In this new 360 in 360 video I walk through Azure Virtual Network peering including enabling peers to talk to each other, enabling connectivity to other connected networks and key permission considerations.
In this video we go through the major concepts in natural language processing using Python libraries! We use examples to help drill down the concepts. There is content in this video for all skill levels (beginners to experts). I originally recorded this video for the PyCon Conference. GitHub repo: https://github.com/KeithGalli/pycon2020 Patreon: https://www.patreon.com/keithgalli YT Membership: https://www.youtube.com/c/KGMIT/membership Some of the topics we cover: – Bag-of-words – Word vectors – Stemming/Lemmatization – Spell correction – Transformer Architecture (Attention is all you need) – State of the art models (OpenAI GPT, BERT) Some of the libraries used: – sklearn – spaCy – NLTK – TextBlob Hope you enjoy & let me know if you have any questions! Make sure to subscribe if you haven’t already :). ————————- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube – https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon – https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. ————————- Song at the end good morning by Amine Maxwell https://soundcloud.com/aminemaxwell Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoY Music promoted by Audio Library https://youtu.be/SQWFdnbzlgI ————————- Video Timeline! ~~ NLP Fundamentals ~~ 0:00 – Announcements! 1:12 – Video overview & [More]
Will A.I. take over artists? Explore the INSANE world of AI Art and Midjourney, and learn how you can get started with generating art using Artificial Intelligence. Right from discovering the insanity of AI and the incredible images it can create to learning some of the most advanced codes for getting the best results, we will cover it all. In this guide, we will cover every aspect of this new technology and share all the pros, cons, and warnings you need to know before creating and using these art pieces. We will also learn how you can extract codes for inspiration from other artists’ creations, discuss the cost and whether it makes sense, and also learn how you can get more advanced with prompts. I hope you enjoy this video. Thank you so much for watching 🙂 ► TIMESTAMPS: 00:00 Speechless. AI is Unbelievable! 03:03 This Cannot Exist! 05:23 Disclaimer: I’ll Come Clean 05:46 What Is It and How Does It Work? 07:09 How to Get Started 12:39 Price with Pros and Cons 14:39 Advanced Prompt Codes 17:17 Code List and Further Reading 17:59 How to Get Better Results & Copy Command 20:46 Use Your Own Images As Reference 21:47 BIG WARNING! DO NOT MISS THIS! 22:21 Will A.I. Take Over? Your Thoughts? ► IMPORTANT LINKS MENTIONED IN THIS VIDEO: 1. Midjourney: https://www.midjourney.com/ 2. Pratik Naik’s A.I. Art Page: https://www.instagram.com/futurist.ai/ 3. Midjourney User Manual & Code List: https://github.com/midjourney/docs/blob/main/user-manual.md#parameters-to-imagine ► SUPPORT THE CHANNEL & GAIN PREMIUM ACCESS: https://www.patreon.com/piximperfect ► WATCH FOR [More]
All the materials are available in the below link https://courses.ineuron.ai/Mega-Community-Live Time Stamp: 00:00:00 Introduction 00:01:25 AI Vs ML vs DL vs Data Science 00:07:56 Machine LEarning and Deep Learning 00:09:05 Regression And Classification 00:18:14 Linear Regression Algorithm 01:07:14 Ridge And Lasso Regression Algorithms 01:33:08 Logistic Regression Algorithm 02:13:52 Linear Regression Practical Implementation 02:28:30 Ridge And Lasso Regression Practical Implementation 02:54:21 Naive Baye’s Algorithms 03:16:02 KNN Algorithm Intuition 03:23:47 Decision Tree Classification Algorithms 03:57:05 Decision Tree Regression Algorithms 04:02:57 Practical Implementation Of Deicsion Tree Classifier 04:09:14 Ensemble Bagging And Bossting Techniques 04:21:29 Random Forest Classifier And Regressor 04:29:58 Boosting, Adaboost Machine Learning Algorithms 04:47:30 K Means Clustering Algorithm 05:01:54 Hierarichal Clustering Algorithms 05:11:28 Silhoutte Clustering- Validating Clusters 05:17:46 Dbscan Clustering Algorithms 05:25:57 Clustering Practical Examples 05:35:51 Bias And Variance Algorithms 05:43:44 Xgboost Classifier Algorithms 06:00:00 Xgboost Regressor Algorithms 06:19:04 SVM Algorithm Machine LEarning Algorithm
Join us for a 3 part online technical workshop series: Managing the Complete Machine Learning Lifecycle with MLflow. If you’re interested in learning about machine learning and MLflow, this workshop series is for you! Workshop 1 of 3 | Introduction to MLflow: How to Use MLflow Tracking Level: Beginner/Intermediate Data Scientist or ML Engineer Details: This workshop is an introduction to MLflow. Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve these challenges, MLflow (https://mlflow.org/), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, encapsulate models that can be used with many existing tools, and central repository to share models, accelerating the ML lifecycle for organizations of any size. What you will learn: Understand the four main components of open source MLflow—MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each component helps address challenges of the ML lifecycle. – How to use MLflow Tracking to record and query experiments: code, data, config, and results. (https://mlflow.org/docs/latest/tracking.html) – How to use MLflow Projects packaging format to reproduce runs. (https://mlflow.org/docs/latest/projects.html) – How to use MLflow Models general format to send models to diverse deployment tools. (https://mlflow.org/docs/latest/models.html) – How to use Model Registry [More]
Machine Learning Engineer Roadmap: Step by step 6 months learning roadmap for machine learning engineer career. Most of the resources mentioned in this roadmap are free resources. Please follow below steps for learning requires skills for machine learning engineer: https://github.com/codebasics/roadmaps/blob/master/machine-learning-engineer-roadmap-2021/ml_engineer_roadmap_2021.md ⭐️ Timestamps ⭐️ 0:00 Why machine learning? 0:45 Computer Science Fundamentals 1:39 Programming skills 2:22 Data Structure and Algorithms 5:13 Databases 7:51 Numpy, Pandas, matplotlib 11:06 Math and Statistics for ML 12:36 Machine learning 16:19 Deep learning 18:41 Use ML Lifecycle tools Extra Tips ========== * Discord server: Making group and buddies * Participate in kaggle competitions and solve problems 🌎 My Website For Video Courses: https://codebasics.io/ Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. 🔖Hashtags🔖 #machinelearningroadmap #machinelearning #mlroadmap #mlengineer #roadmaptomachinelearning #completeroadmapformachinelearning #mlengineerroadmap 🎥 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/ ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers’.
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take. In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. Like, Subscribe & share Support our Channel Tuitions Tonight
What to do after 12th? This is the biggest question of students have… They take admission for any degree or college due to lack of guidance even though they have a talent and continue to compromise their dreams all their life … Hence to give proper guidance and to use their competence to the fullest, I provide this video. In this video we will discuss details like… • What is Artificial Intelligence • Uses of Artificial Intelligence • Future of Artificial Intelligence • Deference between Artificial Intelligence , Machine Learning , Deep learning , Data Science • Programming Language • Courses Offered • Colleges and university • Eligibility And Entrance Exam • Career opportunities ========================================================================== Important Video Links— • What is IIT ? |How to become IITians – https://youtu.be/T4343tU49vk • Courses After 12th Science – https://youtu.be/W_0feDSRqb4 • B.tech Biotechnology – https://youtu.be/iHwIw9xrC1k • Hotel Management – https://youtu.be/OoMO9PGpcQw • How to become commercial Pilot in India – https://youtu.be/5-avgLSBHik • NDA Exam Complete Details – https://youtu.be/B7uslNQOhPU • How to become a CA (Chartered Accountant) – https://youtu.be/3wTkmcachKw • Courses After 12th Arts – https://youtu.be/frJrqxqsTJ4 ========================================================================== Follow us on…. YouTube Subscribe here– https://www.youtube.com/channel/UCUhtX-XvPY3MlyRPmvviXqA Instagram link– https://www.instagram.com/ementor.pratikraut/ Facebook link– https://www.facebook.com/ementor.pratikraut Twitter link– https://twitter.com/iampratikraut Image and video by pixabay.com #ArtificialIntelligence #CareerinAI #Ementor
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence. Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning. Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems. ⭐️ Google Colaboratory Notebooks ⭐️ 📕 Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true 📗 Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true 📘 Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true 📙 Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true 📔 Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true 📒 Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true ⭐️ Course Contents ⭐️ ⌨️ (00:03:25) Module 1: Machine Learning Fundamentals ⌨️ (00:30:08) Module 2: Introduction to TensorFlow ⌨️ (01:00:00) Module 3: Core Learning Algorithms ⌨️ (02:45:39) Module 4: Neural Networks with TensorFlow ⌨️ (03:43:10) Module 5: Deep Computer Vision – Convolutional Neural Networks ⌨️ (04:40:44) Module 6: Natural Language Processing with RNNs ⌨️ (06:08:00) Module 7: Reinforcement Learning with Q-Learning ⌨️ (06:48:24) Module 8: Conclusion and Next Steps [More]
This half hour tutorial takes you step by step through the fundamental concepts you need to know to build a no code chatbot with Power Virtual Agents. Watch and pause and build your own bot side by side with this step-by-step instructional video. You will learn: – How to create a bot – What topics are and how to create them – How to use variables to store information from the user response for the bot to use later – What entity extraction is and how it enables the bot to have a natural conversation, including skipping questions – How to test your bot, and publish it to a demo website to share with others – How to use Power Automate to call an action – in this example we post information from a bot chat into Microsoft Teams – How to use the topic redirect feature
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take. In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. #Part2 #AIforEveryone #AIbyAndrewNg Like, Subscribe & share Support our Channel Tuitions Tonight
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Understand aspects of MLflow APIs Using tracking APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics Package, save, and deploy an MLflow model Serve it using MLflow REST API What’s next and how to contribute