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🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=DescriptionFirstFold&utm_source=youtube This Machine Learning Algorithms video will help you learn what is Machine Learning, various Machine Learning problems and the algorithms, key Machine Learning algorithms with simple examples and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest and KNN algorithm. Below topics are covered in this Machine Learning Algorithms Tutorial: 00:00 – 03:39 Machine Learning example and real-world applications 03:39 – 04:40 What is Machine Learning? 04:40 – 06:14 Processes involved in Machine Learning 06:14 – 09:40 Type of Machine Learning Algorithms 09:40 – 10:04 Popular Algorithms in Machine Learning 10:04 – 29:10 Linear regression 29:10 – 52:49 Logistic regression 52:49 – 01:04:45 Decision tree and Random forest 01:04:52 – 01:10:28 K nearest neighbor Dataset Link – https://drive.google.com/drive/folders/1FaV91OkTsABJrjnfeeTR4rwLe0mxFHxZ What is Machine Learning? Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Various machine learning algorithms are used to train models that can solve business problems. Linear regression, Logistic regression, Decision tree, Random forest, and K nearest neighbors are some of the popular machine learning algorithms used in the industries. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Machine-Learning-Algorithms-I7NrVwm3apg&utm_medium=Tutorials&utm_source=youtube Machine Learning [More]
Reality behind data science jobs. Is machine learning really cool? 🌎 Website: https://www.skillbasics.com/ 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/ 📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/ 🔗 Patreon: https://www.patreon.com/codebasics?fan_landing=true ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers’.
Mata Haggis-Burridge works in the gaming industry, where he works with Artificial Intelligince systems every day. Too often, he sees that people assume AI is neutral because it’s based on facts and patterns. However, the data that is collected to build these AI systems are merely a reflection of past patterns. In this way, despite the best intentions of developers, discrimination, racism and prejudice sneak into the systems we now rely on to eliminate such human biases. In order to break with the patterns of our past, we need to recognize their flaws and teach the algorithms of the future to do the same. Dr. Mata Haggis-Burridge is Professor of Creative and Entertainment Games at NHTV: Breda University of Applied Sciences, where he has worked since 2010. He completed his PhD on Cyberculture in 2006. Mata is an award winning video game developer, as well as researching the social implications of national and international policies regarding video games. His work frequently involves confronting how problematic outcomes emerge from complex systems, and trying to find new paths to better solutions. Mata’s talk is about how Artificial Intelligences can affect ourlives in many strange ways. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
With the IoT market set to triple in size by 2020, and massive increases in computing power on small devices, the intersection of IoT and machine learning is a trend that all developers should pay attention to. This talk will cover three core use cases, including: how to manage sourcing data from IoT devices to drive machine-learned models; how to deploy and use trained models on mobile devices; and how to do on-device training with a Raspberry Pi computer. Rate this session by signing-in on the I/O website here → https://goo.gl/rYcGev Watch more IoT sessions from I/O ’18 here → https://goo.gl/xfowJ8 See all the sessions from Google I/O ’18 here → https://goo.gl/q1Tr8x Subscribe to the Google Developers channel → http://goo.gl/mQyv5L #io18 event: Google I/O 2018; re_ty: Publish; product: Cloud – Internet of Things (IoT) – IoT Core, Cloud – Data Analytics – PubSub, Cloud – Containers – Google Kubernetes Engine (GKE), TensorFlow – General, Cloud – AI and Machine Learning – AI Platform; fullname: Laurence Moroney, Kaz Sato; event: Google I/O 2018;
7604818524, 9488042210 #ArtificialIntelligence and Robotics | Career in AI | Complete Details Super Smart Artificial Intelligence and the Future | A New Era of Artificial Intelligence How To Become An Artificial Intelligence Engineer | AI Engineer Career Path And Skills How To Become An Artificial Intelligence Engineer | #AIEngineer Career Path And Skills B Tech #ArtificialIntelligenceandData Science in 2021 Higher Studies Career Selection in Tamil Google Free Artificial Intelligence Certification Course | Get Foreign University Certificate Free 🔥Artificial Intelligence Tamil 2018 | Artificial Intelligence இலவசமாக படிக்கலாம் | Tamil #AI jobs vacancy Liked videos SUBSCRIPTIONS B.Tech. in AI & #DataScience AI Certification – Artificial Intelligence Classes Industry Endorsed & Practical Learning via Projects & Case Studies. Meet an Expert Today. Live… Artificial Intelligence and Robotics | Career in AI AI VS ML VS DL VS Data Science #AIMachineLearning Basics | What Is #MachineLearning? | Introduction To Machine Learning |
This talk and related interactive demonstration aim to introduce our efforts in developing an Open data hub and Open-source simulation and optimization framework based on virtual tracks and transportation mesh networks. We will demonstrate how to deliver rapid prototyping of SCDT and enable smarter multimodal policy decisions. We will also discuss a cross-resolution modeling approach to enable consistency from discrete traffic simulation to continuous fluid queue-based model. The proposed open-data-based and open-source enabled framework also intends to create a user community of thought leaders in this emerging area across different geographically distributed communities. Within the Open-SCDT context, large-scale agent-based simulator and GMNS based modeling tools aim to engage citizens with local transportation planning by making it as easy as possible to imagine how changes might affect a person’s commute. It could be used by city authorities to interactively communicate proposed and ongoing projects, by the general public to explore and submit ideas for improving their community, and by advocacy groups to educate people about options for reducing automobile dependency.
Hello Friends, In this episode we are going to do Emotion Detection using Convolutional Neural Network(CNN). I will do the step by step implementation starting for the dataset download, accessing data set, preprocessing images, designing CNN, training CNN , saving trained model and using that saved model to do the emotion detection on video or live stream. Code link : https://github.com/datamagic2020/Emotion_detection_with_CNN Emotion detection in 5 Lines using pre-trained model -: https://youtu.be/ERXqo_ZEnIo =========== Time Code =========== 00:01 Introduction to Emotion Detection using CNN 01:21 FER 2013 Facial Expression Dataset 04:12 files in emotion detection project 05:52 Image preprocessing using Image Data Generator 08:09 Design/Create Convolution Neural Network for Emotion Detection 10:33 Train out CNN with FER 2013 Dataset / Train CNN for Emotion Detection 11:59 Save the trained model weights and structure 13:08 Test Trained Emotion Detection model 14:15 Load saved model 15:05 Access Video or Camera Feed for testing Emotion Detection model 16:20 Face detection with Haarcascade classifier 18:16 Detect and Highlight each face on video 20:06 Predict Emotion using model 20:21 Display Emotion on video 21:53 Emotion Detection Demo 24:58 emotion detection improvisations Stay tuned and enjoy Machine Learning !!! Cheers !!! #emotiondetection #CNN #DeepLearning Connect with me, ☑️ YouTube : https://www.youtube.com/c/DataMagic2020 ☑️ Facebook : https://www.facebook.com/datamagic2020 ☑️ Instagram : http://instagram.com/datamagic2020 ☑️ Twitter : http://www.twitter.com/datamagic5 ☑️ Telegram: https://t.me/datamagic2020 For Business Inquiries : datamagic2020@gmail.com Best book for Machine Learning : https://amzn.to/3qCe0Rf 🎥 Playlists : ☑️Machine Learning Basics https://www.youtube.com/playlist?list=PLTmQbi1PYZ_E1iTkBrZWK_htO0hY4vcGK ☑️Feature Engineering/ Data Preprocessing https://www.youtube.com/playlist?list=PLTmQbi1PYZ_EnBmO1-E0Z81ArnE-zSR1a ☑️OpenCV Tutorial [Computer Vision] https://www.youtube.com/playlist?list=PLTmQbi1PYZ_GrjMHiGCYa0WyDZfxu-yTz ☑️Machine Learning Algorithms [More]
5 Essential end to end data science projects for a data scientist resume. 3 of these projects are machine learning projects and 2 of them are power bi, tableau dashboarding BI projects. These are end to end data science, machine learning projects that will look very good on your resume. All of these projects are free and available on youtube along with the code. at 11:08 I have discussed important tips to generate new project ideas for data science and machine learning. ***This video is sponsored by Udemy, a popular e-learning platform. Get up to 80% off on your udemy course purchase by using below links, Udemy: https://click.linksynergy.com/fs-bin/click?id=fR6pl33ZVWU&offerid=624447.18009&type=3&subid=0 Here are few courses that I recommend for learning Power BI and Tableau. Power BI Up and Running: https://click.linksynergy.com/fs-bin/click?id=fR6pl33ZVWU&offerid=624447.18011&type=3&subid=0 Complete Power BI Intro: https://click.linksynergy.com/fs-bin/click?id=fR6pl33ZVWU&offerid=624447.18010&type=3&subid=0 Tableau: https://click.linksynergy.com/fs-bin/click?id=fR6pl33ZVWU&offerid=624447.18008&type=3&subid=0 (Note: I earn some affiliate commission when you buy above courses) 🤝 Support my youtube channel by buying a data science, coding 👕 T-shirt: https://kaaipo.com/collections/coding-collection/?utm_source=youtube&utm_medium=post&utm_campaign=codebasics-community ⭐️ Timestamps ⭐️ 00:00 Introduction 00:56 Power BI, Tableau project to generate sales insights 03:09 Power BI project for personal finances 04:28 Machine learning project: Classification 06:20 Machine learning project: Regression 08:58 Deep learning project in Tensorflow 11:08 3 Tips to generate data science project ideas Projects playlists: Sales insights (Power BI): https://www.youtube.com/playlist?list=PLeo1K3hjS3uva8pk1FI3iK9kCOKQdz1I9 Sales insights (Tableau): https://www.youtube.com/playlist?list=PLeo1K3hjS3usDI9XeUgjNZs6VnE0meBrL Personal finance dashboard (Power BI): https://www.youtube.com/watch?v=pqSoCa2NGj4 Classification Project: https://www.youtube.com/playlist?list=PLeo1K3hjS3uvaRHZLl-jLovIjBP14QTXc Regression Project: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg Deep Learning Project: https://www.youtube.com/playlist?list=PLeo1K3hjS3ut49PskOfLnE6WUoOp_2lsD 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub [More]
Apple Revenue (2019): US$ 260.2 Billion Google Revenue (2019): US$ 160 Billion Microsoft Revenue (2019): US$ 125.8 Billion Facebook Revenue (2019): US$ 70.7 Billion All these Giants have started their companies with an idea, which has changed our world. My idea is a proposal “Free-of-charge Online Healthcare Technology Platform”, which will grant free subscription for more than 160,000 hospitals worldwide (2017) and organize an expected $8.2 trillion healthcare market (End 2020). This platform will give enormous savings “large profit” for the hospitals worldwide with free subscription, similar to any online free account (Facebook, Twitter, Instagram, LinkedIn…etc). The platform’s fortune will be a big data representing global healthcare market “$8.2 trillion”. To understand my proposal “$$$$ Billion”, you need just to watch this 11 minutes video on my YouTube Channel. “Engineering The Future OF Healthcare Technology” LIU Team: Mohamad Abou Ali, Hassan Nasser & Mohamad Hajj-Hassan This material has been prepared and presented by the Biomedical Engineering Department at the Lebanese International University (LIU), Lebanon. Best Regards, Authors: Mohamad Abou Ali (Instructor, BENG – LIU) Mohamad Hajj-Hassan (Dean, BENG – LIU ) Department of Biomedical Engineering (BENG), Lebanese International University (http://liu.edu.lb/) #HealthcareTechnologyPlatform #AIBigData #SmartInvestment
How can artificial intelligence be biased? Bias in artificial intelligence is when a machine gives consistently different outputs for one group of people when compared to another. Typically these biased outputs follow classic human societal biases like race, gender, biological sex, nationality, or age. Biases can be as a result of assumptions made by the engineers who developed the AI, or they can be as a result of prejudices in the training data that taught the AI, which is what Johann Diedrick explains in the latest edition of Mozilla Explains. Learn more about Diedrick’s project, Dark Matters: https://foundation.mozilla.org/blog/dark-matters-new-project-spotlights-the-inbuilt-bias-in-digital-voice-assistants/ Featured in this video, Survival of the Best Fit is a Mozilla Creative Media awardee built by Jihyun Kim, Gábor Csapo, Miha Klasinc, and Alia ElKattan. Experience it here: https://www.survivalofthebestfit.com/
🔥 Data Science Master Program (Use Code “𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎”): https://www.edureka.co/masters-program/data-scientist-certification This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners tutorial video: 00:00 Agenda 2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probablity 1:33:34 Marginal Probablity 1:34:06 Joint Probablity 1:34:58 Conditional Probablity 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:!1:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 [More]
In the world today, we’re seeing everything sped up including a shift to automation, which I believe will be combined with blockchain technology and artificial intelligence. I believe the next biggest trend will focus on automated technology using blockchain and AI. One of the biggest use cases will be in the supply chain. Learn about a few more areas that will eventually benefit from blockchain technology and Artificial Intelligence. Today, we are going to explain what’s happening in the world of blockchain when it comes to automation and artificial intelligence and why that’s an important trend to keep a watchful eye on. We’ll explore blockchain technology in healthcare, social media, data sharing, and more. Invest in yourself! Use my link and check out the first chapter of any course for FREE! https://bit.ly/3s28OGG ⭐ 1 Million Subscriber Giveaway! ⭐ As we approach 1 million subscribers on YouTube, we will be giving away 1 full Bitcoin, 5 Ethereum, & 4,000 Cardano to 10 lucky winners. Sign up for the contest here ➡️ https://gleam.io/g6BKF/bitboy-crypto-1-million-subscriber-bitcoin-giveaway 00:00 Intro 00:59 Blockchain and AI use cases 03:23 DataCamp 04:46 Fetch.ai 06:09 Blockchain in healthcare system Connect with Me & the BitSquad! Join the BitSquad ➡️ http://t.me/BitSquad Join the BitBoy Lab ➡️ http://discord.BitBoy.Live Join BitSquad Traders ➡️ http://t.me/BitSquadTraders Join Me on Twitter ➡️ https://twitter.com/Bitboy_Crypto Join Me on Instagram ➡️ https://www.instagram.com/bitboy_crypto Join Me on TikTok ➡️ https://www.tiktok.com/@BitBoyCrypto ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● Find deals on hardware wallets, trading indicators, exchanges & more. Find Crypto DEALS ➡️ https://bitboycrypto.com/deals ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● All of our videos are [More]
Parkinson’s disease (PD) is a progressive disorder with a presymptomatic interval; that is, there is a period during which the pathologic process has begun, but motor signs required for the clinical diagnosis are absent. There is considerable interest in discovering markers to diagnose this preclinical stage. Current predictive marker development stems mainly from two principles; first, that pathologic processes occur in lower brainstem regions before substantia nigra involvement and second, that redundancy and compensatory responses cause symptoms to emerge only after advanced degeneration. Decreased olfaction has recently been demonstrated to predict PD in prospective pathologic studies, although the lead time may be relatively short and the positive predictive value and specificity are low. Screening patients for depression and personality changes, autonomic symptoms, subtle motor dysfunction on quantitative testing, sleepiness and insomnia are other potential simple markers. More invasive measures such as detailed autonomic testing, cardiac MIBG-scintigraphy, transcranial ultrasound, and dopaminergic functional imaging may be especially useful in those at high risk or for further defining risk in those identified through primary screening. Despite intriguing leads, direct testing of preclinical markers has been limited, mainly because there is no reliable way to identify preclinical disease. Idiopathic RBD is characterized by loss of normal atonia with REM sleep. Approximately 50% of affected individuals will develop PD or dementia within 10 years. Dataset Link: https://archive.ics.uci.edu/ml/datasets/parkinsons #machinelearning #artificialintelligence #ai #datascience #python #programming #technology #deeplearning #coding #bigdata #computerscience #tech #data #pythonprogramming #programmer #developer #dataanalytics #software #datascientist #javascript #iot #java #coder #ml #innovation #robotics #linux #analytics [More]
Mauro Martino from the Northeastern University Mauro is a pioneer in the use of the artificial neural network in the sculpture field and talks about “Beautiful Data Science”. His talk is titled “Data Exploration Through Artificial Intelligence and Dataviz”. Shooting and editing: Frieder Aurin for 2SPOT production, www.2spot.tv
To learn more about digital twins please visit: https://www.autodesk.com/solutions/digital-twin/architecture-engineering-construction
In this keynote, we announced the launch of Databricks Machine Learning, the first enterprise ML solution that is data-native, collaborative, and supports the full ML lifecycle. This launch introduces a new purpose-built product surface Databricks ML provides a solution for the full ML lifecycle by supporting any data type at any scale, enabling users to train ML models with the ML framework of their choice and managing the model deployment lifecycle – from large scale batch scoring to low latency online serving. Additionally, we announced two machine learning capabilities. First, Databricks Feature Store, the first feature store codesigned with a data and MLOps platform. Second, Databricks AutoML, a ‘glass box’ approach to autoML that accelerates model development without sacrificing control and transparency. Finally, this keynote covers and end-to-end demo of Databricks Machine Learning. Register for free to see the rest of the keynotes and exciting announcements live, plus over 200+ sessions. Learn from the creators and top contributors of technologies like PyTorch, TensorFlow, MLflow, Delta Lake, Apache Spark, Hugging Face, DBT and more. https://databricks.com/dataaisummit/north-america-2021 Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc/
Here we talk about the future role of layers, how their job will change, and how they can best prepare for the 4th industrial revolution.
You are a HUGE football fan. Every week you pick winners in an NFL pick-em’ league. Somehow, all that fan experience doesn’t translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and “knowledge” from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative [More]
Wish to build your career in Data Science and Artificial Intelligence space? Here is a video with the best advice from subject matter experts. Listen to these data scientists share tips on how to crack a data science interview or an artificial intelligence interview along with advice on how to make a career transition into AI, Machine Learning and Data Science. You will also find tips on how to build an effective resume. 0:33 AI Career Advice from Swiggy Data Scientist 2:12 Data Science Career Advice from Youplus Data Scientist 3:35 Data Science Career Advice from Flipkart Data Scientist 4:38 DS and AI Career Advice from K-Mart Data Scientist 6:09 Data Science Career Advice from Walmart Labs Data Scientist Subscribe to our channel to get updates on the latest videos. Hit the subscribe button now! http://bit.ly/36DfiCy Who is data science for? http://bit.ly/33M0a2T What are the required skills for data science? http://bit.ly/2qnTFFY What does Machine Learning Engineer do? http://bit.ly/2Yeewry Who are we? Springboard is an online learning platform that helps you master in-demand skills through a personal 1:1 mentor-led model and a project-driven curriculum. Over the last 6+ years, we have served 10K+ learners in 100+ countries. We are now in India and are offering Career Track programs in Data Science, Data Analytics and AI/ML along with job guarantee. Apply here: http://bit.ly/34JJt9D For more information, please write to us at india@springboard.com or call us at +91 8098866488 or +91 7483024694 Follow Springboard: Facebook: https://www.facebook.com/springboardind/ LinkedIn: https://www.linkedin.com/company/spri Twitter: https://twitter.com/springboard_ind Medium: https://medium.com/@springboard_ind #DataScience [More]
Watch Rachel Thomas’s talk, “How to Learn Deep Learning (When You’re Not a Computer Science PhD)” from the free, live online Demystifying Data Science conference hosted by Metis on September 27, 2017. Rachel Thomas has a math Ph.D. from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research”. She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright.
👉 Download Our Free Data Science Career Guide:✅https://bit.ly/31XjqMw 👉 Sign up for Our Complete Data Science Training:✅https://bit.ly/3mqEBzW So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/31XjqMw ** Expand for some additional INFO and LINKS ** Data science consulting companies are a hot choice if you’re looking for a job in the field. They offer numerous development opportunities, access to the latest technologies, and provide data-based solutions for top-notch companies across the globe. Furthermore, on top of generous salaries, they seem to have tons of cool perks – from unlimited vacation days and free meals to hair salons and masseuses on site. This doesn’t make your choice any simpler, though. With so many industries and companies out there, it’s hard to keep track of who-offers-what-and-where. So, watch this video to find out which companies provide the best overall employee experience! Complete Data Science Online Training Program. Earn a data science degree at your own pace. Sign up here: https://bit.ly/3mqEBzW Follow us on YouTube: ✅https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: ✅Website: http://bit.ly/2TNHi0B ✅Facebook: https://www.facebook.com/365datascience ✅Instagram: https://www.instagram.com/365datascience ✅Q&A Hub: https://365datascience.com/qa-hub/ ✅LinkedIn: https://www.linkedin.com/company/365datascience Prepare yourself for a career in data science with our comprehensive program: ✅https://bit.ly/3mqEBzW Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #companies #career #firms #scientist
This tutorial was recorded at KDD 2020 as a live, hands-on tutorial. The content is available at https://dssg.github.io/fairness_tutorial/
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. – Natural Language Processing (Part 1): Introduction to NLP & Data Science – Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python – Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python – Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python – Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python – Natural Language Processing (Part 6): Text Generation with Markov Chains in Python All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
In a profound talk about technology and power, author and historian Yuval Noah Harari explains the important difference between fascism and nationalism — and what the consolidation of our data means for the future of democracy. Appearing as a hologram live from Tel Aviv, Harari warns that the greatest danger that now faces liberal democracy is that the revolution in information technology will make dictatorships more efficient and capable of control. “The enemies of liberal democracy hack our feelings of fear and hate and vanity, and then use these feelings to polarize and destroy,” Harari says. “It is the responsibility of all of us to get to know our weaknesses and make sure they don’t become weapons.” (Followed by a brief conversation with TED curator Chris Anderson) Check out more TED Talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world’s leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design — plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED