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Learn about machine learning motivations, use cases, and important Machine Learning paradigms like supervised vs. unsupervised learning. We also explore the Machine Learning project workflow, which includes data collection, validation, and analysis. To learn more about CENGN Academy and the courses they offer, visit: https://www.cengn.ca/services/cengn-academy/ To learn more about DAIR, visit: https://www.canarie.ca/cloud
Yale University’s Wu Tsai Institute and the Schmidt Program on Artificial Intelligence, Emerging Technologies, and National Power co-host the talk, “The Alignment Problem: Machine Learning and Human Values,” by Brian Christian, an award-winning author and Science Communicator in Residence at the Simons Institute for the Theory of Computing at University of California – Berkeley. Christian is recognized as a leading authority on artificial intelligence and the ethical challenges associated with emerging technologies. His latest book, “The Alignment Problem: Machine Learning and Human Values,” is a blend of history and on-the-ground reporting, tracing the explosive growth of machine learning and the wide range of resulting risks, opportunities, and unintended consequences. The book is a Los Angeles Times Finalist for Best Science & Technology Book of the Year, and Microsoft CEO Satya Nadella has named it one of the five books that inspired him in 2021. Christian is the author of the acclaimed bestsellers “The Most Human Human” and “Algorithms to Live By.” His writing has appeared in The New Yorker, The Atlantic, Wired, and The Wall Street Journal, as well as peer-reviewed journals. He holds degrees in computer science, philosophy, and poetry from Brown University and the University of Washington.  The talk is moderated by John Lafferty, John C. Malone Professor of Statistics & Data Science, and Director of the Center for Neurocomputation and Machine Intelligence at Yale.
🔥 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]
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners. 🔗 Course website with learning resources: https://antern.co/pages/ml001.html 💻 Code: https://github.com/ayush714/ML001-Project-Sources-Code-and-Learning-Materials ✏️ Course developed by Ayush Singh. Check out his channel: https://www.youtube.com/c/neweraa ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Course Introduction ⌨️ (0:04:34) Fundamentals of Machine Learning ⌨️ (0:25:22) Supervised Learning and Unsupervised Learning In Depth ⌨️ (0:35:39) Linear Regression ⌨️ (1:07:06) Logistic Regression ⌨️ (1:24:12) Project: House Price Predictor ⌨️ (1:45:16) Regularization ⌨️ (2:01:12) Support Vector Machines ⌨️ (2:29:55) Project: Stock Price Predictor ⌨️ (3:05:55) Principal Component Analysis ⌨️ (3:29:14) Learning Theory ⌨️ (3:47:38) Decision Trees ⌨️ (4:58:19) Ensemble Learning ⌨️ (5:53:28) Boosting, pt 1 ⌨️ (6:11:16) Boosting, pt 2 ⌨️ (6:44:10) Stacking Ensemble Learning ⌨️ (7:09:52) Unsupervised Learning, pt 1 ⌨️ (7:26:58) Unsupervised Learning, pt 2 ⌨️ (7:55:16) K-Means ⌨️ (8:20:21) Hierarchical Clustering ⌨️ (8:50:28) Project: Heart Failure Prediction ⌨️ (9:33:29) Project: Spam/Ham Detector 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 AppWrite — Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
The field of construction is well placed to benefit from the advent of machine learning and artificial intelligence. As part of the BIM 360 Project IQ Team at Autodesk, I’ve had the privilege of being a part of Autodesk’s foray into machine learning for construction. This talk will focus on summarizing the developments in this space, and we’ll cover some ways in which one can prepare to maximize value from this technology. The class has 2 sections. The first part provides a broad survey of some of the applications of AI and machine learning in construction, and the potential impact. These processes are making changes across various areas, including risk management, schedule management, subcontractor management, construction site environment monitoring, and safety, to name a few. In the second part, the focus will be on construction industry leaders who will talk about their experiences with smarter tools in their daily jobs, and their views of the impact that these tools might have in the short and long terms. For more, visit Autodesk University online.
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’.
⭐️ Content Description ⭐️ In this video, I have explained about speech emotion recognition analysis using python. This is a classification project in deep learning. I have build a LSTM neural network to build a classifier. GitHub Code Repo: http://bit.ly/dlcoderepo Dataset link: https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess 🔔 Subscribe: http://bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: https://calendly.com/hackersrealm/consult 📷 Instagram: https://www.instagram.com/aswintechguy 🔣 Linkedin: https://www.linkedin.com/in/aswintechguy 🎯 GitHub: https://github.com/aswintechguy 🎬 Share: https://youtu.be/-VQL8ynOdVg ⚡️ Data Structures & Algorithms tutorial playlist: http://bit.ly/dsatutorial 😎 Hackerrank problem solving solutions playlist: http://bit.ly/hackerrankplaylist 🤖 ML projects tutorial playlist: http://bit.ly/mlprojectsplaylist 🐍 Python tutorial playlist: http://bit.ly/python3playlist 💻 Machine learning concepts playlist: http://bit.ly/mlconcepts ✍🏼 NLP concepts playlist: http://bit.ly/nlpconcepts 🕸️ Web scraping tutorial playlist: http://bit.ly/webscrapingplaylist Make a small donation to support the channel 🙏🙏🙏:- 🆙 UPI ID: hackersrealm@apl 💲 PayPal: https://paypal.me/hackersrealm 🕒 Timeline 00:00 Introduction to Speech Emotion Recognition 03:51 Import Modules 06:20 Load the Speech Emotion Dataset 12:34 Exploratory Data Analysis 25:20 Feature Extraction using MFCC 38:20 Creating LSTM Model 45:37 Plot the Model Results 49:15 End #speechemotionrecognition #machinelearning #hackersrealm #deeplearning #classification #lstm #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3n7saLk Professor Christopher Manning & PhD Candidate Abigail See, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule
(Pieter Abbeel, UC Berkeley | Covariant) Pieter Abbeel is Professor at UC Berkeley, where he is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, with main emphasis on deep reinforcement learning, meta-learning. His lab also investigates how AI could advance other science and engineering disciplines. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework and exams), Covariant (AI for robotic automation of warehouses and factories). Abbeel is also the host of The Robot Brains Podcast. Abbeel has received many awards and honors, including the PECASE, NSF-CAREER, ONR-YIP, Darpa-YFA, TR35. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.
Unlock the full self-paced class from Databricks Academy! Introduction to Data Science and Machine Learning (AWS Databricks) https://academy.databricks.com/course… Introduction to Data Science and Machine Learning (Azure Databricks) https://academy.databricks.com/course… In this video, Conor Murphy introduces the core concepts of Machine Learning and Distributed Learning, and how Distributed Machine Learning is done with Apache Spark. He also sets up the goal of the entire video series: building an end-to-end machine learning pipeline using Databricks. Download the code here: https://files.training.databricks.com/classes/ml/ml-on-spark.dbc Don’t have a Databricks Account? Sign up for Community Edition: https://databricks.com/try-databricks This is Part 1 of our Introduction to Machine Learning Video Series: https://www.youtube.com/playlist?list=PLroeQp1c-t3pT3_d6JmjVnBdOKpyeQtQr About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unified-data-analytics-platform 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/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner
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;
Machine Learning, Abstract Thought, and the Expanding Reach of AI: Ethical and Conceptual Frontiers A conference and workshop co-hosted by the Institute for Research in Sensing (IRiS) and the Department of Philosophy at the University of Cincinnati, with support from the Taft Research Center.
In this video, learn about the importance of MLOps and the processes associated with it. Download the 30-day learning journey for machine learning on Azure – https://azure.microsoft.com/en-us/overview/ai-platform/data-scientist-resources/ #Microsoft #Azure
🔥Enroll for Free AI Course & Get Your Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AIMLFCJan15&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥Enroll for Free Machine Learning Course & Get Your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=AIMLFCJan15&utm_medium=DescriptionFirstFold&utm_source=youtube This Artificial Intelligence and Machine Learning full course video cover all the topics that you need to know to become a master in the field of AI and Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will also help us understand the basics of artificial intelligence. We will look at the future of AI and listen to some of the industry experts and learn what they have to say about AI. Finally, you will learn the Top 10 Artificial Intelligence Technologies In 2021. ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Artificial Intelligence tutorial videos: https://www.youtube.com/watch?v=ad79nYk2keg&list=PLEiEAq2VkUULyr_ftxpHB6DumOq1Zz2hq #ArtificialIntelligence #MachineLearning #AIAndMachineLearning #ArtificialIntelligenceFullCourse #MachineLearningFullCourse #MachineLearningTutorial #AITutorial #AIAndML #Simplilearn What Exactly is Machine Learning? A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, with Machine Learning, computers find insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process. What is Artificial Intelligence? Artificial Intelligence is a method of making a [More]
How to learn AI for Free : https://goo.gl/6sz9J5 Future Updates : https://goo.gl/fNTNAr Developers who are moving towards Artificial intelligence and Machine learning are confused as which language is the best choice. This video answers that. In this video we will see : – Which is the best language to learn AI – In AI language doesn’t matter. Language is needed when we want to implement the algorithm. – Selection of language based on Ranking Area of work Background – Ranking Based on use and number of libraries available the top 5 languages are : 1. Python 2. Java 3. C/C++ 4. Javascript 5. R language – Area of work or Domain : If working on Natural Language Processing or Sentiment Analysis then Python is best language as there are many libraries available If domain is Enterprise like Big Data, Fraud detection, Network security then best language is Java If working on Robots, Speech recognition, Games then no doubt go for C and C++ – Tensorflow.js – Javascript – R language Editing Monitors : https://amzn.to/2RfKWgL https://amzn.to/2Q665JW https://amzn.to/2OUP21a. Check out our website: http://www.telusko.com Follow Telusko on Twitter: https://twitter.com/navinreddy20 Follow on Facebook: Telusko : https://www.facebook.com/teluskolearn… Navin Reddy : https://www.facebook.com/navintelusko Follow Navin Reddy on Instagram: https://www.instagram.com/navinreddy20 Subscribe to our other channel: Navin Reddy : https://www.youtube.com/channel/UCxmk… Telusko Hindi : https://www.youtube.com/channel/UCitz… Donation: PayPal Id : navinreddy20 Patreon : navinreddy20 http://www.telusko.com/contactus
So, there’s a lot of speculation and many opinions I hear in my space, (recruiting). Where people say that AI and Machine Learning is going to take over the world and kill everyone, and along with that, ruin the entire recruiting industry for recruiters and recruiting agencies. Not true, not going to happen. (argue in comment section below). Or you can argue in my FB GROUP (Link Below) Six – Figure Recruiters Facebook Group https://www.facebook.com/groups/372761016618589/ ALSO, you should LIKE & FOLLOW The Six – Figure Recruiter FB Page (Link Below) https://www.facebook.com/TheSixFigureRecruiter/ ======================================================== Now that you’ve done that, let me elaborate. What is A.I and Machine Learning in the first place? Well in the most uneducated and rough description in the world for multiple purposes (my own lack of brain cells, and for those that need it dumbed down), it’s basically the collection of tons of data, and information which is then used by algorithms created by massively smart people which are then injected into both physical and digital tools that we use to enhance our use of them thus making life easier. OK, cool right? Now, here’s the deal with recruiting… Recruiting has and always will be a RELATIONSHIP business. As in, it’s built upon a relationship and the value you bring to that relationship ie: the candidates and their caliber and your timing and abilities to find that talent. If you do all of that well, then great, you’re awesome and you’ll continue to be awesome. Of course, most [More]
🔥Free Machine Learning Course With Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearningFC&utm_medium=DescriptionFirstFold&utm_source=youtube In this video on Machine Learning with Python full course, you will understand the basics of machine learning, essential applications of machine learning, machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We’ll also learn about regularization, dimensionality reduction, PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap for 2021? 00:00:00 Machine Learning Basics 00:08:45 Top 10 applications of machine learning 00:13:40 Machine Learning Tutorial Part-1 0:14:26 Why Machine Learning 0:18:33 What is Machine Learning 0:25:15 Types of Machine Learning 0:25:27 Supervised Learning 0:27:47 Reinforcement Learning 0:29:07 Supervised vs Unsupervised Learning 0:39:23 Decision Trees 01:15:10 Machine Learning Tutorial Part-2 01:19:47 K-Means Algorithm 02:10:47 Mathematics for Machine Learning 2:11:15 What is Data? 02:12:07 Quantitative/Categorical Data 02:14:54 Qualitative/Categorical Data 02:15:12 Linear Algebra 02:38:01 Calculus 02:52:21 Statistics 03:05:16 Demo on Statistics 03:22:27 Probability 03:48:09 Demo on Naive Bayes 04:01:00 Linear Regression Analysis 04:20:37 Logistic Regression 04:38:35 Confusion Matrix 04:58:31 Decision Tree in Machine Learning 05:20:30 Random Forest 05:50:29 K Nearest Neighbors 06:16:56 Support Vector Machine 06:35:57 Regularization in ML 07:05:03 PCA 07:35:16 US Election Prediction 08:03:49 Machine Learning roadmap 2021 ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4 #MachineLearningCourse #MachineLearningFullCourse #MachineLearningWithPython #MachineLearningWithPythonFullCourse #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #MachineLearningTraining #Simplilearn Dataset Link – https://drive.google.com/drive/folders/15lSrc4176J9z9_3WZo_b91BaNfItc2s0 About Machine Learning Certification Course: Explore this Machine Learning certification course to understand cutting-edge concepts [More]
Stock Price Prediction Using Python & Machine Learning (LSTM). In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock. Disclaimer: The material in this video is purely educational and should not be taken as professional investment advice. Invest at your own discretion. NOTE: In the video to calculate the RMSE I put the following statement: rmse=np.sqrt(np.mean((predictions- y_test)**2)) When in fact I meant to put : rmse=np.sqrt(np.mean(((predictions- y_test)**2))) You can use the following statements to calculate RMSE: 1. rmse =np.sqrt(np.mean(((predictions- y_test)**2))) 2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2))) 3. rmse = np.sqrt(((predictions – y_test) ** 2).mean()) Please Subscribe ! ⭐Get the code here⭐: https://www.patreon.com/computerscience ▶️ Get 4 FREE stocks (valued up to $1600) on WeBull when you use the link below and deposit $100 or more: https://act.webull.com/kol-us/share.html?hl=en&inviteCode=LR6VIpFiAkPe ▶️ Earn $10 in Bitcoin by signing up with BlockFi and depositing $100 or more: https://blockfi.com/?ref=e5b523e0 ⭐Please Subscribe !⭐ ⭐Support the channel and/or get the code by becoming a supporter on Patreon: https://www.patreon.com/computerscience ⭐Websites: ► http://everythingcomputerscience.com/ ⭐Helpful Programming Books ► Python (Hands-Machine-Learning-Scikit-Learn-TensorFlow): https://amzn.to/2AD1axD ► Learning Python: https://amzn.to/3dQGrEB ►Head First Python: https://amzn.to/3fUxDiO ► C-Programming : https://amzn.to/2X0N6Wa ► Head First Java: https://amzn.to/2LxMlhT ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 📚Helpful Financial Books📚 ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🌟Stock Market Investing Books: ✔️The Bogleheads’ Guide to Investing https://amzn.to/3s0icxA ✔️The Intelligent Investor https://amzn.to/34Mj7t1 ✔️A Random Walk Down Wall Street https://amzn.to/3Bv0ghW 🌟Money Mindset Books ✔️Rich Dad Poor Dad: https://amzn.to/3rZW6eE ✔️Get Good With Money: Ten Simple Steps To Becoming Financially Whole: https://amzn.to/3I1UXc1 #StockPrediction #Python #MachineLearning
Live knowledge sharing sessions by industry experts on latest and trending skills and technologies. This One Hour session will provide the participants with an insight into the latest industrial standards and applications on the desired domain. Reg link : https://bit.ly/3dfmMgU For E_Certificate Feedback form entry is mandatory For Previous Videos:-https://www.youtube.com/playlist?list=PLDvagq1BEqnB-oxVvv-PZqI6F9jY0FJAe #NoviTech #SpeechEmotionDetection #MachineLearning
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]
Hello Friends welcome to Well Academy In this video i am going to Share with you Top 5 Channels to Learn Machine Learning and you can Boost up your Machine Learning Skill with the help of this Youtube Channels and there Machine Learning Tutorials. This channels are the Best Machine Learning Youtube Channels which are mainly Focusing on Machine Learning Content. 1) Simplilearn : https://www.youtube.com/user/Simplilearn 2) Siraj Raval : https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A 3) Sendex https://www.youtube.com/user/sentdex 4) Welch labs https://www.youtube.com/user/Taylorns34 5) Lauis serrano https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ ******************************************************************** To get Each and Every Update of Videos Join Our Telegram Group click on the below link to join 👉 👉 https://t.me/wellacademy ******************************************************************** Below are Links of video lectures of GATE Subjects ******************************************************************** 👉 DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D 👉 Discrete Mathematics GATE | discrete mathematics for computer science gate | NET | PSU : https://www.youtube.com/playlist?list=PL9zFgBale5fvLZEn6ahrwDC2tRRipZQK0 👉 Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy 👉 Computer Organization and Architecture GATE (Hindi) | Computer Organization GATE | Computer Organization and Architecture Tutorials : https://www.youtube.com/playlist?list=PL9zFgBale5fsVaOVUqXA1cJ22ePKpDEim 👉 Theory of Computation GATE Lectures | TOC GATE Lectures | PSU | GATE : https://www.youtube.com/playlist?list=PL9zFgBale5ftkr9FLajMBN2R4jlEM_hxY ******************************************************************** Click here to subscribe well Academy 👉 https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group 👉 https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : 👉 Facebook page : https://www.facebook.com/wellacademy/ 👉 Instagram page : https://instagram.com/well_academy 👉 Twitter : https://twitter.com/well_academy
See what’s new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r Machine learning is quickly becoming a powerful tool for solving complex modeling problems across a broad range of industries. It is enabling engineers and scientists to develop models which learn from data and can be deployed as a part of packaged applications that can run efficiently on embedded systems as well as cloud infrastructure. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. However, successfully applying machine learning in practice presents several challenges. It is not always clear which data is going to be the most useful for prediction, and tuning machine learning hyperparameters can consume a large amount of time. In this webinar, you will learn how machine learning tools in MATLAB address these challenges. We will demonstrate: Working with large out-of-memory data using the MATLAB “tall” framework Reducing dimensionality and identifying import features using advanced feature selection techniques Best practices for tuning hyperparameters to optimize the performance of your model How to deploy models for use in production IT systems or embedded devices
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]
SASTRA Day 1, Session 03 ATAL AICTE FDP on AI,ML u0026DL 2020 09 14 at 01 18 GMT 7 Workshop topics – Introduction to Artificial Intelligence – Introduction to Python – Introduction to Internet of Things(IoT) – Problem Formulations & Representations – Uninformed and Informed Search Algorithms – Knowledge Representation and different types of Knowledge Representation – Ontology Engineering – Fuzzy and Temporal Logic Systems – Natural Language Processing – Machine Learning and Deep Learning – Reinforcement Learning – Application and current trends of AI – Sample Problems – Case Studies & hands-on Coding using Python for the above topics Full playlist: https://www.youtube.com/playlist?list=PL7Ld-_6sF_dqunfOyV4TuztyvbbhxZWJO