Introduction to machine learning and model building using linear regressionGirish Gore
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.
Types of Machine Learning- Tanvir Siddike MoinTanvir Moin
Machine learning can be broadly categorized into four main types based on how they learn from data:
Supervised Learning: Imagine a teacher showing you labeled examples (like classifying pictures of cats and dogs). Supervised learning algorithms learn from labeled data, where each data point has a corresponding answer or label. The algorithm analyzes the data and learns to map the inputs to the desired outputs. This is commonly used for tasks like spam filtering, image recognition, and weather prediction.
Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. It's like being given a pile of toys and asked to organize them however you see fit. The algorithm finds hidden patterns or structures within the data. This is useful for tasks like customer segmentation, anomaly detection, and recommendation systems.
Reinforcement Learning: This is inspired by how humans learn through trial and error. The algorithm interacts with its environment and receives rewards for good decisions and penalties for bad ones. Over time, it learns to take actions that maximize the rewards. This is used in applications like training self-driving cars and playing games like chess.
Semi-Supervised Learning: This combines aspects of supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger amount of unlabeled data to improve the learning process. This is beneficial when labeled data is scarce or expensive to obtain.
Introduction to machine learning and model building using linear regressionGirish Gore
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.
Types of Machine Learning- Tanvir Siddike MoinTanvir Moin
Machine learning can be broadly categorized into four main types based on how they learn from data:
Supervised Learning: Imagine a teacher showing you labeled examples (like classifying pictures of cats and dogs). Supervised learning algorithms learn from labeled data, where each data point has a corresponding answer or label. The algorithm analyzes the data and learns to map the inputs to the desired outputs. This is commonly used for tasks like spam filtering, image recognition, and weather prediction.
Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. It's like being given a pile of toys and asked to organize them however you see fit. The algorithm finds hidden patterns or structures within the data. This is useful for tasks like customer segmentation, anomaly detection, and recommendation systems.
Reinforcement Learning: This is inspired by how humans learn through trial and error. The algorithm interacts with its environment and receives rewards for good decisions and penalties for bad ones. Over time, it learns to take actions that maximize the rewards. This is used in applications like training self-driving cars and playing games like chess.
Semi-Supervised Learning: This combines aspects of supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger amount of unlabeled data to improve the learning process. This is beneficial when labeled data is scarce or expensive to obtain.
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
Machine Learning techniques used in Artificial Intelligence- Supervised, Unsupervised, Reinforcement Learning. It discusses about Linear Regression, Logistic Regression, SVM, Random forest, KNN, K-Means Clustering and Apriori Algorithm. It also Illustrates the applications of AI in various fields.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
In this slide I answer the basic questions about machine learning like:
What is Machine Learning?
What are the types of machine learning?
How to deal with data?
How to test model performance?
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/
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
Machine Learning techniques used in Artificial Intelligence- Supervised, Unsupervised, Reinforcement Learning. It discusses about Linear Regression, Logistic Regression, SVM, Random forest, KNN, K-Means Clustering and Apriori Algorithm. It also Illustrates the applications of AI in various fields.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
In this slide I answer the basic questions about machine learning like:
What is Machine Learning?
What are the types of machine learning?
How to deal with data?
How to test model performance?
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/
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
6. Supervised Machine Learning
Supervised learning trains with labelled data (think flashcards) to
make predictions on new data
Labelled data - This has a label attached that tells you what the
data is.
There are two main categories of supervised learning:
• Regression- output will be Numerical , Decimal value
• Classification – output will be categorical value like ‘dog or not
dog’.
7. Un-Supervised Machine Learning
Unsupervised learning explores unlabelled data, finding hidden
patterns and groups
Unlabelled data - This is raw data without any labels.
There are three main categories of supervised learning:
• Clustering: This groups similar data points together, like sorting
customers by purchase history.
• Association rule learning: This finds relationships between data points
• Dimensionality reduction: This simplifies complex data by reducing the
number of features, making it easier to analyse.
9. Simple Linear Regression
(supervised ML)
Goal – To create a best fit
line in such a way that the
summation of the error will
be minimum.
Prediction Formula
y_pred = mx + c
13. What we will Learn today :-
• Convergence Algorithm
• Multiple Linear Regression
• Types of Cost/Loss Function
14. Convergence Algorithm
Convergence in machine learning is when a model's performance
stops improving during training, signifying it has reached an
optimal state.
15.
16.
17. Multiple Linear Regression
Multiple linear regression predicts a value using several
factors. Imagine estimating house prices based on size,
location, and number of bedrooms.
So our equation will be :-
Y_pred = c + m1x1 + m2x2 + m3x3 +
…….+mnxn