Machine learning presentation
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Machine learning is motivated by the ability to use data to build models that can make predictions without being explicitly programmed. There are three main types of machine learning: supervised learning where labeled data is used to train models, unsupervised learning where unlabeled data is used to find hidden patterns in data, and reinforcement learning where agents learn from rewards and punishments. The machine learning process involves getting data, preparing it, training models on the data, testing the models, and improving the models through iterative training. Popular tools for machine learning include Python programming languages, TensorFlow framework, cloud platforms like Google Cloud, and resources like online courses and Kaggle competitions.
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This document provides an overview of machine learning. It discusses supervised learning techniques like classification and regression. It also covers unsupervised learning techniques like clustering, dimensionality reduction, and association rule learning. The document outlines the machine learning workflow and compares instance-based versus model-based learning. It discusses challenges like insufficient data, poor data quality, irrelevant features, and overfitting. The goal is to provide learners with a base to build machine learning skills and solve problems using techniques like regression, data preprocessing, visualization, and evaluating models.
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Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills?
If your response to both questions is 'Yes', we are in the same position.
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This document outlines the machine learning workflow, including:
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3) Performing exploratory data analysis and feature engineering.
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- For full details of the license terms, see the URL provided.
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- For full details of the license terms, see the URL provided.
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2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
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2. Analyse the importance of hyperparameters.
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Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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Machine learning presentation
- 2. Agenda ● Motivation behind ML ● Concept of the ML ● Technique of the ML 2
- 3. Motivation ● What’s Machine learning ? ● Where using Machine learning ? ● The future of Machine learning ? 33
- 4. Part 1 : Concept of the ML 44
- 5. Concept ● Sequential logic and Machine learning ● Types of Algorithms 5 5
- 6. Sequential logic and Machine learning Sequential logic Machine learning Input Output label Model 6 6
- 7. Type of learning ● Supervised ● Unsupervised ● Reinforced 7 7
- 8. Supervised ● Input data are already labeled ● Model train by iterating ● Commun exemples : 1) Regression (predicting value) 8 8
- 9. 2) Classification (predicting Label) Supervised 9 9
- 10. Unsupervised ● Input data are not labeled ● Model deducer structures ● Commun exemples : 1) Clustering 10 10
- 12. 12 Reinforcement ● Learn by using a reward function ● Commun exemples : 1) Games 1212
- 13. Part 2 : Technique of the ML 13 13
- 14. Technique ● The machine learning process ● Real example ● Share tools for you ● Touch and play with some tools (Practical) 14 14
- 15. 1515
- 16. Get Data ● Need lots of data ● Lots of excellent ones are online 16 16
- 17. Clean , prepare data ● Convert into appropriate format (using python) ● Label the data ● Distort data to generalize you model ● Make a good model first to avoid problems 17 17
- 18. Train model ● Very computationally expensive(GPU,CPU,TPU) ● Only experts can configure from scratch (using cloud) 18 18
- 19. Testing ● Typically saving some train data for testing ● Test for worst state for highest purpose 19 19
- 20. Improve ● Iterative and increase accuracy 20 20
- 22. Tools for you ● Programming languages : R , Python (recommended ) ● Platform : Any typing editor ● Framework : Tenserflow lib ● Clouds : floydhub (free to test) Google cloud , amazon or azure (train model) ● Maths : Linear algebra , Calculus , probability and statistic ● Library and Competition : https://www.kaggle.com/ ● Lecture (courses) : Coursera , Udacity ● Books : building machine learning systems with python (for beginners) 22 22
- 23. Bye 2323