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Data scientists should be more end-to-end | Machine Learning Monthly September 2020

Every month I review the latest and greatest resources in the world of machine learning in a newsletter called Machine Learning Monthly. This is the video version of that newsletter for September 2020. And as usual, it’s been a massive month on tour. Read the blog post – https://zerotomastery.io/blog/machine-learning-monthly-september-2020 Learn ML (beginner-friendly course) – https://dbourke.link/mlcourse […]
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56:23

Metaflow, a Human-Centric Framework for Data Science with Ville Tuulos – #326

Today we kick off our re:Invent 2019 series with Ville Tuulos, Machine Learning Infrastructure Manager at Netflix. At re:Invent, Netflix announced the open-sourcing of Metaflow, their “human-centric framework for data science.” In our conversation, we discuss all things Metaflow, including: • The problem Metaflow is trying to solve • Why it was important for Netflix […]
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36:51

Drew Conway Interview – Cognitive Biases in Data Science

This show features my interview with Drew Conway, whose Wrangle keynote could have been called “Confessions of a CIA Data Scientist.” The focus of our interview, and of Drew’s presentation, is an interesting set of observations he makes about the role of cognitive biases in data science. If your work involves making decisions or influencing […]
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01:11:50

Machine Learning Tools and Frameworks – EMEA Meetup #3 – October 2018

**SUBSCRIBE AND TURN ON NOTIFICATIONS** **twimlai.com** This video is a recap of our November 2018 EMEA TWiML Online Meetup. In this month’s community segment we discuss the upcoming EMEA meetup group, along with our ongoing and upcoming Fast.AI study groups. We explore some of the differences between the v0.7 course and the current itter, v3 […]
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09:23

Learning Programming? You should try Anki | Learning Intelligence 38

Anki is one of my favourite tools for helping me to learn new things like a boss. It’s free, give it a try! LINKS FROM THE SHOW: My AI Masters Degree – https://bit.ly/AIMastersDegree My favourite AI/ML courses – https://bit.ly/AIMLresources Anki – https://apps.ankiweb.net/ Coursera (search Applied Data Science with Python) – http://bit.ly/courseraDS Python for Data Analysis […]
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14:16

Linux on the Desktop for the Data Science (with a Lenovo ThinkPad P53)

Most data science and machine learning tools are developed on Linux first. Generally, when you run machine learning in the cloud, it will be on Linux. What about running Linux on the desktop for a data scientist. In this video I demonstrate a Lenovo ThinkPad P53 that is setup for data science with Ubuntu as […]
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47:33

Data Science vs Data Processing vs Data Visualisation? | 3000 Subscriber Livestream

What’s the deal between data science, data processing and data visualisation? Should you focus on one? Or learn them all? I answer some of the most common questions I get on stream (plus the ones I get asked live). See you in the next one! CONNECT: Web – http://bit.ly/mrdbourkeweb Quora – http://bit.ly/mrdbourkequora Medium – http://bit.ly/mrdbourkemedium […]
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Buy AND Build for Production Machine Learning with Nir Bar-Lev – #488

Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML. In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, and his thoughts on why experiment management has become […]
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25:51

Shell: Turning An AI Strategy Into Practice | 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/ Shell: Turning An AI Strategy Into Practice. Presentation given at CogX 2019 Dan Jeavons; Head of Data Science, Shell Bernard Marr; CEO & Co-Founder, Bernard Marr […]
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38:17

cuDF, cuML & RAPIDS: GPU Accelerated Data Science with Paul Mahler – TWiML Talk #254

Today we’re joined by Paul Mahler, senior data scientist and technical product manager for machine learning at NVIDIA. In our conversation, Paul and I discuss NVIDIA’s RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and machine learning tasks. We dig into the various subprojects like cuDF and cuML […]
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12:19

Target Encoding for Categorical Values in Data Science

Target encoding is a technique that can be used to encode categorical values. Unlike dummy variables, target encoding only requires a single element in the feature vector. Source code for this vide: https://github.com/jeffheaton/present/blob/master/youtube/target_encoding.ipynb Source of this machine learning/AI Video
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44:21

Data Science on AWS with Chris Fregly and Antje Barth – #490

Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS.  In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released […]
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49:15

The Alan Turing Institute: Data Science for Science | CogX 2020

The Alan Turing Institute: Data Science for Science Ella Gale – Postdoctoral Research Associate – University of Bristol Adrian Bevan – Director – Particle Physics Research Centre Adeniyi Fasoro – Research Project Manager, AI for Science – The Alan Turing Institute Hugh Williamson – Research Fellow – University of Exeter Bringing the world’s most interesting […]
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39:14

Librosa: Audio and Music Processing in Python with Brian McFee – TWiML Talk #263

Today we continue our PyDataSci series joined by Brian McFee, assistant professor of music technology and data science at NYU, and creator of LibROSA, a python package for music and audio analysis. Brian walks us through his experience building LibROSA, including: • Detailing the core functions provided in the library, • His experience working within […]
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Multithreaded Python Will Save Hours of Your Life

In This tutorial you are going to learn how to parallelize your data preprocesing to get massive time savings in your data science career. It’s actually reasonably straight forward, and only entails a few extra lines of code for all these time savings. Learn how to turn deep reinforcement learning papers into code: Deep Q […]
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16:58

These are the Most Important Factors in a Data Science Graduate Degree Program

Applying to and choosing a graduate program can be a really stressful process. In this video, I break down some of the key parameters I used and recommend you use, to simplify the process. The too long didn’t watch is this: 1 – Maximize the name brand recognition of the university 2 – Maximize the […]
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09:47

How to Install PyTorch on Linux for CPU or GPU – No Driver Install Needed

In this video, I show you how to install PyTorch using the Linux GUI for either GPU or CPU. Linux can be a great operating system for data science and machine learning. This video shows installation through the Gnome GUI on Ubuntu using a Lenovo ThinkPad P53 with an NVIDIA Quadro RTX 5000 GPU. The […]
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Scaling Jupyter Notebooks with Luciano Resende – TWiML Talk #261

Today we kick off PyDataSci with Luciano Resende, an Open Source AI Platform Architect at IBM and part of the Center for Open Source Data and AI Technology. Luciano and I caught up to discuss his work on Jupyter Enterprise Gateway, a scalable way to share Jupyter notebooks and other resources in an enterprise environment. […]
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35:51

Lab to live: Supercharging science: How organisations can drive innovation with AI

Lab to live: Supercharging science: How organisations can drive innovation with AI Kunle Olukotun – Co-Founder and Chief Technologist, SambaNova Systems Venkatram Vishwanath – Computer Scientist, Data Science Group Lead, Argonne National Laboratory Jennifer Glore – Senior Director, Customer Engineering at SambaNova Systems – SambaNova (Moderator) #CogX2021 #JoinTheConversation YouTube Source for this AI Video
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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Learning Data Science with My Brother! | Learning Intelligence 37

My brother and I started an applied data science course on Coursera. Links: Applied Data Science with Python – http://bit.ly/courseraDS My AI Masters Degree – https://bit.ly/AIMastersDegree My favourite AI/ML courses – https://bit.ly/AIMLresources Pandas for Data Analysis Book – https://amzn.to/2LJ6iAI Deep RL Bootcamp – https://www.youtube.com/watch?v=qaMdN6LS9rA&list=PLAdk-EyP1ND8MqJEJnSvaoUShrAWYe51U CONNECT: Web – http://bit.ly/mrdbourkeweb Quora – http://bit.ly/mrdbourkequora Medium – http://bit.ly/mrdbourkemedium Twitter […]