It’s time to reveal the magician’s secrets behind deploying machine learning models! In this tutorial, I go through an example machine learning deployment scenario using Google Cloud and an image recognition app called Food Vision 🍔👁. Get all the code on GitHub – Slides – Full CS329s syllabus – Learn ML (my beginner-friendly ML course) – Connect elsewhere: Web – Get email updates on my work – Timestamps: 0:00 – Intro/hello 1:42 – Presentation start (what we’re going to cover) 6:00 – Food Vision 🍔👁 (the app we’re building) recipe 11:16 – The end goal we’re working towards (data flywheel) 13:07 – The data flywheel: the holy grail of ML apps 14:57 – Tesla’s data flywheel 17:02 – Food Vision’s data flywheel 18:24 – Deploying a model on the cloud outline 21:14 – Steps we’re going to go through to deploy our app 27:06 – Question: “How do you identify hard samples in your data?” 37:53 – Creating a bucket on Google Storage 45:51 – Uploading to Google Storage from Google Colab 48:02 – Deploying a model to AI Platform 52:50 – Creating an AI Platform Prediction version 58:10 – Creating a Service Account to access our model on Google Cloud 1:02:32 – Authenticating our app with our private Service Account key 1:09:19 – What happens when we run make gcloud-deploy 1:11:27 – Problems you’ll run into when deploying your models 1:20:12 – Extensions you could perform on this tutorial 1:20:49 – Part 2 [More]
Here are a list of free resources for you to learn machine learning. I’ve shown resources that I used to learn machine learning and deep learning. The best part about them is that they are free and available on YouTube and GitHub. Many of you were asking me how to learn machine learning in 2020 in the comments, so I had to make this one! Here are the links for them: 1. Linear Algebra Freecodecamp: 2. Linear Algebra MIT OCW: 3. Linear Algebra 3 Blue 1 Brown: 4. Probability and Stats: 5. Machine Learning Stanford by Andrew Ng: 6. Practical Deep Learning by FastAi: 7. Stanford CS231N: 8. Tech with Tim Machine Learning Mega Course: 9. Awesome Deep Learning GitHub: (Includes a ton of resources to learn machine learning) 10. Lex Fridman: 11. Vincent Boucher: 12. Daniel Bourke Machine Learning: 🌟 Please leave a LIKE ❤️ and SUBSCRIBE for more AMAZING content! 🌟 Hey!! I am Ishan Sharma, Second year Student at 📍 BITS Pilani, Goa 🏫 pursuing Electrical Engineering 🔌. I enjoy reading books 📚 and solving problems 📝 using Computer Science 💻. This channel is on college, growth 📈 and everything in between. New videos every week 📅. We’ll be talking about productivity ⏳, work life balance, career and more. 📸 Instagram: 🌎 Website: 📱 Twitter: 📝 LinkedIn: 📂 GitHub: 📕Buy My Book – Crush It on LinkedIn: 🎥Channel Playlists [More]
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. Connect with us: Website: Facebook: Twitter: LinkedIn: Instagram:
Dr. Dennis Ong is a Technology Evangelist. Dr. Ong is Distinguished Architect and Managing Principal at Verizon. Previously, he served as Chief Architect and Director at Nokia, Alcatel-Lucent, Lucent, and AT&T. He and his teams have launched many innovative and award-winning solutions combining the strengths of high tech companies and multitude of start-up companies. At Verizon, he is working with startups to utilize IoT and Machine Learning to create Smart City solutions to address the most challenging problems facing cities and municipalities. At Nokia, he led a recently acquired start-up to develop the IoT IMPACT platform which received the “”Best IoT Innovation for Mobile Networks”” award at the 2017 Mobile World Congress. At Alcatel-Lucent, he and his team collaborated with three start-ups, based in India, Israel, and Silicon Valley, in creating a highly scalable video optimization and analytics platform that served tens of millions of mobile subscribers worldwide. At Lucent, he launched the first packet-based cellular small cell solution with a start-up in Boston. Originally from Hong Kong, Dennis received Ph.D. in Electrical Engineering from the Ohio State University as University Fellow and MBA with honors from the University of Chicago. He was an adjunct faculty at the Ohio State University. Dennis and his wife, Timmy, enjoy coaching Christian marriage retreats. They are proud parents of three children – Joshua, Jeremiah, and Hannah. Dennis is an active learner and an avid swimmer. Dr. Dennis Ong is Distinguished Architect and Managing Principal at Verizon. Previously, he served as Chief Architect and [More]
We’ve learned how to train different machine learning models and make predictions, but how do we actually choose which model is “best”? We’ll cover the train/test split process for model evaluation, which allows you to avoid “overfitting” by estimating how well a model is likely to perform on new data. We’ll use that same process to locate optimal tuning parameters for a KNN model, and then we’ll re-train our model so that it’s ready to make real predictions. Download the notebook: Quora explanation of overfitting: Estimating prediction error: Understanding the Bias-Variance Tradeoff: Guiding questions for that article: Visualizing bias and variance: WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: 2) SUBSCRIBE for more videos: 3) JOIN “Data School Insiders” to access bonus content: 4) ENROLL in my Machine Learning course: 5) LET’S CONNECT! – Newsletter: – Twitter: – Facebook: – LinkedIn:
MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020 Instructor: Jeremy Kepner, Vijay Gadepally View the complete course: YouTube Playlist: This lecture provided an overview on artificial intelligence and took a deep dive on machine learning, including supervised learning, unsupervised learning, and reinforcement learning. License: Creative Commons BY-NC-SA More information at More courses at
** Machine Learning Training with Python: ** This Edureka video on ‘Mathematics for Machine Learning’ teaches you all the math needed to get started with mastering Machine Learning. It teaches you all the necessary topics and concepts of Linear Algebra, Multivariate Calculus, Statistics, and Probability and also dives into the actual implementation of these topics. Blog Link: Check out our playlist for more videos: —————————————————————————— Subscribe to our channel to get video updates: Hit the subscribe button above. Edureka Community: Instagram: Facebook: Twitter: LinkedIn: SlideShare: #Edureka #MachineLearning #MathematicsForMachineLearning # —————————————————————————— How does it work? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you with a Grade and a Verifiable Certificate! —————————————————————————— About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You [More]
🔥 Get the pdf of this course: 🔥 Great Learning brings you this live session on ‘Predicting COVID-19 With Machine Learning’.In this session, we will take a COVID-19 dataset and understand how the disease has spread across different countries. We will perform some data manipulation and data visualization operations on top of the dataset. We will also be implementing a linear regression algorithm to understand the number of active and recovered cases Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. The data-set used is from ‘Our World in Data’. You can download the dataset from this link: Get the free Great Learning App for a seamless experience, enrol for free courses and watch them offline by downloading them. About Great Learning: – Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. – For more interesting tutorials, don’t forget to subscribe to our channel: – Learn More at For more updates [More]
Can you predict the Bitcoin Price with Machine Learning? It seems like it’s possible! Using an LSTM algorithm, I showcase how you can use machine learning to predict prices of cryptocurrencies. Machine Learning can most definitely be used as a support in your bitcoin investing, and as a predictor of the price of cryptocurrencies. Find the code at: Resources: PEACE! ———————————————————————————————- JOIN NO PMO NATION 👬: ———————————————————————————————- 👬 Instagram: ———————————————————————————————- JOIN THE ARMY OF HAPPIER AND STRONGER PEOPLE 👬: ———————————————————————————————- 🎓 SUBSCRIBE ON YOUTUBE: 🎓 JOIN US ON SLACK: 🎓 JOIN MY EXCLUSIVE MAILING LIST: ———————————————————————————————- POPULAR EDUCATION SERIES 💝: ———————————————————————————————- 🎓 MASTER NOFAP: 🎓 BECOME HAPPIER: 🎓 ATTRACT WOMEN: 🎓 MACHINE LEARNING: 🎓 ARTIFICIAL INTELLIGENCE: ———————————————————————————————- HOW TO ASK OSCAR QUESTIONS 🎤: ———————————————————————————————- 👬 MESSAGE ME ON INSTAGRAM: 👬 ASK ME ON SLACK: Linkedin: Facebook: Website: ———————————————————————————————- PRODUCTS I LOVE ❤️: ———————————————————————————————- LIFE-CHANGING BOOKS: MY CAMERA/PROGRAMMING GEAR: ———————————————————————————————- ABOUT OSCAR 💝: ———————————————————————————————- Oscar is a leader, educator and programmer specialised in Artificial Intelligence and Machine Learning who strives to build a world where all leadership spawns from an intrinsic compassion for others. He is heavily interest in mindfulness and meditation and is a daily Brazilian Jiu-Jitsu practitioner. Furthermore, he Loves lifting heavy things and reads a lot of books and believes in a world where compassion and mutual understanding and respect permeate all of our actions. 🎉 Leader of [More]
Secretly aspire to be a fortune teller to impress your friends? What to build a fun python project? What if you could predict the iPhone price? Yes, even for the latest iPhone 12. #python #project #tutorial You can do this and that too with just 6 simple lines of python code. WHAT IS THE VIDEO ABOUT? • Predict iPhone (especially iPhone 12) price and show off your skills with just 6 lines of Python code Complete Code (give us a star): #python #machine #learning #machinelearning #iphone #iphone12 #apple #pythonhack #funpython #beginners #iphoneprice #iphonefuture Now, if you’re new to the programming world and don’t know what to do, go check out our app and build your own game immediately while learning. Android App: iPhone Version: CHECK OUT If you hate to study, let’s hear it. Turn your books into audiobooks today: ENJOYED THE VIDEO? Save yourself from our Grandma ⁠— she’ll come to your house to steal your old iPhone charger and sell it to Tim Cook if you don’t click on the Like button and also turn the Subscribe button from red to white. If you like and subscribe, she will be ready to make love with you. 😉 OUR SOCIAL MEDIA Watch us on Facebook: Peep us on Instagram: Fly with us on Twitter: Board with us on Pinterest: Don’t SHARE this with your friends. They’ll know your secret. We’re always with you. Feel free to mail us anytime you need [More]
In this video, make sure you define the X’s like so. I flipped the last two lines by mistake: X = np.array(df.drop([‘label’],1)) X = preprocessing.scale(X) X_lately = X[-forecast_out:] X = X[:-forecast_out:] To forecast out, we need some data. We decided that we’re forecasting out 10% of the data, thus we will want to, or at least *can* generate forecasts for each of the final 10% of the dataset. So when can we do this? When would we identify that data? We could call it now, but consider the data we’re trying to forecast is not scaled like the training data was. Okay, so then what? Do we just do preprocessing.scale() against the last 10%? The scale method scales based on all of the known data that is fed into it. Ideally, you would scale both the training, testing, AND forecast/predicting data all together. Is this always possible or reasonable? No. If you can do it, you should, however. In our case, right now, we can do it. Our data is small enough and the processing time is low enough, so we’ll preprocess and scale the data all at once. In many cases, you wont be able to do this. Imagine if you were using gigabytes of data to train a classifier. It may take days to train your classifier, you wouldn’t want to be doing this every…single…time you wanted to make a prediction. Thus, you may need to either NOT scale anything, or you may scale the data separately. As [More]
#ArtificalIntelligence #MachineLearning #collegesuggest #KnowYourCourse Welcome to College Suggest! Let’s take a look at one of the most innovative and widely popular courses available today – CSE with a specialization in Artificial Intelligence and Machine Learning, which offers great opportunities due to plenty of demand for skilled professionals. 00:00 Intro 01:10 What exactly is artificial intelligence? 1:54 What to study? 02:57 Core components of AI and ML 03:34 Where to study? 04:54 Why Artificial Intelligence? 06:02 Job roles 06:59 Salary trends 07:38 Top Recruiters 08:22 Salary 09:16 Tips 09:58 How to stay updated? 10:12 Reasons to study AI & ML
🔥Edureka AWS Training: This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: 🔹Check Edureka’s Blog playlist here: ——————————————————————————————– 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: Twitter: LinkedIn: Instagram: Facebook: SlideShare: Castbox: Meetup: #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]
Machine Learning courses and articles from coursera, edX, Udacity, dataCamp to udemy. Here are the resources link collection: —- R Language —- 1. 2. —- Python Language —- 1. 2. —- Java Language —- 1. 2. EdX : Course Lists 3. Coursera: Course List —- Machine Learning fundamentals —- 1. EdX: 2. Udacity:–ud120-india 3. Udemy: 4. Coursera: 5. Google —- ML Tools and packages —- 1. NumPy, SciPy, matplotlib a. EdX 2. TensorFlow a. Coursera : b. EdX : 3.Scikit Learn a. b. Udemy c. 4. Pandas a. —- Nano Degrees —- 1. IBM : 2. Coursera 3. EdX Microsoft : 4. Nanodegree–nd002-inpy —- Machine Learning Maths —- 1. 2. EdX 3. Coursera Udemy 4. 5. 6. I am sure this will help you and please share this video — Follow me — : Twitter – Facebook – Instagram – (ask me questions!) — QUESTIONS? — Leave a comment below and I or someone else can help you. For quick questions you may also want to ask me on Twitter, I respond almost immediately. Email me Thanks for all your support!
Intellipaat Artificial Intelligence Course:- Artificial Intelligence Webinar video is an introduction to what is Ai?, what is Deep Learning?, Industries getting disrupted by AI & Deep Learning, Machine Learning vs AI, Robotics, Tensorflow, Career in AI & Future of AI in this Artificial Intelligence Tutorial in detail. Interested to learn Deep Learning & Machine Learning still more? Please check similar Artificial Intelligence Tutorial and other Artificial Intelligence Course Blogs here:- Watch complete Artificial Intelligence, Deep Learning & Machine Learning tutorials here:- This Artificial Intelligence Tutorial conference video helps you to learn following topics: 12:48 – What is Ai? 20:18 – Artificial Intelligence history 24:10 – How A.I. Works? 27:17 – What is Deep Learning? 31:37 – Industries getting disrupted by A.I. 35:30 – Applications of Artificial Intelligence 44:55 – Future of AI 53:33 – Job Trends in Artificial Intelligence Are you looking for something more? Enroll in our Artificial Intelligence Course and become a certified A.I. Professional ( It is a 32 hrs instructor led AI for everyone training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you’ve enjoyed this Deep Learning, Machine Learning and Robotics tutorial, Like us and Subscribe to our channel for more similar Robotics, Machine Learning vs AI videos and free tutorials. Got any questions about Artificial Intelligence Course & Future of AI? Ask us in the comment section below. —————————- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance [More]
Jean-Francois Bonnefon, Toulouse School of Economics, held a keynote, “The Moral Machine Experiment”, at IJCAI-ECAI 2018, the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, the premier international gathering of researchers in AI.
Download the audio at InfoQ: Maciej Ceglowski wonders what tech companies can do to reduce the amount of data collected, closing the path to mass surveillance and bringing some morality in using ML with this data. This presentation was recorded at ETE 2017. For more awesome presentations on innovator and early adopter topics check InfoQ’s selection of talks from conferences worldwide
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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]
In this video we will understand how we can implement Diabetes Prediction using Machine Learning. The dataset is taken from Kaggle. Please subscribe and support the channel. github url: Data Science Projects playlist: NLP playlist: Statistics Playlist: Feature Engineering playlist: Computer Vision playlist: Data Science Interview Question playlist: You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url:
Are Machine Learning Certifications worth it and which are the best ones out there? There are a lot of new machine learning certifications out on the Internet and you might be wondering which one will truly help you land that machine learning or data science role. In this video, I explain in detail exactly how some top machine learning certifications are structured. I also explain which one’s are worth it and why they might be worth it for certain people and why they might not be. A well-known machine learning certificate will add a tremendous amount of value to your resume when apply to machine learning roles, ESPECIALLY if you don’t have a traditional computer science degree. However combined with a degree in computer science, it will truly make you an asset. —————————LINKS—————————————– DOWNLOAD Machine Learning Roadmap 2021:​ #machinelearning​​ #machinelearningjob​​ #learnml​​ #learnmachinelearning​​ #machinelearningintern​​ #internship​​ #machinelearningcertification #tensorflowdeveloper #tensorflow #sas ————————————————————————– MORE VIDEOS: ————————————————————————– 📌Why You Should NOT Learn Machine Learning! 📌How I Learnt Machine Learning In 6 Steps (3 months) 📌How To Learn Machine Learning For Free ————————————————————————– Follow me: ————————————————————————– Subscribe:​​ LinkedIn:​​ Instagram:​​ background music:
This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing a TFX pipeline and a discussion of current topics in model understanding. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → TensorFlow at Google I/O 2019 Playlist → Google I/O 2019 All Sessions Playlist → Learn more on the I/O Website → Subscribe to the TensorFlow Channel → Get started at → Speaker(s): Kevin Haas , Tulsee Doshi , Konstantinos Katsiapis T02F52 event: Google I/O 2019; re_ty: Publish; product: TensorFlow – TensorFlow Extended; fullname: Tulsee Doshi;
Last year, Databricks launched MLflow, an open source framework to manage the machine learning lifecycle that works with any ML library to simplify ML engineering. MLflow provides tools for experiment tracking, reproducible runs and model management that make machine learning applications easier to develop and deploy. In the past year, the MLflow community has grown quickly: 80 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow. In this talk, we’ll present our development plans for MLflow 1.0, the next release of MLflow, which will stabilize the MLflow APIs and introduce multiple new features to simplify the ML lifecycle. We’ll also discuss additional MLflow components that Databricks and other companies are working on for the rest of 2019, such as improved tools for model management, multi-step pipelines and online monitoring. 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: Connect with us: Website: Facebook: Twitter: LinkedIn: Instagram:
Do you know what Apriori Algorithms are and how to use it for machine learning? Watch this video to find out. ⭐ Buy Me Coffee – ———————————————————— ⭐ 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! ———————————————————— ►FREE YOLO GIFT – ►Ultimate AI-CV Webinar – Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ———————————————————— Support us on Patreon ► Chat to us on Discord ► Interact with us on Facebook ► Check my latest [More]
Thore Graepel, Research Scientist shares an introduction to machine learning based AI as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
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