# How speed and distance value summaries look. NA’s ?
summary(cars)
# Is there a correlation between speed and time to stop
cor(cars$speed, cars$dist)
31. Plotting the data
31
### CODE SNIPPET ###
plot(cars, main=“Distance between Speed and Distance to Stop”)
scatter.smooth(cars,lpars = list(col = "red", lwd = 3 , lty = 3))
boxplot(cars$dist, main="Outliers for Distance")
plot(density(cars$speed) , main="Density Distribution of Speed" ,
type="h",col="blue")
32. Basic Linear Model
32
### CODE SNIPPET ###
linear_model = lm(dist ~ speed , data=cars)
summary(linear_model)
33. CoefficientAnalysis
33
• Coefficient - Estimate
• Y intercept given is -17.5791
• Every 1 mph increase in the speed of a car, the required distance to stop goes up by 3.9324 feet.
• Coefficient - Standard Error
• The coefficient Standard Error measures the average amount that the coefficient estimates vary from
the actual average value of our response variable.We’d ideally want a lower number relative to its
coefficients.
• Coefficient - t value
• The coefficient t-value is a measure of how many standard deviations our coefficient estimate is far
away from 0.We want it to be far away from zero as this would indicate we could reject the null
hypothesis - that is, we could declare a relationship between speed and distance exist. In general, t-
values are also used to compute p-values.
• Coefficient - Pr(>t)
• A small p-value for the intercept and the slope indicates that we can reject the null hypothesis which
allows us to conclude that there is a relationship between speed and distance.
36. Residual Standard Error
36
• Residual Standard Error is measure of the quality of a linear
regression fit.
• The Residual Standard Error is the average amount that the response
(dist) will deviate from the true regression line.
• In our example, the actual distance required to stop can deviate from
the true regression line by approximately 15.3795867 feet, on
average. (Which is ~ 3.93 * 4 times)
• The Residual Standard Error was calculated with 48 degrees of
freedom. Simplistically, degrees of freedom are the number of data
points that went into the estimation of the parameters
37. Coefficient of Determination
• In statistics, the coefficient of determination, denoted R2 or r2 and pronounced
"R squared", is a number that indicates the proportion of the variance in the
dependent variable that is predictable from the independent variable(s)
• The R2 we get is 0.6511. Roughly 65% of the variance found in the response
variable (distance) can be explained by the predictor variable (speed)
• R2 value significance is relative to domain , Adjusted R2 used for multi linear
https://en.wikipedia.org/wiki/Coefficient_of_determination
38. F Statistics & PValue
• Indicator of whether there is a relationship between our predictor and the
response variables
• Greater than 1 suggests we can reject the null hypothesis : No relation between
speed and distance exists
• We can consider a linear model to be statistically significant only when both
these p-Values are less that the pre-determined statistical significance level,
which is ideally 0.05
40. What allWe did ?
• Examined the data
• Plotting the data
• Simple Linear Regression Model Creation
• Co efficient Analysis
• Residual Analysis
• R2 Analysis
• F Statistics
Is the current state of model good to be deployed /
used on live ?
41. Evaluation of Model : SplitTrain /Test
### CODE SNIPPET ###
## 80% of the sample size
sample_size <- floor(0.80 * nrow(cars))
## set the seed to make your partition reproductible
set.seed(123)
train_index <- sample(seq_len(nrow(cars)), size = sample_size)
train <- cars[ train_index, ]
test <- cars[-train_index, ]
linear_model_subset <- lm(dist ~ speed, data=train)
distPred <- predict(linear_model_subset, test)
summary(linear_model_subset)
plot(distPred, test$dist)
42. RMSE :To compare between models
### CODE SNIPPET ###
rmse <-function(error)
{
sqrt(mean(error^2))
}
print(rmse(test$dist - distPreds))
• RMSE : Root Mean Squared Error
• Average Distance between the observed values and the model predictions
OR
• How far are the residuals from zero
43. Food for thought !!!
Is the test / train split model the best
generalization we have ??
.. Covered in Upcoming Sessions