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🔥Post Graduate Program In AI And Machine Learning: 🤍 This Machine Learning basics video will help you understand what Machine Learning is, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into self-learning mode without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. The below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? ( 00:21 ) 2. Types of Machine Learning ( 02:43 ) 2. What is Supervised Learning? ( 02:53 ) 3. What is Unsupervised Learning? ( 03:46 ) 4. What is Reinforcement Learning? ( 04:37 ) 5. Machine Learning applications ( 06:25 ) 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 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 toward your dream career- 🤍 Watch more videos on Machine Learning: 🤍 #MachineLearning #WhatIsMachineLearning #MachineLearningTutorial #MachineLearningBasics #MachineLearningTutorialForBeginners #Simplilearn ➡️ Professional Certificate Program In AI And Machine Learning In collaboration with Purdue, our AI and ML Course will help you unlock your AI and ML potential. You'll study Machine Learning, Deep Learning, Computer Vision, NLP, Speech Recognition, and Reinforcement Learning with Simplilearn's AI ML course online. ✅ Key Features - Professional Certificate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 8X higher live interaction in live online classes by industry experts - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist help ✅ Skills Covered - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision -GANs - Keras - Tensorflow - Reinforcement Learning - Speech Recognition - Recommendation Systems - Ensemble Learning - NumPy 👉Learn More at: 🤍 For more updates on courses and tips follow us on: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
Machine Learning is the process of teaching a computer how perform a task with out explicitly programming it. The process feeds algorithms with large amounts of data to gradually improve predictive performance. #ai #python #100SecondsOfCode 🔗 Resources Machine Learning Tutorials 🤍 What is ML 🤍 Neural Networks 🤍 ML Wiki 🤍 🔥 Watch more with Fireship PRO Upgrade to Fireship PRO at 🤍 Use code lORhwXd2 for 25% off your first payment. 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font Topics Covered - Convolutional Neural Networks - Machine Learning Basics - How Data Science Works - Big Data and Feature Engineering - Artificial Intelligence History - Supervised Machine Learning
Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts. ✏️ Kylie Ying developed this course. Check out her channel: 🤍 ⭐️ Code and Resources ⭐️ 🔗 Supervised learning (classification/MAGIC): 🤍 🔗 Supervised learning (regression/bikes): 🤍 🔗 Unsupervised learning (seeds): 🤍 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) 🔗 MAGIC dataset: 🤍 🔗 Bikes dataset: 🤍 🔗 Seeds/wheat dataset: 🤍 🏗 Google provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:58) Data/Colab Intro ⌨️ (0:08:45) Intro to Machine Learning ⌨️ (0:12:26) Features ⌨️ (0:17:23) Classification/Regression ⌨️ (0:19:57) Training Model ⌨️ (0:30:57) Preparing Data ⌨️ (0:44:43) K-Nearest Neighbors ⌨️ (0:52:42) KNN Implementation ⌨️ (1:08:43) Naive Bayes ⌨️ (1:17:30) Naive Bayes Implementation ⌨️ (1:19:22) Logistic Regression ⌨️ (1:27:56) Log Regression Implementation ⌨️ (1:29:13) Support Vector Machine ⌨️ (1:37:54) SVM Implementation ⌨️ (1:39:44) Neural Networks ⌨️ (1:47:57) Tensorflow ⌨️ (1:49:50) Classification NN using Tensorflow ⌨️ (2:10:12) Linear Regression ⌨️ (2:34:54) Lin Regression Implementation ⌨️ (2:57:44) Lin Regression using a Neuron ⌨️ (3:00:15) Regression NN using Tensorflow ⌨️ (3:13:13) K-Means Clustering ⌨️ (3:23:46) Principal Component Analysis ⌨️ (3:33:54) K-Means and PCA Implementations 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Learn more about WatsonX: 🤍 Learn about a data and AI platform that’s built for business → 🤍 What is Artificial Intelligence (AI)? → 🤍 What is Machine Learning? → 🤍 What is really the difference between Artificial intelligence (AI) and machine learning (ML)? Are they actually the same thing? In this video, Jeff Crume explains the differences and relationship between AI & ML, as well as how related topics like Deep Learning (DL) and other types and properties of each. Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #ai #ml #dl #artificialintelligence #machinelearning #deeplearning #watsonx
Learn about WatsonX: 🤍 What is Machine Learning → 🤍 What is Deep Learning → 🤍 Get a unique perspective on what the difference is between Machine Learning and Deep Learning - explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen. Download a free AI ebook → 🤍 Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #AI #Software #ITModernization #DeepLearning #MachineLearning
In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources. All courses: 🤍 Get your Free Token for AssemblyAI Speech-To-Text API 👇 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning 0:00 Introduction 1:01 MATH 1:58 PYTHON PYTHON 2:37 ML TECH STACK ML TECH STACK 3:35 ML COURSES ML COURSES 4:44 HANDS-ON & DATA PREPARATION 5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO 6:16 SPECIALIZE & CREATE BLOG
WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert. Still haven’t subscribed to WIRED on YouTube? ►► 🤍 Listen to the Get WIRED podcast ►► 🤍 Want more WIRED? Get the magazine ►► 🤍 Get more incredible stories on science and tech with our daily newsletter: 🤍 Also, check out the free WIRED channel on Roku, Apple TV, Amazon Fire TV, and Android TV. Here you can find your favorite WIRED shows and new episodes of our latest hit series Tradecraft. ABOUT WIRED WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture. Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners. 🔗 Learning resources: 🤍 💻 Code: 🤍 ✏️ Course developed by Ayush Singh. Check out his channel: 🤍 ⭐️ 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: 🤍 Read hundreds of articles on programming: 🤍
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Learn about a next-generation enterprise studio for AI builderss → 🤍 Learn more about Machine Learning → 🤍 Learn more about Deep Learning → 🤍 Learn more about Supervised Learning → 🤍 What is Machine Learning and how do businesses leverage it today? How does Machine Learning differ from Artificial Intelligence (AI) and Deep Learning, or are they all the same? In this lightboard video, Luv Aggarwal with IBM Cloud, answers these questions and many more as he visually explains what Machine Learning is, how it compares to AI and Deep Learning, as well as why and how an enterprise would use a Machine Learning solution. Chapters 0:00 - Intro 0:17 - Differences between Machine Learning, AI, and Deep Learning 1:32 - Supervised Learning 4:26 - Unsupervised Learning 6:38 - Reinforcement Learning 7:44 - Summary Get started on IBM Cloud at no cost → 🤍 Subscribe to see more videos like this in the future → 🤍 #MachineLearning #AI #DeepLearning #watsonx
(Links on this page my give me a small commission from purchases made - thank you for the support!) Introduction to Statistical Learning: 🤍 Roadmap to Become a Data Scientist / Machine Learning Engineer in 2022: 🤍 Roadmap to Become a Data Analyst in 2022: 🤍 Roadmap to Become a Data Engineer in 2022: 🤍 Here's my favourite resources: Best Courses for Analytics: - + IBM Data Science (Python): 🤍 + Google Analytics (R): 🤍 + SQL Basics: 🤍 Best Courses for Programming: - + Data Science in R: 🤍 + Python for Everybody: 🤍 + Data Structures & Algorithms: 🤍 Best Courses for Machine Learning: - + Math Prerequisites: 🤍 + Machine Learning: 🤍 + Deep Learning: 🤍 + ML Ops: 🤍 Best Courses for Statistics: - + Introduction to Statistics: 🤍 + Statistics with Python: 🤍 + Statistics with R: 🤍 Best Courses for Big Data: - + Google Cloud Data Engineering: 🤍 + AWS Data Science: 🤍 + Big Data Specialization: 🤍 More Courses: - + Tableau: 🤍 + Excel: 🤍 + Computer Vision: 🤍 + Natural Language Processing: 🤍 + IBM Dev Ops: 🤍 + IBM Full Stack Cloud: 🤍 + Object Oriented Programming (Java): 🤍 + TensorFlow Advanced Techniques: 🤍 + TensorFlow Data and Deployment: 🤍 + Generative Adversarial Networks / GANs (PyTorch): 🤍 Become a Member of the Channel! 🤍 Follow me on LinkedIn! 🤍 #machinelearning #datascience #statistics #datascience
Machine Learning Specialization from Coursera 👉🏼 🤍 Books for Data Nerds 👇🏼 📕 Machine Learning (Python) 👉🏼 🤍 📘 Machine Learning (Concepts) 👉🏼 🤍 📗 Data Science Must Read 👉🏼 🤍 📚 Books I’ve read 👉🏼 🤍 Certificates & Courses Coursera Courses: 📜 Google Data Analytics Certificate (START HERE) 👉🏼 🤍 💿 SQL for Data Science 👉🏼 🤍 🧾 Excel Skills for Business 👉🏼 🤍 🐍 Python for Everybody 👉🏼 🤍 📊 Data Visualization with Tableau 👉🏼 🤍 🏴☠️ Data Science: Foundations using R 👉🏼 🤍 Coursera Plus Subscription (7-day free trial) 👉🏼 🤍 👨🏼🏫 All courses 👉🏼 🤍 Tech for Data Nerds ⚙️ Tech I use 👉🏼 🤍 🪟Windows on a Mac (Parallels VM) 👉🏼 🤍 👨🏼💻 M1 Macbook Air (Mac of choice) 👉🏼 🤍 💻 Dell XPS 13 (PC of choice) 👉🏼 🤍 💻 Asus Vivo Book (Lowest Cost PC) 👉🏼 🤍 💻Lenovo IdeaPad (Best Value PC)👉🏼 🤍 Build a Portfolio Online 👩🏻💻Build portfolio here 👉🏼 🤍 Rebate Code: "LUKE" My Portfolio 👉🏼 🤍 Social Media / Contact Me 🙋🏼♂️Newsletter: 🤍 🌄 Instagram: 🤍 ⏰ TikTok: 🤍 📘 Facebook: 🤍 📥 Business Inquiries: luke🤍lukebarousse.com As a member of the Amazon, Coursera, Hostinger, Parallels, Interview Query, and Data Camp Affiliate Programs, I earn a commission from qualifying purchases on the links above. It costs you nothing but helps me with content creation. #datanerd #dataanalyst #datascience
What are the neurons, why are there layers, and what is the math underlying it? Help fund future projects: 🤍 Written/interactive form of this series: 🤍 Additional funding for this project provided by Amplify Partners Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: 🤍 There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! 🤍 I also highly recommend Chris Olah's blog: 🤍 For more videos, Welch Labs also has some great series on machine learning: 🤍 🤍 For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: 🤍 Lion photo by Kevin Pluck - Timeline: 0:00 - Introduction example 1:07 - Series preview 2:42 - What are neurons? 3:35 - Introducing layers 5:31 - Why layers? 8:38 - Edge detection example 11:34 - Counting weights and biases 12:30 - How learning relates 13:26 - Notation and linear algebra 15:17 - Recap 16:27 - Some final words 17:03 - ReLU vs Sigmoid Correction 14:45 - The final index on the bias vector should be "k" Animations largely made using manim, a scrappy open source python library. 🤍 If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: 🤍 Stream the music on Spotify: 🤍 If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍
Confused about understanding machine learning models? Well, this video will help you grab the basics of each one of them. From what they are, to why they are used, and what purpose do they serve. All Major Software Architecture Patterns Explained in 7 Minutes | Meaning, Design, Models & Examples 🤍 7 Basic Machine Learning Concepts for Beginners 🤍 What is Deep Learning and How it Works | Deep Learning Explained 🤍 Machine Learning Model Deployment Explained 🤍 What is Neural Network and How it Works | Neural Network Explained 🤍 What is Data Science Project Life Cycle Explained Step by Step 🤍 After watching this video, you'll be able to answer, - How many machine learning models are there - Some common machine learning models explained - What is supervised learning - What is unsupervised learning - What is regression - Types of ml models - Common types of regression - Common types of classification - What is classification - What are popular ML models explained - What are the types of supervised learning - What are the types of unsupervised learning - Understanding the basics of machine learning models - Learn machine learning models from scratch - What are common machine learning models for beginners - Understand machine learning models overview - Whats are few ml models basics to grasp Obviously, there is a ton of complexity if you dive into any particular model, but this should give you a fundamental understanding of how each machine learning model works! Like my content? Be sure to smash that like button and hit Subscribe to get the latest updates! Let's get social! 🤍 🤍 🤍 #WhiteboardProgramming #MachineLearning #MLmodels
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit 🤍 for our text-based lesson. This video includes information on: • How machine learning works • How machine learning is used • The future of machine learning We hope you enjoy!
How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning. The 7 Steps of Machine Learning article: 🤍 Learn more through our hands-on labs → 🤍 Watch more episodes of AI Adventures here: 🤍 TensorFlow Playground: 🤍 Machine Learning Workflow: 🤍 Hands-on intro level lab Baseline: Data, ML, AI → 🤍 Qwiklabs: 🤍 Want more machine learning? Subscribe to the channel: 🤍 #AIAdventures
I made an A.I. that teaches itself to drive in the racing game Trackmania, using Machine-Learning. I used Deep-Q-Learning, a Reinforcement Learning algorithm. Again, a big thanks to Donadigo for TMInterface ! Contact : Discord – Yosh#5919 Twitter – 🤍
So how do you get started with machine learning and AI? What should you learn first? Well in this video I will be discussing the exact things you need to learn to get started with machine learning. I'll be talking about which language to learn, how much math you need and what ML algorithms to learn first. ⭐️ Thanks to Kite for sponsoring this video! Download the best AI automcolplete for python programming for free: 🤍 ◾◾◾◾◾ 💻 Enroll in The Fundamentals of Programming w/ Python 🤍 📸 Instagram: 🤍 🌎 Website 🤍 📱 Twitter: 🤍 ⭐ Discord: 🤍 📝 LinkedIn: 🤍 📂 GitHub: 🤍 🔊 Podcast: 🤍 💵 One-Time Donations: 🤍 💰 Patreon: 🤍 ◾◾◾◾◾◾ ⚡ Please leave a LIKE and SUBSCRIBE for more content! ⚡ Tags: - Tech With Tim - Get started with Machine Learning - Machine learning getting started - Get started with AI #AI #MachineLearning
Wat is machinaal leren? Geef ons 90 seconden en je komt het te weten met Marian Verelst (KU Leuven) en WetenSNAP! Een klassiek computerprogramma voert exact uit wat de programmeur er zelf in heeft gezet. Niet meer, niet minder. Bijvoorbeeld: ‘Robotje, loop naar de overkant van dit parcours’. Maar er zijn ook computers die zichzelf iets kunnen aanleren en later ook bijleren, net zoals mensen/wij. In het begin kunnen die computers niets, maar ze zijn geprogrammeerd om te leren uit data en na een tijdje oefenen kunnen ze nieuwe dingen. Dat noemen we machinaal leren, of in het Engels, Machine Learning. Hoe leert een machine? Als ik jou deze foto laat zien, of deze, of deze, dan weet jij ‘dat is een kat’. Een computer weet dat niet. Je zou een computerprogramma kunnen schrijven met regels die volledig beschrijven wanneer een dier een kat is. Bv. Als het puntige oren heeft, is het een kat. Maar… andere dieren hebben soms ook puntige oren. Je zou dus duizenden regels moeten schrijven om zo’n programma goed te laten werken. OF… we laten de computer zelf leren. We voeden de computer met miljoenen foto’s van katten. En zeggen daarbij: ‘Dit zijn katten’. De computer analyseert elke foto en ziet patronen/gelijkenissen tussen de foto’s. Hoe meer voorbeelden van katten een computer krijgt, hoe beter hij zal weten wat een kat is en wat niet. Machine learning is overal aanwezig. Het zit in de gezichtsherkenning op je GSM of de keuze advertenties die je te zien krijgt op sociale media. ✺ Meer wetenschap? Check: Onze website! ► 🤍siteitvanvlaanderen... Twitter ► 🤍 Facebook ► 🤍 Instagram ► 🤍 ✺ Voor niet-commercieel gebruik is het toegestaan om fragmenten (mits context behouden) te gebruiken. Bij twijfel, mail ons op info🤍universiteitvanvlaanderen.be. ✺ Wil je reageren op onze video’s? Fijn, we horen graag van je! We willen je er wel even op wijzen dat... ✦ schelden niet is toegestaan, net als kwetsende, discriminerende, seksistische of racistische uitspraken, ✦ links naar andere pagina’s, kanalen of websites niet zijn toegestaan, tenzij naar wetenschappelijke artikels of nieuwsberichten, ✦ feitelijke onwaarheden of onduidelijke berichten niet kunnen, ✦ een inhoudelijk debat zeker kan, maar persoonlijke opmerkingen over de spreker niet kunnen. Onze wetenschappers doen vrijwillig mee aan onze video’s, laten we het leuk houden voor hen.
Create your own machine learning model RIGHT NOW: 💥💥START HERE: Create a FREE Oracle Cloud account: 🤍 🧪LAB 1 - Data Extraction: 🤍 🧪LAB 2 - Model Building with scikit-learn and AutoGluon: 🤍 🧪LAB 3: Build a Neural Network: 🤍 CHECKOUT ORACLE - -Developer.Oracle.Com: 🤍 -Join the Oracle Slack community: 🤍 -another fun Hand-on-Lab: Oracle RedBull Pit Strategy 🤍 Anyone can learn machine learning. You don’t need a fancy college degree nor do you need to be a math genius. No matter where you are in your journey, you can start learning machine learning and Artificial Intelligence. In this video, NetworkChuck will show you how you can get started with Machine Learning, an insanely lucrative path. So, don’t wait, start learning the technologies behind things like ChatGPT and OpenAI. Learn more about Santiago (a REAL ML Engineer) - YouTube: 🤍 Twitter: 🤍 Website: 🤍 🔥🔥Join the NetworkChuck Academy!: 🤍 Sponsored by Oracle Cloud ALTERNATE TITLES: - The Future is Here: How AI is Revolutionizing Our Lives - The Power of AI: How Machine Learning is Changing the Game - AI: The Next Frontier in Technology - The Rise of AI: How Artificial Intelligence is Changing the World - The AI Revolution: How Machine Learning is Transforming Industries - AI for Beginners: A Guide to Understanding Artificial Intelligence - AI 101: Everything You Need to Know About Artificial Intelligence - Demystifying AI: Understanding the Basics of Machine Learning - AI Made Easy: A Beginner's Guide to Artificial Intelligence - Unlocking the Power of AI: A Comprehensive Guide to Machine Learning - i taught my computer to tell me I suck at video games - i taught my computer to play video games - i used AI to win League of Legends - chatgpt thinks I suck at video games - i used machine learning to suck less at video games - i taught my computer LoL (machine learning) - my computer predicted i suck at video games (machine learning) - you need to learn Machine Learning RIGHT NOW!! - machine learning is easy - even idiots can learn machine learning - even dumb dumb stupids can learn machine learning - i learned machine learning in 24 hours - machine learning isn’t that hard (7 Steps) - machine learning isn’t that hard (even I can do it) - learning AI isn’t that hard - you to need to learn AI RIGHT NOW!! - learning AI isn’t that hard - - AI is Easier Than You Think - Don't Fear AI: It's Easier Than You Think - AI: It's Easier Than You Realize - Take the Leap: Learning AI Isn't Hard - Easily Learn AI - You Can Do It! - Learning AI: Start Small and Achieve Big Results - AI: It's Easier than You Think - Get Started Today - AI: Learn It and Conquer It - Don't Panic: AI Is Easier to Learn Than You Think - AI: Uncover Its Secrets and Conquer Its Challenges SUPPORT NETWORKCHUCK - ➡️NetworkChuck membership: 🤍 ☕☕ COFFEE and MERCH: 🤍 Check out my new channel: 🤍 🆘🆘NEED HELP?? Join the Discord Server: 🤍 STUDY WITH ME on Twitch: 🤍 READY TO LEARN?? - -Learn Python: 🤍 -Get your CCNA: 🤍 FOLLOW ME EVERYWHERE - Instagram: 🤍 Twitter: 🤍 Facebook: 🤍 Join the Discord server: 🤍 AFFILIATES & REFERRALS - (GEAR I USE...STUFF I RECOMMEND) My network gear: 🤍 Amazon Affiliate Store: 🤍 Buy a Raspberry Pi: 🤍 Do you want to know how I draw on the screen?? Go to 🤍 and use code NetworkChuck to get 20% off!! fast and reliable unifi in the cloud: 🤍 #machinelearning #chatgpt #artificialintelligence
Terms Deep Learning and Machine Learning are sometimes used interchangeably. But they are far from being identical. The key difference is how they are trained! ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning #ArtificialIntelligence #Shorts
🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 : 🤍 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners and professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Roadmap course: 00:00:00 Introduction 00:01:08 Agenda 00:02:45 What is Machine learning? 00:06:28 Supervised Machine Learning 00:11:49 Un-Supervised Machine Learning 00:16:03 Reinforcement Machine Learning 00:32:21 How to Become a Machine Learning Engineer? 00:41:53 Machine Learning Algorithm 01:03:46 Linear Regression Algorithm 01:06:40 What is Linear Regression 01:11:13 Linear Regression Use Cases 01:12:24 Use Case- How to Implement Linear Regression using Python 01:30:22 Logistic Regression Algorithm 01:35:44 Logistic Regression Use cases 02:17:36 Linear Regression Vs Logistic Regression 02:21:05 Decision Tree Algorithm 02:25:53 Types of Classification 02:34:57 What is Decision Tree? 02:58:25 What is Pruning? 02:58:36 Hands-on 03:06:42 Random Forest 03:10:46 Working of Random Forest 03:17:45 Splitting Methods 03:20:32 Advantages & Disadvantages of Random Forest 03:23:52 Hands-on Random Forest 03:35:18 KNN Algorithm 03:37:39 Features of the KNN Algorithm 03:45:54 How KNN works 03:51:21 Hands-on KNN Algorithm 04:07:45 Naive Bayes Classifier 04:29:25 Support Vector Machine 04:31:13 How do SVM work 04:55:00 K- Means Clustering Algorithm 04:58:26 K Means Clustering 05:07:16 Agglomerative Clustering 05:09:16 Division Clustering 05:09:41 Mean shift Clustering 05:18:21 Hierarchical Clustering 05:25:10 How Agglomerative Clustering Works 05:32:59 Applications of Hierarchical Clustering 05:38:34 Apriori Algorithm Explained 05:52:58 Demo 06:30:26 Linear Algebra Application 06:54:00 Probability 07:07:01 Statistics 07:12:47 Types of Statistics 07:38:40 How to select the correct predictive modeling techniques 07:50:54 ML Model Deployment with Flask on Heroku 08:28:32 Azure Machine Learning 08:54:49 AWS Machine Learning 09:35:24 Machine learning Engineer Skills 09:43:30 Machine Learning Engineer Job Trend, Salary & Resume 09:59:20 Top Machine Learning Tools & Frameworks 10:09:12 ML Roadmap 10:22:20 ML Interview Question & Answers 🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 DevOps Online Training: 🤍 🌕 AWS Online Training: 🤍 🔵 React Online Training: 🤍 🌕 Tableau Online Training: 🤍 🔵 Power BI Online Training: 🤍 🌕 Selenium Online Training: 🤍 🔵 PMP Online Training: 🤍 🌕 Salesforce Online Training: 🤍 🔵 Cybersecurity Online Training: 🤍 🌕 Java Online Training: 🤍 🔵 Big Data Online Training: 🤍 🌕 RPA Online Training: 🤍 🔵 Python Online Training: 🤍 🌕 Azure Online Training: 🤍 🔵 GCP Online Training: 🤍 🌕 Microservices Online Training: 🤍 🔵 Data Science Online Training: 🤍 🌕 CEHv12 Online Training: 🤍 🔵 Angular Online Training: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: 🤍 🌕 Cloud Architect Masters Program: 🤍 🔵 Data Scientist Masters Program: 🤍 🌕 Big Data Architect Masters Program: 🤍 🔵 Machine Learning Engineer Masters Program: 🤍 🌕 Business Intelligence Masters Program: 🤍 🔵 Python Developer Masters Program: 🤍 🌕 RPA Developer Masters Program: 🤍 🔵 Web Development Masters Program: 🤍 🌕 Computer Science Bootcamp Program : 🤍 🔵 Cyber Security Masters Program: 🤍 🌕 Full Stack Developer Masters Program : 🤍 🔵 Automation Testing Engineer Masters Program : 🤍 🌕 Python Developer Masters Program : 🤍 🔵 Azure Cloud Engineer Masters Program: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🌕 Professional Certificate Program in DevOps with Purdue University: 🤍 🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: 🤍 🌕 Artificial and Machine Learning PGD with E&ICT Academy NIT Warangal: 🤍 Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): 🤍 This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video: 00:00 Introduction 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hierarchical Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions -Edureka Machine Learning Training 🔵 Machine Learning Course using Python: 🤍 🔵 Machine Learning Engineer Masters Program: 🤍 🔵Python Masters Program: 🤍 🔵 Python Programming Training: 🤍 🔵 Data Scientist Masters Program: 🤍 PG Diploma in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 🔴 Subscribe to our channel to get latest video updates: 🤍 ⏩ NEW Top 10 Technologies To Learn In 2023 - 🤍 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: 🤍 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: 🤍 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: 🤍 📌𝐌𝐞𝐞𝐭𝐮𝐩: 🤍 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: 🤍 For more information, please write back to us at sales🤍edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Learn more about Transformers → 🤍 Learn more about AI → 🤍 Check out IBM Watson → 🤍 Transformers? In this case, we're talking about a machine learning model, and in this video Martin Keen explains what transformers are, what they're good for, and maybe ... what they're not so good at for. Download a free AI ebook → 🤍 Read about the Journey to AI → 🤍 Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #AI #Software #ITModernization
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): 🤍 🔥Professional Certificate Program In AI And Machine Learning: 🤍 In this video on Machine Learning with Python full course, you will understand the basics of machine learning and Python. In this Machine Learning tutorial for beginners, we will cover essential machine learning topics like applications of machine learning and machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, and PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap. Below are the topics covered in this video: 00:00:00 Machine Learning With Python Full Course 2023 00:08:36 Introduction to Machine Learning 00:16:14 Top 10 Applications of Machine Learning 00:32:38 Types of Machine Learning 00:37:46 Machine Learning Algorithms 00:38:14 Linear Regression 00:46:52 Decision Tree 01:23:25 Clustering 01:26:11 K-Means Clustering 02:18:03 Data and its types 03:29:22 Probability 04:07:53 Multiple Linear Regression 04:45:55 Confusion Matrices 05:59:54 KNN 06:23:40 Support Vector Machine 07:14:40 Principle Component Analysis(PCA) 07:53:01 Corona Virus Analysis 🔥Free Machine Learning Course With Completion Certificate: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #MachineLearningCourse #MachineLearningFullCourse #MachineLearningWithPython #MachineLearningWithPythonFullCourse #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #MachineLearningTraining #Simplilearn Dataset Link -🤍 ✅ AI ML Course Overview This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision -GANs - Keras - Tensorflow - Reinforcement Learning - Speech Recognition - Recommendation Systems - Ensemble Learning - NumPy 👉Learn more at: 🤍 Get the Simplilearn app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
All the materials are available in the below link 🤍 Visit 🤍 for data sscience blogs 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
Machine learning is awesome. Who doesn't want to build a cool AI that you can teach to do anything. The only problem is machine learning is very confusing. In this video I breakdown what a neural network is, how you can create one, and how to train it. By the end of this video you will have a fully functional AI. 📚 Materials/References: GitHub Code: 🤍 Brain.js Library: 🤍 🧠 Concepts Covered: - How to use brain.js - What a neural network is - How neural networks work - How to train and use a neural network 🌎 Find Me Here: My Blog: 🤍 My Courses: 🤍 Patreon: 🤍 Twitter: 🤍 Discord: 🤍 GitHub: 🤍 CodePen: 🤍 #MachineLearning #WDS #JavaScript
📚 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 👉 🤍 📚 Probabilistic Machine Learning: An Introduction 👉 🤍 📚 The Master Algorithm 👉 🤍 👩🏻💻 COURSES & RESOURCES 📖 Learn SQL Basics for Data Science Specialization 👉 🤍 📖 Excel Skills for Business 👉 🤍 📖 Machine Learning Specialization 👉 🤍 📖 Data Visualization with Tableau Specialization 👉🤍 📖 Deep Learning Specialization 👉 🤍 📖 Mathematics for Machine Learning and Data Science Specialization 👉 🤍 📖 Google Data Analytics Certificate 👉 🤍 📖 Applied Data Science with Python 👉 🤍 🙋🏻♀️ LET'S CONNECT! 🤓 Join my Discord server: 🤍 📩 Newsletter: 🤍 ✍ Medium: 🤍 🔗 All links: 🤍 As a member of the Amazon and Coursera Affiliate Programs, I earn a commission from qualifying purchases on the links above. By using the links you help support this channel at no cost for you. #shorts
This video we walk through a roadmap of how to get started in machine learning and AI. It can seem like a lot at first, but in this video Rob Mulla, kaggle grandmaster, breaks down his suggestions for anyone looking to start in this field. This is a good entry into anyone looking to start a career in data science or machine learning. We break it down into a few steps. This will help you map out your path to becoming a machine learning master including: which programming language to use, what courses to take, and the rest. Timeline: 00:00 Starting your journey 00:46 Picking a programming language 02:00 Math & Statistics 03:10 Data Wrangling 03:44 Learn Algorithms 04:45 Picking a Focus 06:50 Tools 08:06 Learn through Doing (Kaggle) Follow me on twitch for live coding streams: 🤍 My other videos: Speed Up Your Pandas Code: 🤍 Speed up Pandas Code: 🤍 Intro to Pandas video: 🤍 Exploratory Data Analysis Video: 🤍 Working with Audio data in Python: 🤍 Efficient Pandas Dataframes: 🤍 * Youtube: 🤍 * Discord: 🤍 * Twitch: 🤍 * Twitter: 🤍 * Kaggle: 🤍 #machinelearning #datascience #python
We'll learn how to build an end-to-end machine learning project. We'll cover the main steps in building a machine learning project, then walk you through writing the Python code to create the project. In the project, we'll try to predict how many medals each country will win in the olympics using a linear regression model. At the end, you'll have a full machine learning project that you can continue working on. You can find the README and code here - 🤍 . Chapters 00:00 Introduction 00:40 7-step project process 10:15 Loading the data 12:10 Data exploration 18:05 Building our model 22:30 Measuring error 26:30 Is the model good? 34:20 Wrap-up and next steps
hello, in this video I share how I prepared for google's machine learning software engineer interview and the resources I found helpful :) links to resources mentioned: 1. cracking the coding interview: 🤍 2. grokking the coding interview: 🤍 3. leetcode: 🤍 4. pramp: 🤍 5. cs229 cheatsheet: 🤍 6. cs230 cheatsheet: 🤍 7. ml systems design course: 🤍 timestamps 0:00 - intro 0:41 - submitting application 1:34 - interview focus areas 1:44 - data structures prep 2:13 - algorithms prep 2:48 - practising coding problems 4:50 - mock interviews 5:35 - machine learning knowledge prep 6:55 - nlp prep 7:33 - ml systems design prep 8:07 - behavioral prep 9:31 - outro #googleinterview #pramp #machinglearningsystemdesign
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): 🤍 🔥Professional Certificate Program In AI And Machine Learning: 🤍 This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of 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 help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression, K Means Clustering, Decision Tree, and Support Vector Machines are some of the important algorithms you will understand with a hands-on demo. Dataset Link - 🤍 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 Below topics are explained in this Machine Learning course for beginners: 0:00 Table of contents 01:46 Basics of Machine Learning 09:18 Why Machine Learning 13:25 What is Machine Learning 18:32 Types of Machine Learning 18:44 Supervised Learning 21:06 Reinforcement Learning 22:26 Supervised VS Unsupervised 23:38 Linear Regression 25:08 Introduction to Machine Learning 26:40 Application of Linear Regression 27:19 Understanding Linear Regression 28:00 Regression Equation 35:57 Multiple Linear Regression 55:45 Logistic Regression 56:04 What is Logistic Regression 59:35 What is Linear Regression 01:05:28 Comparing Linear & Logistic Regression 01:26:20 What is K-Means Clustering 01:38:00 How does K-Means Clustering work 02:15:15 What is Decision Tree 02:25:15 How does Decision Tree work 02:39:56 Random Forest Tutorial 02:41:52 Why Random Forest 02:43:21 What is Random Forest 02:52:02 How does Decision Tree work- 03:22:02 K-Nearest Neighbors Algorithm Tutorial 03:24:11 Why KNN 03:24:24 What is KNN 03:25:38 How do we choose 'K' 03:27:37 When do we use KNN 03:48:31 Applications of Support Vector Machine 03:48:55 Why Support Vector Machine 03:50:34 What Support Vector Machine 03:54:54 Advantages of Support Vector Machine 04:13:06 What is Naive Bayes 04:17:45 Where is Naive Bayes used 04:54:48 Top 10 Application of Machine Learning 04:59:46 How to become a Machine Learning Engineer 05:09:03 Machine Learning Interview Questions 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- 🤍 Watch more videos on Machine Learning: 🤍 #MachineLearning #CompleteMachineLearningCourse #MachineLearningForBeginners #MachineLearningTutorial #MachineLearningWithPython #LearnMachineLearning #MachineLearingBasics #MachineLearningAlgorithms #MachineLearningEngineer #MachineLearningEngineerSalary #MachineLearningEngineerSkills #SimplilearnMachineLearning #MachineLearningCourse We've partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here - 🤍 About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. Learn more at: 🤍 For more updates on courses and tips follow us on: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
Got lots of data? Machine learning can help! In this episode of Cloud AI Adventures, Yufeng Guo explains machine learning from the ground up, using concrete examples. Learn more through our hands-on labs → 🤍 Associated article "What is Machine Learning?" → 🤍 Qwiklabs → 🤍 Watch more episodes of AI Adventures here → 🤍 TensorFlow → 🤍 Cloud ML Engine → 🤍 Hands-on intro level lab Baseline: Data, ML, AI → 🤍 Don't forget to subscribe to the channel! → 🤍 #AIAdventures
AI Teaches Itself to Walk! In this video an AI named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time. Check the pinned comment for more information on how the AI was trained! Current Subscribers: 135,027
Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C, you build a system which is trained on data to infer the rules itself. But what does ML actually look like? In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney🤍) walks through a basic Hello World example of building an ML model, introducing ideas which we'll apply in later episodes to a more interesting problem: computer vision. Try this code out for yourself in the Hello World of Machine Learning → 🤍 This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → 🤍 Subscribe to the TensorFlow channel → 🤍
Learn about the differences between deep learning and machine learning in this MATLAB® Tech Talk. We walk through several examples and learn how to decide which method to use. Related Resources: - MATLAB for Deep Learning: 🤍 - Learn more about Deep Learning: 🤍 - Download a trial: 🤍 The video outlines the specific workflow for solving a machine learning problem. The video also outlines the differing requirements for machine learning and deep learning. You’ll learn about the key questions to ask before deciding between machine learning and deep learning. The choice between machine learning or deep learning depends on your data and the problem you’re trying to solve. MATLAB can help you with both of these techniques – either separately or as a combined approach.
What is Machine Learning and how does it work: Many people who hear about machine learning often think of it as magic. In this video, I will explain to you what ML actually is and why it is more of a computation strategy than some out-of-the-box magic! ►ML Roadmap video: 🤍 ►Check out my English channel here: 🤍 ►Click here to subscribe - 🤍 Best Hindi Videos For Learning Programming: ►Learn Python In One Video - 🤍 ►Python Complete Course In Hindi - 🤍 ►C Language Complete Course In Hindi - 🤍 ►JavaScript Complete Course In Hindi - 🤍 ►Learn JavaScript in One Video - 🤍 ►Learn PHP In One Video - 🤍 ►Django Complete Course In Hindi - 🤍 ►Machine Learning Using Python - 🤍 ►Creating & Hosting A Website (Tech Blog) Using Python - 🤍 ►Advanced Python Tutorials - 🤍 ►Object Oriented Programming In Python - 🤍 ►Python Data Science and Big Data Tutorials - 🤍 Follow Me On Social Media ►Website (created using Flask) - 🤍 ►Facebook - 🤍 ►Instagram - 🤍 ►Personal Facebook A/c - 🤍 Twitter - 🤍 Comment "#HarryBhai" if you read this 😉😉
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: 🤍 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍
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