Manufacturing innovation trends – how manufacturing is changing – Future of Manufacturing 4.0 Keynote Speaker. Lessons from Toyota assembly lines for fork lift trucks. How to optimise factory production with just-in-time and hundreds of small enhancements with huge total impact, using predictive analytics and Big Data. How to harness human genius in the workforce to make things faster and more reliably. Key manufacturing trends presented by Futurist keynote speaker Patrick Dixon – tour of Toyota factory site in Mjölby, Sweden.
A masterclass to discuss data science career development options and how to develop a personal capacity building roadmap. It speaks to the hype, myths and facts on data science career while providing versed yet converged research-based evidence on the opportunities, growth drivers and emerging applications of AI for personal and societal value creation. It showcases how you can play in the four domains of impact -Business problem-solver, Expert Practitioner, Researcher and Community builder.
Pdf link: ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I’ve been using Kite for a few months and I love it! ————————————————————————————————————————– Subscribe my vlogging channel Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Telegram link: Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more ————————————————————————————————————————- Connect with me here: Twitter: Facebook: instagram:
Join the community session . Here All the materials will be uploaded. Playlist: The Oneneuron Lifetime subscription has been extended. In Oneneuron platform you will be able to get 100+ courses(Monthly atleast 20 courses will be added based on your demand) Features of the course 1. You can raise any course demand.(Fulfilled within 45-60 days) 2. You can access innovation lab from ineuron. 3. You can use our incubation based on your ideas 4. Live session coming soon(Mostly till Feb) Use Coupon code KRISH10 for addition 10% discount. And Many More….. Enroll Now OneNeuron Link: Direct call to our Team incase of any queries 8788503778 6260726925 9538303385 866003424
In this episode, Gary discussed the recent HIMSS conference with John Glaser, Ph.D., Executive-in-Residence, Harvard Medical School, Kaveh Safavi, M.D., J.D., Senior Managing Director, Accenture, and Suchi Saria, Ph.D., Founder and CEO, Bayesian Health. They discussed the utility of in-person meetings, as well as the many cutting edge technologies that will be influencing healthcare systems in the near future. For More Info about The Gary Bisbee Show Visit: Suchi Saria, Ph.D., is the Founder and CEO of Bayesian Health, the John C. Malone Associate Professor of computer science, statistics, and health policy, and the Director of the Machine Learning and Healthcare Lab at Johns Hopkins University. She has published over 50 peer-reviewed articles with over 3000 citations and was recently described as “the future of 21st century medicine” by The Sloan Foundation. Her research has pioneered the development of next-generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. At Bayesian Health, Dr. Saria is leading the charge to unleash the full power of data to improve healthcare, unburdening caregivers and empowering them to save lives. Backed by 21 patents and peer-reviewed publications in leading technical and clinical journals, Bayesian leverages best-in-class machine learning and behavior change management expertise to help health organizations unlock improved patient care outcomes at scale by providing real-time precise, patient-specific, and actionable insights in the EMR. Dr. Saria earned her M.Sc. and Ph.D. from Stanford University working with Professor Daphne Koller. She visited Harvard University as an NSF Computing [More]
Visit to get started for free and get 20% off your annual subscription. Thanks to Brilliant for sponsoring this video 🙂 Here are the courses mentioned:- Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas – 🎓 Complete Python Programmer Bootcamp – 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition 📘 Automate the Boring Stuff with Python 📙 Python Basics – A Practical Introduction to Python 3 📕 Python Programming An Introduction to Computer Science 📗 Invent Your Own Computer Games with Python 🆓 Free Python Resource (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light – 🎧 Sony Noise Cancelling Headphones – 📱 Social Media 👌 SUBSCRIBE to ME!👌
*** TOP 10 TRENDS IN AI, MACHINE LEARNING & DATA FOR 2022 *** Do you want to better understand what’s happening in the world of Machine Learning, Artificial Intelligence, and Big Data (MAD)? Learn from venture capitalist Matt Turck, Partner at FirstMark, for a deep dive into the MAD Landscape, touching on ten megatrends: 1. Every company is a data company 2. The big unlock: data warehouses and lakehouses 3. Consolidation vs data mesh: the future is hybrid 4. An explosive funding environment 5. A busy year in DataOps 6. It’s time for real time 7. The action moves to the right side of the warehouse 8. The rise of AI generated content 9. From MLOps to ModelOps 10. The continued emergence of a separate Chinese AI stack For the full analysis, look here: This presentation was shared at Data Driven NYC, a monthly event covering Big Data and data-driven products and startups. Join our event here:
Gary Marcus, Founder & CEO of Robust AI (previously Founder & CEO of Geometric Intelligence acq. by Uber), spoke with FirstMark’s Matt Turck at a fireside chat at Data Driven NYC in September 2019. They spoke on a number of topics covering AI & ML, including about Gary’s brand new book (which actually launched at this event) that warns AI is heading in the wrong direction for a number of reasons. He argued we need to stop looking for silver bullets because cognition is complicated. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark Capital.
How seriously is the data science industry taking the issue of bias in machine learning? In this clip taken from a recent CareerFoundry live event senior Data Scientist Tom Gadsby will share some thoughts on the matter! Want more content like this? Check out CareerFoundry’s events page for more deep dive data based content, and much more: — Looking to start a career in Data? Take your first steps with CareerFoundry’s free data analytics short course: Want a deeper dive on some key UX topics? Check out CareerFoundry’s blog here: Thanks for watching! #DataAnalytics #DataScience #Shorts Want more from CareerFoundry? Check out our other social media channels and blog here: 🔍​ For more information on our programs, visit us at: 🖥 Data Science – Bias In Machine Learning Algorithms
#datascience #aiethics #techforgood Increasingly, data and technologies such as artificial intelligence (AI) and machine learning are involved with everyday decisions in business and society. From tools that sort our online content feeds to online image moderation systems and healthcare, algorithms power our daily lives. But with new technologies come questions about how these systems can be used for good – and it is up to data scientists, software engineers and entrepreneurs to tackle these questions. To learn about issues such as ethical AI and using technology for good, we speak with Rayid Ghani, professor in the Machine Learning Department of the School of Computer Science at Carnegie Mellon University and former Chief Scientist at Obama for America 2012. Professor Ghani has an extraordinary background at the intersection of data science and ethics, making this an exciting and unique show! — The conversation includes these important topics: — About Rayid Ghani and technology for good — Why is responsible AI important? — What are the ethical challenges in data science and AI? — What is the source of bias in AI? — What are some examples of AI ethical issues in healthcare? — What is the impact of culture in driving socially responsible AI? — How can we address human bias when it comes to AI and machine learning? — How can we avoid human bias in AI algorithms and data? — What skills are needed to create explainable AI and focus on AI ethics and society? — What kinds of [More]
🔥🔥In this video on Data Science vs Artificial Intelligence you will understand about the difference between these two and which one should you choose for better career. 🔥Intellipaat Data Science Architect training: So in this Data Science vs Artificial Intelligence comparison video some important parameters have been taken into consideration to tell you the difference between Data Science and Artificial Intelligence & also which one is preferred over the other in certain aspects in detail. #DataSciencevsArtificialIntelligence #DSvsAI #ArtificialIntelligencevsDataScience #Intellipaat 📌 Do subscribe to Intellipaat channel & get regular updates on videos: 📌 Do subscribe to Intellipaat channel & get regular updates on videos: 🔗 Watch Data Science tutorials here:- 📕 Read insightful blog on what is Data Science: 📕 Read insightful blog on what is Data Analytics: 📰Interested to know about Data Science certifications? Read this blog: Are you looking for something more? Enroll in our data science architect certification course and become a certified data science professional ( It is a 232 hrs instructor led data science training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you’ve enjoyed this Data Science vs Artificial Intelligence video, Like us and Subscribe to our channel for more similar informative video. Got any questions about difference between Data Science and Artificial Intelligence? Ask us in the comment section below. —————————- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 [More]
Why is there often confusion surrounding where to start when it comes to approaching a career in AI or data science? There are many intersections and overlaps between AI and data science. AI has numerous subsets, like Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). With many career opportunities in both fields, there are lots of conflicting perspectives on educational paths for starting a career in one of these fields. Join Simplilearn on Thursday, October 29, 2020, at 9 PM IST (8:30 AM PDT) for a talk with Ronald van Loon, CEO of Intelligent World and a Simplilearn Advisory Board member. Ronald will give you insights into: 1. The main differences between AI and Data Science 2. The industries and types of companies hiring Data Science and AI professionals 3. The most popular and in-demand types of careers in both fields 4. Fundamental learning and educational paths that can help people choose which career path is best for them 5. Specific examples of educational paths for a career in data science and for a career in AI 🔥Free AI Course: 🔥Free Data Science : ✅Subscribe to our Channel to learn more about the top Technologies: ⏩ Check out the Artificial Intelligence training videos: #ArtificialIntelligenceCareer #DataScieneCareer #ArtificialIntelligenceAndDataScienceJobOpportunities #ArtificialIntelligence #AI #SimplilearnAI #RiseofAI #FutureOfAI#Simplilearn Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design [More]
Moderator: Geoffrey Baehr, General Partner, Almaz Capital Partners Speakers: – Angelo Del Priore, Partner, HP Tech Ventures – Upal Basu, General Partner, NGP Capital – Aaron Jacobson, Partner, NEA – Anis Uzzaman, General Partner, Fenox Venture Capital – Ashmeet Sidana, General Partner, Engineering Capital
The Naive Bayes classifier is a simple classifier that classifies based on probabilities of events. It is the applied commonly to text classification. Though it is a simple algorithm, it performs well in many text classification problems. Other Pros include less training time and less training data. Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more Please do subscribe my other channel too If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: Facebook: instagram:
Python Machine Learning Tutorial – Learn how to predict the kind of music people like. 👍 Subscribe for more Python tutorials like this: 👉 The CSV file used in this tutorial: 🚀 Learn Python in one hour: 🚀 Python (Full Course): Want to learn more from me? Courses: Twitter: Facebook: Blog: #Python, #MachineLearning, #Jupyter TABLE OF CONTENT 0:00:00 Introduction 0:00:59 What is Machine Learning? 0:02:58 Machine Learning in Action 0:05:45 Libraries and Tools 0:10:40 Importing a Data Set 0:17:01 Jupyter Shortcuts 0:22:53 A Real Machine Learning Problem 0:26:09 Preparing the Data 0:29:15 Learning and Predicting 0:33:20 Calculating the Accuracy 0:39:41 Persisting Models 0:42:55 Visualizing a Decision Tree
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 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 – 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: 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- Machine Learning [More]
Reality behind data science jobs. Is machine learning really cool? 🌎 Website: 🎥 Codebasics Hindi channel: #️⃣ Social Media #️⃣ 🔗 Discord: 📸 Instagram: 🔊 Facebook: 📱 Twitter: 📝 Linkedin (Personal): 📝 Linkedin (Codebasics): 🔗 Patreon: ❗❗ 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
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 → Watch more IoT sessions from I/O ’18 here → See all the sessions from Google I/O ’18 here → Subscribe to the Google Developers channel → #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 : Emotion detection in 5 Lines using pre-trained model -: =========== 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 : ☑️ Facebook : ☑️ Instagram : ☑️ Twitter : ☑️ Telegram: For Business Inquiries : Best book for Machine Learning : 🎥 Playlists : ☑️Machine Learning Basics ☑️Feature Engineering/ Data Preprocessing ☑️OpenCV Tutorial [Computer Vision] ☑️Machine Learning Algorithms [More]