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In this video, we will look into a parallel conquering technique to learn machine learning from scratch. This video will teach you how to learn machine learning for programmers. Machine learning is a very popular field when it comes to programming domains. Download PDF Here: https://codewithharry.com/blogpost/complete-ml-roadmap-for-beginners TimeStamps: 00:00 – Introduction 01:30 – Step 1: Learn a Language 06:04 – Step 2: Algebra Resources 07:40 – Step 3: Parallel Conquering Technique 10:00 – Step 4: ML Algorithms Resources 14:20 – Step 5: Python Libraries Resources 14:50 – Step 6: Learning Deployment ►Checkout my English channel here: https://www.youtube.com/ProgrammingWithHarry ►Click here to subscribe – https://www.youtube.com/channel/UCeVMnSShP_Iviwkknt83cww Best Hindi Videos For Learning Programming: ►Learn Python In One Video – https://www.youtube.com/watch?v=ihk_Xglr164 ►Python Complete Course In Hindi – https://www.youtube.com/playlist?list=PLu0W_9lII9agICnT8t4iYVSZ3eykIAOME ►C Language Complete Course In Hindi – https://www.youtube.com/playlist?list=PLu0W_9lII9aiXlHcLx-mDH1Qul38wD3aR&disable_polymer=true ►JavaScript Complete Course In Hindi – https://www.youtube.com/playlist?list=PLu0W_9lII9ajyk081To1Cbt2eI5913SsL ►Learn JavaScript in One Video – https://www.youtube.com/watch?v=onbBV0uFVpo ►Learn PHP In One Video – https://www.youtube.com/watch?v=xW7ro3lwaCI ►Django Complete Course In Hindi – https://www.youtube.com/playlist?list=PLu0W_9lII9ah7DDtYtflgwMwpT3xmjXY9 ►Machine Learning Using Python – https://www.youtube.com/playlist?list=PLu0W_9lII9ai6fAMHp-acBmJONT7Y4BSG ►Creating & Hosting A Website (Tech Blog) Using Python – https://www.youtube.com/playlist?list=PLu0W_9lII9agAiWp6Y41ueUKx1VcTRxmf ►Advanced Python Tutorials – https://www.youtube.com/playlist?list=PLu0W_9lII9aiJWQ7VhY712fuimEpQZYp4 ►Object Oriented Programming In Python – https://www.youtube.com/playlist?list=PLu0W_9lII9ahfRrhFcoB-4lpp9YaBmdCP ►Python Data Science and Big Data Tutorials – https://www.youtube.com/playlist?list=PLu0W_9lII9agK8pojo23OHiNz3Jm6VQCH Follow Me On Social Media ►Website (created using Flask) – http://www.codewithharry.com ►Facebook – https://www.facebook.com/CodeWithHarry ►Instagram – https://www.instagram.com/codewithharry/ ►Personal Facebook A/c – https://www.facebook.com/geekyharis Twitter – https://twitter.com/Haris_Is_Here
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