MIT 6.0002 11. Introduction to Machine Learning
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MIT 6.0002 11. Introduction to Machine Learning

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016View the complete course: http://ocw.mit.edu/6-0002F16Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SAMore information at http://ocw.mit.edu/termsMore courses at http://ocw.mit.edu
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Forget models, build a data flywheel | Machine Learning Monthly January 2021

As usual an unbelievable month on tour in the world of machine learning for January! Seems like the trend of the year is: multi-modal models, transformers taking over everything and machine learning deployment becoming mainstream (don’t let your models die in notebooks, deploy them). Machine Learning Monthly January 2021 – https://zerotomastery.io/blog/machine-learning-monthly-january-2021/ Support freeCodeCamp’s Data Science […]
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Break into AI: I took an online course, what's next?

Welcome to the virtual Learner Community Event hosted by DeepLearning.AI. We have assembled a panel of machine learning practitioners who have gotten into the field from different paths. They will be sharing their first-hand experience and suggestions on how to transition from online courses to landing your first ML job! Topics that you can expect […]
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Case Study: Airbus | CogX 2019

Join the CogX Global Leadership Summit and Festival of AI and Breakthroughs Technology – June 8th to 10th 2020 – https://cogx.co/ Subscribe to our epic newsletters for free https://cognitionx.com/newsletter-subscribe/ Case Study: Airbus. Presentation given at CogX 2019, on the Lab to Live Stage Pete Burnap; Professor of Data Science and Cybersecurity, Cardiff University Matilda Rhode; […]
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Setup Apple Silicon Mac for Machine Learning in 11 minutes (PyTorch edition)

Setup your Apple M1, M1 Pro, M1 Max or M1 Ultra Mac for data science and machine learning with PyTorch. Get the code on GitHub – https://github.com/mrdbourke/pytorch-apple-silicon PyTorch on Mac announcement blog post – https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ Learn PyTorch – https://learnpytorch.io Setup Apple M1 for TensorFlow – https://youtu.be/_1CaUOHhI6U Other links: Learn ML (beginner-friendly courses I teach) – […]
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The Applied Data Science Course is epic! | Learning Intelligence 42

The Applied Data Science with Python Specialization on Coursera is turning out to be one of the best I’ve ever done. I’d highly recommend it. Sign up for the Applied Data Science Course on Coursera – http://bit.ly/courseraDS OTHER LINKS FROM SHOW: My AI Masters Degree – https://bit.ly/AIMastersDegree My favourite AI/ML courses – https://bit.ly/AIMLresources Python for […]
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Expert Panel: Optimizing BizOps with AI

Welcome to DeepLearning.AI Expert Panel – Optimizing BizOps with AI – in partnership with FourthBrain. Join us for a discussion from AI leaders to find out how AI is deployed at their companies and across industries to improve business efficiency. 40-min Panel discussion + 20-min Q&A. We will be taking questions from Slido. Speakers: -Patrick […]
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Panel Discussion: AI for Societal Impact

AI researchers are striving to create intelligent machines that complement human reasoning and enrich human experiences and capabilities. At the core is the ability to harness the explosion of digital data and computational power with advanced algorithms that extend the ability for machines to learn, reason, sense, and understand—enabling collaborative and natural interactions between machines […]
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Neural Network Architectures & Deep Learning

This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: http://databookuw.com/ Steve Brunton’s website: eigensteve.com Follow updates on Twitter @eigensteve This video is part of a playlist “Intro to Data Science”: https://www.youtube.com/playlist?list=PLMrJAkhIeNNQV7wi9r7Kut8liLFMWQOXn YouTube […]
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Responsible Data Science in the Fight Against COVID-19 (Coronavirus)

“We are at a critical point in the global response to COVID-19 – we need everyone to get involved in this massive effort to keep the world safe.” – WHO Director-General Dr. Tedros Adhanom Ghebreyesus Since the beginning of the coronavirus pandemic, we’ve seen an outpouring of interest on the part of data scientists and […]
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All Python Libraries You Need For Machine Learning And Data Science

All Python Libraries You Need For Machine Learning, Deep Learning, And Data Science. Blog on your personal domain and grow your audience without any annoying pop-ups/ads. Start now on https://hashnode.com * ————————————————- Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook ✅ Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer * 🪁 […]
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Setting up Windows 10 (as a Data Scientist)

Would you like to see how I set up a Win10 computer for data science and machine learning? In this video, I show you what software I install and how I configure this machine. If you would like to see a video about the building of my computer, check out: Disable reboots: reg add “HKLMSOFTWAREPoliciesMicrosoftWindowsWindowsUpdateAU” […]
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Data Science in 4 Minutes: Quick High Level Overview

In this video I discuss classification, regression, overfitting, underfitting, bias, variance, and feature engineering. This gives a very quick overview of data science in 4 minutes. Source of this machine learning/AI Video
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Cyberpunk 2077 $5K NVIDIA RTX A6000 GPU Data Science Machine

A quick video to show what happens when you play Cyberpunk 2077 on my machine learning workstation equipped with an NVIDIA RTX A6000? The frame rate is quite good at 70+ FPS with basic game settings. After some suggestions in the comments, I tried setting Raytracing to ultra and got 50 FPS, adding DLSS max […]
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2.2: Pandas and Anaconda Python for Data Science and Deep Learning (Module 2, Part 2)

More on Pandas and how to process dataframes and perform calculations. This is geared towards using pandas with Keras and Tensorflow. This course is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, at Washington University […]
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Agile Applied AI Research with Parvez Ahammad – #492

Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn. In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to identify products […]
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An introduction to MLOps with TensorFlow Extended (TFX)

Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. An ML application in production requires modern software development methodology, as well as issues unique to ML and data science. Hear about the importance of MLOps, the use of ML pipeline architectures for implementing production ML applications, […]