ML explained

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All Machine Learning Models Explained in 5 Minutes | Types of ML Models Basics


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. 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 🤍 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! Read the full blog on 🤍 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

Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn


🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 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 ) 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 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. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning. The Machine Learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022 at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach, including working on 28 projects and one capstone project. 3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering, and more. 5. Be able to model a wide variety of robust Machine Learning algorithms, including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientists or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning 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: 🤍

Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED


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

AI and ML explained... in under 100 words


AI here, ML there – in recent times the terms Artificial Intelligence (AI) and Machine Learning (ML) have become very popular. They play a prominent role in most conversations about the future of technology, business, the workplace and even humanity itself. But could you explain what those terms really mean?

Machine Learning Explained in 10 Minutes | ML Explained


Get a look at our course on data science and AI here : 👉 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video, we are going to explain to you the basics of machine learning in under 10 minutes. 📌 Watch the explainer playlist here 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ AI SCIENCES provides Free tutorials and videos in Data Science, Machine Learning, and AI for beginners like you! Follow AI Sciences! AI Sciences' Website 👉 🤍 AI Sciences' Facebook Page 👉 🤍 AI Sciences' LinkedIn Page 👉 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 👨👩👧👦 JOIN THE LARGEST DATA SCIENCE and AI FAMILY! Udemy Courses ► 🤍 Paid Books ► ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 👋 About AI Sciences: AI Sciences is an e-learning company; the company publishes online courses and books about data science and computer technology for anyone, anywhere. We are a group of experts, Ph.D. students, and young practitioners of artificial intelligence, computer science, machine learning, and statistics. Some of us work for big-name companies like Google, Facebook, Microsoft, KPMG, BCG, and Mazars. We decided to produce courses and books mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory or lengthy reading. Today, we also publish more complete books on selected topics for a wider audience. Consider subscribing to new videos regularly showing you can be a data engineer, a data scientist, to learn Statistics, or to be a data analyst from scratch.

Machine Learning Algorithms In-Depth Guide For 2022 | ML Algorithms Explained | Simplilearn


This video on Machine Learning Algorithm will take you through a detailed concept of machine learning algorithm. This video will help you to understand What is an Algorithm, What is Machine Learning, Types of Machine Learning, How Algorithms works in Machine Learning/Programing, Some popular Machine Learning Algorthms - Linear Regression Algorithm,Logistic Regression Algorithm, Decision Tree, SVM (Support Vector Machine), KNN (K Nearest Neighbor), K-Means Clustering, Random Forest, Apriori Algorithm, and hands-on-lab Demo- Linear Regression 01:23 What is an algorithm 02:18 What is machine learning 02:55 Types of machine learning 04:28 How do algorithms work in ML 07:03 Popular machine learning algorithms 29:39 Hands-on lab demo ✅ What is an Algorithm? An algorithm is a set of well-defined instructions to solve a particular problem. It takes a bunch of information sources and delivers the ideal result. ✅ What is machine learning? Machine learning is a sub-part of artificial intelligence, the art of making computers learn and act like humans by feeding data and making predictions based on experience. ✅ Types of Machine Learning 1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement learning ✅ Some popular machine learning algorithms - Linear Regression Algorithm - Logistic Regression Algorithm - Decision Tree - SVM (Support Vector Machine) - KNN (K Nearest Neighbor) - K-Means Clustering - Random Forest - Apriori ✅ Linear Regression : Linear regression is one of the most famous and straightforward machine learning algorithms utilized for predictive analysis. Linear regression shows the linear connection between the dependent and independent factors. ✅ Logistic Linear Regression: Logistic regression is the supervised machine learning algorithm utilized to anticipate all categorical factors or discrete values. It could very well be used for the grouping issues in machine learning, and the result of the logistic regression can be either yes or no, 0 or 1, Men or Women, and so on. it gives the values which lie between 0 and 1. ✅ Decision Trees A decision tree is a tree-structured classifier that could be used for both classification and regression. A decision tree is a tree in which each non-leaf node is assigned an attribute. Additionally, each arc contains one of the available values for its parent node's (categorical) property, which is associated with each leaf node (i.e., the node from where the arc is directed). ✅ SVM - Support Vector Machine A support vector machine is a well-known supervised machine learning model. It is utilized for both information classification and regression. It is regularly utilized for grouping issues. We can involve it in different life-care systems ✅ KNN (K Nearest Neighbor) KNN is a Supervised Learning Technique. KNN Classifies new data into a targeted class, depending on the features of its neighboring points, and also could be used for regression problems. It is an instance-based learning algorithm and a bit lazy learning algorithm. ✅ K-Means Clustering K-Means is a cluster falls underthat is an unsupervised learning algorithm. It is used to address ML clustering problems and utilized to tackle the grouping issues in ML. ✅ Random Forest Random forest is an adaptable, simple-to-utilize ML algorithm that produces, even without hyper-boundary tuning, an extraordinary outcome more often than not. It is likewise quite possibly the most-utilized algorithm, because of its effortlessness and variety (it tends to be utilized for both grouping and classification tasks). ✅ Apriori Algorithm The Apriori algorithm utilizes standard item sets to create affiliation rules and is intended to chip away at the information bases containing exchanges. With the assistance of these affiliation rules, it decides how firmly or feebly two objects are associated. 🔥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: 🤍 #MachineLearningAlgorithms #MLAlgorithms #MachineLearning #ML #MLBasics #MachineLearningTutorial #MLTutorial #SimplilearnMachineLearning #MachineLearningPython #Simplilearn 👉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: 🤍

AI and ML explained


In this short video, Neal Analytic's data scientists Clarissa & Elise, explain what Artificial Intelligence and Machine Learning are and how they can be used to help business. Dylan Dias, Neal Analytic's CEO, shares the company vision where AI is going and how it can impact the future of augmented work.

Difference Between ML Algorithm and ML Model | ML Algorithm vs Model


ML Algorithm vs ML Model: Well, for a beginner, the words “algorithm" & "model” in machine learning creates a lot of confusion. Yes, there is a difference between machine learning algorithm and model. This video will help you understand each with a deeper insight and crucially understand which one to use as a term when. After watching this video, you'll be able to answer, - What is an Algorithm in Machine Learning - What is a Model in Machine Learning - Algorithm vs. Model in ML - Difference between Machine Learning Algorithm and Model Recommended Videos, All Machine Learning Models Explained in 5 Minutes Watch: 🤍 7 Basic Machine Learning Concepts for Beginners Watch: 🤍 What does it Mean to Deploy a Model in Machine Learning Watch: 🤍 Like my content? Be sure to smash that like button and hit Subscribe to get the latest updates! Let's get social! 🤍 🤍 🤍 #MachineLearning #MLmodels #WhiteboardProgramming #MLalgorithms

The COMPLETE Beginner's Guide - How to Play Mobile Legends!


Hello my friends! Today I will show you how to play Mobile Legends and win many more matches. Enjoy! Reccomended guides for you 💙 All Attack Items Explained: 🤍 All Magic Items Explained: 🤍 Perfect Settings: 🤍 Mistakes you need to avoid: 🤍 Full Trade Guide: 🤍 10 Easy and OP Heroes: 🤍

How to evaluate ML models | Evaluation metrics for machine learning


There are many evaluation metrics to choose from when training a machine learning model. Choosing the correct metric for your problem type and what you’re trying to optimize is critical to the success of the model. In this video, we will learn about the most commonly used evaluation metrics for classification and regression tasks. Get your Free API token for AssemblyAI here 👇 🤍

Machine Learning Model Deployment Explained | All About ML Model Deployment


Imagine that you’ve spent several months creating a machine learning model (or ml model) that performs a task X. It might feel great, but let me tell you, you're not done yet. Ideally, you'll want your model to perform this X task in a reactive approach. And this is where machine learning model deployment comes into play. This video will give you basics of ml model deployment overview and the prerequisites needed to perform an ideal one. We cover various aspects of machine learning model deployment explained with a defined structure. After watching this video, you'll be able to answer, - What does it mean to deploy a machine learning model - What is model deployment in ml - High-Level architecture of an ml system - What are different methods to deploy ml model - Full picture of machine learning model deployment explained - Which factors you should consider when determining your model deployment method - How model deployment in ml works - Overview of ml model deployment explained - What does it mean by machine learning model deployment Recommended: All Machine Learning Models Explained in 5 Minutes Watch: 🤍 7 Basic Machine Learning Concepts for Beginners Watch: 🤍 Like my content? Be sure to smash that like button and hit Subscribe to get the latest updates! Let's get social! 🤍 🤍 🤍 #MachineLearningDeployment #MLmodels #WhiteboardProgramming #MLmodeldeployment #ModelDeployment

#1 Introduction to Machine Learning - Definition & Example |ML|


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HELLO IF YOU LOVE THIS ONE! PLEASE SUBSCRIBE AND LIKE THIS VIDEO 😊 PS: I am Filipino and not fluent in english so bear with me. 😁 Subscribe to my friend!! Accelerator Channel: 🤍 FB PAGE: 🤍 MUSIC THAT I USED: NEFFEX UNKNOWN BRAIN NCS THEFATRAT ENJOY THIS Q&A!  Q: Who’s you main?  A: Fanny, gusion and Chou  Q: Do you have a skin giveaway?  A: When I hit 50K subs!  Q: Can I ask for a shout-out?  A: Yes! Just write in in the comments section! But don’t be rude!  Q: What phone are you using?  A: Im using OPPO F7 for my videos but have a bit lag thats why i want a new phone Tags William'sTube Jr. ZAngelo Chupsy TV Elgin Hororo Chan JC GAMING Snow Pai Edrian Official Free Diamonds Tips to earn diamonds Messenger free diamonds Bug in Mobile Legends Remove lag in Mobile Legends redemption code 2020 Redeem Code update MLBB 2020 How to create new account in Mobile Legends Create Smurf Account ITEMS GUIDE MOBILE LEGENDS Items explained in Mobile Legends #ItemsExplained #MLBB

Master ML Papers without Losing Your Sh*t


Tired of struggling your way through Machine Learning research papers? Latest deep learning models got you feeling down? Ripping out your hair trying to stack layers? Losing the plot over…plotting loss? I hear you. Let’s face it, reading Machine Learning and Deep Learning research papers can be tough. Having a process to systematically break them down makes working through them a whole heap easier. In this video you’ll learn how to do exactly that. Links Mentioned Papers With Code: 🤍 GoodNotes: 🤍 Keras Functional API: 🤍 Chapters 0:00 - Introduction 0:39 - 1. Take a Breath 1:28 - 2. Read the Abstract, Conclusion, Data and Results Section 3:43 - 3. Get the Code 5:29 - 4. Isolate How it Was Built 9:49 - 5. Try it Out Yourself 11:36 - Ending Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!

Introducing convolutional neural networks (ML Zero to Hero - Part 3)


In part three of Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney🤍) discusses convolutional neural networks and why they are so powerful in Computer vision scenarios. A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this video you'll see how they work, by processing an image to see if you can extract features from it! Codelab: Introduction to Convolutions → 🤍 This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → 🤍 Subscribe to the TensorFlow channel → 🤍

Neural Network Simply Explained - ML for Beginners


In this video, we will talk about neural networks and some of their basic components! 🧠🧠🧠 Neural Networks are machine learning algorithms (sets of instructions) that we use to solve problems that traditional computer programs can barely handle! 🤯 For example Face Recognition, Object Detection and Image Classification. We will take a very close look inside a typical classifier neural network and we will follow an input image from start to finish. The concepts we will cover are: NN, labels, computer vision, weights, hidden layers, training, narrow AI. Have fun!!! and please don't forget to share if you find it useful! 💙💛💙 * 🤓 Further Learning 🤓 * How Computers See Images: 🤍 Draw Images with Open CV: 🤍 Supervised Learning and more: 🤍 * ⏳ Timestamps ⏳ * 00:00 - What is a Neural Network 00:55 - How Computers See Images 02:20 - What is a Label 02:44 - Hidden Layers 03:48 - Training 04:10 - Weights 04:42 - Optimization 05:27 - Narrow AI 06:03 - Input Data 06:18 - Thanks for Watching! * IN THE NEXT TUTORIAL: We will learn how to use Pytorch to train neural networks on popular computer vision databases! We will implement with Python all the concepts we have learned in this video and it's gonna be really really fun! I promise!! 😊 All the beautiful icons in this video and thumbnail are by and! 🤩

What is Machine Learning?


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 😉😉

Mobile Legends: Red and Blue Buff Explained! [ML Mechanics]


Mobile Legends: Red and Blue Buff Explained -Game Mechanics- Like my Facebook page: 🤍 Follow me on Twitter: 🤍 All my Mobile Legends videos in one playlist: 🤍 All my ML Game Mechanics and Tricks videos in one playlist: 🤍 Viewers occasionally asked me this question in the comment section so I thought about making a short video to explain what they are and how they work in detail. Red and Blue buff are temporary buffs you can get by killing specific Monsters in the Jungle, there is one Blue Buff Monster and one Red Buff Monster in each Jungle (ally and enemy jungle), you will be able to see their exact locations in the video but to put it in a simple way Red Buff is given by the "big" Monster in the bottom ally\top enemy jungle, Blue Buff is given by the "big" Monster in the top ally\bot enemy jungle. These Monsters spawn around 30s after the game started. Red Buff: After you hit a target with a skill you will get a 15% movement speed buff for 2s. While you have Red Buff your Physical and Magic attack will be increased by 10%. Blue Buff: For the whole duration of the buff you get 20% cooldown reduction and your skills' cost will be reduced by 50%, 20% if it's an energy based hero (eg. Fanny). Both buffs last 2 minutes and after they expire the Monster you killed to get them will immediately respawn. Getting a buff will add an icon near the bottom of the screen which will show you how much long it's going to take before it expires. Getting a buff also adds a colored ring at the bottom of your hero so everyone (both allies and enemies) will be able to see if you have a buff active. Don't forget to leave a comment and to rate the video! Outro Song: Happiness Song Link: 🤍 Artist: 7oleK - 🤍



Double wave? Sino ba ang core?

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)


#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE: 0:00 - Intro & Overview 4:50 - View Synthesis Task Description 5:50 - The fundamental difference to classic Deep Learning 7:00 - NeRF Core Concept 15:30 - Training the NeRF from sparse views 20:50 - Radiance Field Volume Rendering 23:20 - Resulting View Dependence 24:00 - Positional Encoding 28:00 - Hierarchical Volume Sampling 30:15 - Experimental Results 33:30 - Comments & Conclusion Paper: 🤍 Website & Code: 🤍 My Video on SIREN: 🤍 Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x,y,z) and viewing direction (θ,ϕ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng Links: TabNine Code Completion (Referral): 🤍 YouTube: 🤍 Twitter: 🤍 Discord: 🤍 BitChute: 🤍 Minds: 🤍 Parler: 🤍 LinkedIn: 🤍 BiliBili: 🤍 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: 🤍 Patreon: 🤍 Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Object Detection Explained | Tensorflow Object Detection | AI ML for Beginners | Edureka


Edureka PGP in AI & ML: 🤍 This Edureka video gives you a brief overview of Object Detection. In this quick guide, the following topics will be covered: 1) What is Object Detection? 2) Object Detection Use Case Python Tutorial Playlist: 🤍 Blog Series: 🤍 #PythonEdureka #Edureka #objectdetection #pythonprojects #pythonprogramming #pythontutorial #PythonTraining Subscribe to our channel to get video updates. Hit the subscribe button above: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Telegram: 🤍 SlideShare: 🤍 Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry-grade projects. Why Machine Learning & AI? Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come. - PG Program in Machine Learning & AI 1. Ranked 4th among NITs by NIRF 2. Ranked among Top 50 Institutes in India 3. Designated as Institute of National Importance - Program Features 1. Mentorship from NITW faculty 2. Placement Assistance 3. Alumni Status 4. Industry Networking - Industry Projects 1. Building a Conversational ChatBot 2. Predictive Model for Auto Insurance 3. E-commerce Website - Sales Prediction - Mentors & Instructors Dr. RBV Subramaanyam Professor NITW Dr. DVLN Somayajulu Professor NITW Dr. P. Radha Krishna Professor NITW Dr. V. Ravindranath Professor JNTU Kakinada Is this program for me? If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML. Is there any eligibility criteria for this program? A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2 Will I get any certificate at the end of the course? Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course. For more information, please write back to us at sales🤍 or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

Supervised vs Unsupervised vs Reinforcement Learning | ML Types Explained | Machine Learning Basics


Machine Learning has found its applications in almost every business sector. And there are several algorithms used in machine learning that help you build complex ML models. Each of these algorithms in machine learning can be classified into a certain category. In this video, we’ll learn about the types of machine learning you should know about alongside the key differences between them. There are primarily three types of machine learning: Supervised learning, Unsupervised learning, and Reinforcement Learning Video Walkthrough (ML Types) 0:00 Introduction 0:18 Types of Machine Learning 0:56 What is Supervised Learning Explained 2:30 What is Unsupervised Learning Explained 4:10 Supervised ML and Unsupervised ML 4:26 What is Reinforcement Learning Explained 5:33 Reinforcement vs Supervised Learning 6:11 Supervised vs Unsupervised vs Reinforcement Learning 7:54 Which is the best ML type to use? Recommended Videos, 5 Top Skills Needed for Machine Learning Engineer | ML Engineer Skill Set 🤍 7 Basic Machine Learning Concepts for Beginners | Basic ML Concepts 🤍 Difference Between AI vs ML vs DL vs Data Science Explained 🤍 9 Blockchain Career Path with Salaries 🤍 When NOT to use Machine Learning (ML) or Artificial Intelligence (AI) 🤍 All Machine Learning Models Explained in 5 Minutes 🤍 12 Futuristic AI & ML Project Ideas [Updated 2021] 🤍 Like my content on types of machine learning? Then be sure to smash that like button and hit Subscribe to get these latest updates on AI and ML tutorials! Let's get social! 🤍 🤍 🤍 #AI #ML #MLforBeginners #Supervised #Unsupervised #Reinforcement #MachineLearning #DataScience

√ The Capacity Conversion to mL Explained with Clear Examples. Watch this video to find out!


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AI vs ML vs DL vs Data Science - Difference Explained | Simplilearn


In this video, we will cover AI vs. ML vs. DL vs. Data Science in detail. From this video, you will be able to understand the difference between AI, ML, DL, and Data Science. 0:00 Deep Learning 2:31 Machine Learning 3:49 Artificial Intelligence 5:26 Deep Learning 🔥Enroll in Free Data Science Course & Get Your Completion Certificate: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Data Science tutorial videos: 🤍 #AIvsMLvsDLvsDataScience #ArtificialIntelligence #DeepLearning #MachineLearning #DataScience #DifferenceBetween #LearnDataScience #DataScience #DataScienceTutorial #DataScienceCourse #DataScienceCareers #Simplilearn About Data Science with Python Certification Course: Ranked #1 Data Science program by Economic Times The Data Science with Python certification course provides a complete overview of Python's Data Analytics tools and techniques. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlocking your career as a Data Scientist. Data Science with Python Course Overview: The Python Data Science course teaches you to master the concepts of Python programming. Through this Data Science with Python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential tools of Data Science with Python. Data Science with Python Training Key Features: ✅ 100% Money Back Guarantee ✅ 68 hours of blended learning ✅ 4 industry-based projects ✅ Interactive learning with Jupyter notebooks labs ✅ Lifetime access to self-paced learning ✅ Dedicated mentoring session from faculty of industry experts Benefits Data Science is an evolving field, and Python has become a required skill for 46 percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581 percent by 2020 and professionals with Python skills will have an additional advantage. Eligibility for this Data Science with Python course: The demand for Data Science with Python programming professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python course is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science. Pre-requisites for this Data Science with Python course: To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training. 👉Learn more at: 🤍 🔥Enroll for Free Data Science Course & Get Your Completion Certificate: 🤍 For more updates on courses and tips follow us on: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 - Instagram: 🤍 - Telegram Mobile: 🤍 - Telegram Desktop: 🤍 Get the Simplilearn app: 🤍

Machine Learning Algorithms Explained in 5 minutes | Types of ML Models | Satyajit Pattnaik


Machine Learning Algorithms Explained in 5 minutes | Types of ML Models | Satyajit Pattnaik In this video, we will be understanding various Machine Learning algorithms in under 5-6 minutes. Machine Learning is broadly classified into Supervised, Unsupervised & Reinforcement Learning, and each of the sub categories has multiple algorithms, explaining each one of them won't be possible, but we will try to cover the most important algorithms using few examples in this video. #machinelearning #mlmodels #SatyajitPattnaik Media Partner: UV MEDIA (📞 +91-9368386797) Subscribe our YouTube Channel and press the bell icon to get regular updates: 🤍 Join our Telegram Channel: 🤍 Our Popular Videos: ➮ 🤍 ➮ 🤍 ➮ 🤍 ………………………………………………………………….. Follow us on: FACEBOOK: 🤍 INSTAGRAM: 🤍 LINKEDIN: 🤍 THANKS FOR WATCHING 😊

Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability


In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for interpretable machine learning in order to diagnose and build trust in autonomous systems. Professor Hima Lakkaraju's day-long workshop at Stanford covered modern techniques for interpretable machine learning. About the speaker: Himabindu (Hima) Lakkaraju is an assistant professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in policy and healthcare to understand the real-world implications of explainable and fair ML. Hima has been named as one of the world’s top innovators under 35 by both MIT Tech Review and Vanity Fair. Her research has also received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS, and grants from NSF, Google, Amazon, and Bayer. Hima has given keynote talks at various top ML conferences and workshops including CIKM, ICML, NeurIPS, AAAI, and CVPR, and her research has also been showcased by popular media outlets including the New York Times, MIT Tech Review, TIME magazine, and Forbes. More recently, she co-founded the Trustworthy ML Initiative to enable easy access to resources on trustworthy ML and to build a community of researchers/practitioners working on the topic. Learn more on her website: 🤍 View the full playlist: 🤍 #machinelearning

Volume for kids | How many ml in a litre | Measuring volume for kids | Fum maths | Maths with Nile


Nile uses food colouring to colour water and practice measuring volume. In this second volume lesson, Nile introduces a syringe and a pipette and shows how to use them both. He then completes two challenges at the end using his measuring skills. This channel is for kids to learn and practice maths by joining in with Nile. We hope you enjoy it :) New video every week : SUNDAY 4PM SUBSCRIBE COMMENT LIKE SHARE

Machine Learning Model Drift - Concept Drift & Data Drift in ML - Explanation


What is Model Drift in Machine Learning? What is Concept Drift? What is Data Drift? Why is Model Monitoring Required? Examples of Model Drift in ML? If you had questions like this, this video is a simple explanation of these Model Drift Questions.

Quantum Numbers - n, l, ml, ms & SPDF Orbitals


This chemistry video tutorial provides a basic introduction into quantum numbers n l ml & ms. It explains the basic idea behind the s p d f orbitals. It also discusses electron configuration and drawing orbital diagrams as well. My Website: 🤍 Patreon Donations: 🤍 Amazon Store: 🤍 Subscribe: 🤍 Disclaimer: Some of the links associated with this video may generate affiliate commissions on my behalf. As an amazon associate, I earn from qualifying purchases that you may make through such affiliate links.

Artificial Intelligence and Machine Learning | Difference between AI & ML | Explained in tamil | EES


#AIvsML #ArtificialIntelligence #MachineLearning #EES Hey Guys! In this video we will see about the Artificial Intelligence and Machine Learning and also the differences between them. like , comment and share the video Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly 🔴 Android App Development Course 🤍 🔴 Game Development = 🤍 🔴 Html & CSS Tutorial 🤍 🔴 Web Application Development using Django Course 🤍 🔴 Adobe XD Tutorial 🤍 🔴 Wordpress Development Tutorials 🤍 🔴 Learn in 5 Minutes 🤍 🔴 Just Information = 🤍 🔴 Tell me why? = 🤍 #EternalEngineersSolutions Connect us on social media so you will find my entire tutorial at first. If that sounds like something that could help grow your business, then make sure to join me by subscribing! ➥➥➥ SUBSCRIBE FOR MORE VIDEOS ➥➥➥ Never miss any video. Subscribe ⇢ 🤍 ✅ For Branding & Business Inquiries = ✅ ► eesofficials🤍 ✅ Facebook: 🤍 ✅ Instagram 🤍 ✅ Our Website 🤍

Introduction to Explainable AI (ML Tech Talks)


This talk introduces the field of Explainable AI, outlines a taxonomy of ML interpretability methods, walks through an implementation deepdive of Integrated Gradients, and concludes with discussion on picking attribution baselines and future research directions. Chapters: 00:00 - Intro 2:31 - What is Explainable AI? 8:40 - Interpretable ML methods 14:52 - Deepdive: Integrated Gradients (IG) 39:13 - Picking baselines and future research directions Resources: Integrated gradients → 🤍 Vertex AI → 🤍 What-if-tool → 🤍 Catch more ML Tech Talks → 🤍 Subscribe to TensorFlow → 🤍 product: TensorFlow - General; re_ty: Publish;

Machine Learning Polynomial Regression Explained | ML Tutorial for Beginners


In this video, learn Machine Learning Polynomial Regression Explained | ML Tutorial for Beginners. Find all the videos of the Machine Learning Course in this playlist: 🤍 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-9024244886, +91-9269698122 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #MachineLearningCourse #PolynomialRegression

OpenAI CLIP Explained | Multi-modal ML


OpenAI's CLIP explained simply and intuitively with visuals and code. Language models (LMs) can not rely on language alone. That is the idea behind the "Experience Grounds Language" paper, that proposes a framework to measure LMs' current and future progress. A key idea is that, beyond a certain threshold LMs need other forms of data, such as visual input. The next step beyond well-known language models; BERT, GPT-3, and T5 is "World Scope 3". In World Scope 3, we move from large text-only datasets to large multi-modal datasets. That is, datasets containing information from multiple forms of media, like *both* images and text. The world, both digital and real, is multi-modal. We perceive the world as an orchestra of language, imagery, video, smell, touch, and more. This chaotic ensemble produces an inner state, our "model" of the outside world. AI must move in the same direction. Even specialist models that focus on language or vision must, at some point, have input from the other modalities. How can a model fully understand the concept of the word "person" without *seeing* a person? OpenAI's Contrastive Learning In Pretraining (CLIP) is a world scope three model. It can comprehend concepts in both text and image and even connect concepts between the two modalities. In this video we will learn about multi-modality, how CLIP works, and how to use CLIP for different use cases like encoding, classification, and object detection. 🌲 Pinecone article: 🤍 🤖 70% Discount on the NLP With Transformers in Python course: 🤍 🎉 Subscribe for Article and Video Updates! 🤍 🤍 👾 Discord: 🤍

How Machines Learn


How do all the algorithms around us learn to do their jobs? OMG PLUSHIE BOTS!!: 🤍 Bot Wallpapers on Patreon: 🤍 Footnote: 🤍 Podcasts: 🤍 🤍 Thank you to my supporters on Patreon: James Bissonette, James Gill, Cas Eliëns, Jeremy Banks, Thomas J Miller Jr MD, Jaclyn Cauley, David F Watson, Jay Edwards, Tianyu Ge, Michael Cao, Caron Hideg, Andrea Di Biagio, Andrey Chursin, Christopher Anthony, Richard Comish, Stephen W. Carson, JoJo Chehebar, Mark Govea, John Buchan, Donal Botkin, Bob Kunz 🤍 How neural networks really work with the real linear algebra: 🤍 Music by: 🤍

Democratize machine learning: ML-Agents explained - Unite LA


Recent advances in mathematics, computation, and data are radically redefining what is possible with machine learning (ML). These new techniques have made ML not only more tractable, but broadened the potential uses for it in game production and beyond! In this video, learn about Unity ML-Agents, an open source toolkit that bridges the world of Unity and Machine Learning. We'll go over how advances in AI, such as reinforcement and imitation learning, can open the door for new ways of game development. Speakers: Arthur Juliani (Deep Learning) and Vladimir Oster (Senior Software Engineer, Machine Learning ) - Unity Technologies Learn more: 🤍 Help us caption & translate this video! 🤍

AI VS ML VS DL VS Data Science


Please do subscribe my vlogging channel for motivational videos 🤍 ⭐ 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! 🤍 Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more 🤍 Please do subscribe my other channel too 🤍 - Recording Gears That I Use 🤍 - Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍

[Explained] What is MLOps | Getting started with ML Engineering


Links to relevant resources: - Blogpost on the same: 🤍 - [Book]: Machine learning engineering: 🤍 - Introduction to MLOps - 🤍 - Professional ML engineer certification - 🤍 - AWS Certified ML Specialty - 🤍 - Paper on hidden technical debt in ML systems: 🤍 You can also connect with me on: LinkedIn: 🤍 Twitter: 🤍 Instagram: 🤍 Medium: 🤍

Neural Network Simplified for Beginners | Neural Networks for ML Explained | Machine Learning


We cover Neural networks in the field of AI. An exclusive beginners guide to help you get familiar with essential concepts and the role neural networks play. Join India's number one outcome-focused Data Science and Machine Learning Program now: 🤍 What are neural networks? A Neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of biological neurons or an artificial neural network, for solving artificial intelligence problems. Neural networks reflect the behaviour of the human brain, allowing computer programs to recognise patterns and solve common problems in the fields of AI, machine learning, and deep learning. Neural networks are designed to work just like the human brain does. In the case of recognising handwriting or facial recognition, the brain very quickly makes some decisions. The following topics are covered in this Neural Networks tutorial: 0:00 - Introduction 0:05 - Neural networks for beginners 0:54 - What is Neural network 1:15 - What is deep learning 2:15 - Neural network with example 3:30 - What is perceptron 4:30 - What is multilayer perceptron 6:00 - Components of neural network 7:55 - How neural network works 9:50 - How backpropagation work About Scaler We are a tech-focused upskilling and reskilling platform catering to tech enthusiasts in universities and working professionals. There are more Scaler graduates working at Amazon than all of the IITs combined! Learn more about Scaler: 🤍 📌 Follow us on Social and be a part of an amazing tech community📌 👉 Meet like-minded coder folks on Discord - 🤍 👉 Tweets you cannot afford to miss out on - 🤍 👉 Check out student success stories, expert opinions, and live classes on Linkedin - 🤍 👉 Explore value packed reels, carousels and get access to exclusive updates on Instagram - 🤍 📢 Be a part of our one of a kind telegram community: 🤍 🔔 Hit that bell icon to get notified of all our new videos 🔔 If you liked this video, please don't forget to like and comment. Never miss out on our exclusive videos to help boost your coding career! Subscribe to Scaler now! 🤍 #neuralnetworks #datascience #ml #ai #machinelearning

What is Image Segmentation ? | Computer Vision & ML Techniques Explained for Beginners 17


Artificial Intelligence terms explained in a minute for everyone! This week's term is Image Segmentation. Ask any questions or remarks you have in the comments, I will gladly answer to everything! Subscribe to not miss any AI news and terms explained! #ai #segmentation #machinelearning Follow me for more AI content! Instagram: 🤍 Twitter: 🤍 Facebook: 🤍 Share this to someone who needs to learn more about Artificial Intelligence! Best Machine Learning Course - Stanford - Andrew Ng 🤍 Best Deep Learning Specialization - Andrew Ng 🤍 AI For Everyone - Everything you need to know about AI for everyone - Andrew Ng 🤍 Funny AI merch if you are interested: 🤍 Song credit: 🤍

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