MachineLearning

MachineLearning смотреть последние обновления за сегодня на .

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

1492577
44981
1786
00:26:09
18.08.2021

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

Machine Learning Course for Beginners

918652
32381
978
09:52:19
30.08.2021

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: 🤍

What is Machine Learning?

924600
15466
186
00:05:23
25.08.2017

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

Machine Learning Explained in 100 Seconds

191349
13650
385
00:02:35
09.09.2021

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

Python Machine Learning Tutorial (Data Science)

1542347
38588
1313
00:49:43
17.09.2020

Python Machine Learning Tutorial - Learn how to predict the kind of music people like. 👍 Subscribe for more Python tutorials like this: 🤍 👉 The CSV file used in this tutorial: 🤍 🚀 Learn Python in one hour: 🤍 🚀 Python (Full Course): 🤍 Want to learn more from me? Courses: 🤍 Twitter: 🤍 Facebook: 🤍 Blog: 🤍 #Python, #MachineLearning, #Jupyter TABLE OF CONTENT 0:00:00 Introduction 0:00:59 What is Machine Learning? 0:02:58 Machine Learning in Action 0:05:45 Libraries and Tools 0:10:40 Importing a Data Set 0:17:01 Jupyter Shortcuts 0:22:53 A Real Machine Learning Problem 0:26:09 Preparing the Data 0:29:15 Learning and Predicting 0:33:20 Calculating the Accuracy 0:39:41 Persisting Models 0:42:55 Visualizing a Decision Tree

Machine Learning | What Is Machine Learning? | Machine Learning Tutorial | Simplilearn

3409181
42500
2102
00:07:52
19.09.2018

🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 This Machine Learning basics video will help you understand what is Machine Learning, 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 a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, 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. Below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? ( 00:21 ) - machine learning 2. Types of Machine Learning ( 02:43 ) - machine learning 2. What is Supervised Learning? ( 02:53 ) - machine learning 3. What is Unsupervised Learning? ( 03:46 ) - machine learning 4. What is Reinforcement Learning? ( 04:37 ) - machine learning 5. Machine Learning applications ( 06:25 ) - machine learning 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 #WhatIsMachineLearning #MachineLearningTutorial #MachineLearningBasics #MachineLearningTutorialForBeginners #Simplilearn About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing and 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 size 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 which includes 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 scientist 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: 🤍

Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

2387477
44448
1185
09:38:32
22.09.2019

🔥 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 in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 🔴 Subscribe to our channel to get latest video updates: 🤍 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: 🤍 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: 🤍 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: 🤍 📌𝐌𝐞𝐞𝐭𝐮𝐩: 🤍 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: 🤍 Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at sales🤍edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Machine Learning for Everybody – Full Course

121880
5329
193
03:53:53
26.09.2022

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: 🤍

The 7 steps of machine learning

2296183
39108
568
00:10:36
31.08.2017

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

Machine Learning & Artificial Intelligence: Crash Course Computer Science #34

769722
17393
417
00:11:51
01.11.2017

So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: 🤍 Want to know more about Carrie Anne? 🤍 The Latest from PBS Digital Studios: 🤍 Want to find Crash Course elsewhere on the internet? Facebook - 🤍 Twitter - 🤍 Tumblr - 🤍 Support Crash Course on Patreon: 🤍 CC Kids: 🤍

Machine Learning Methods - Computerphile

201079
4110
110
00:10:41
02.09.2015

We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains supervised and un-supervised methods of machine learning. Silicon Brain: 1,000,000 ARM Cores: 🤍 Brian Kerninghan on Bell Labs: 🤍 Could We Ban Encryption?: 🤍 Computer That Changed Everything - Altair 8800: 🤍 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍

The Mathematics of Machine Learning

372578
12871
317
00:16:34
30.11.2018

Check out the Machine Learning Course on Coursera: 🤍 STEMerch Store: 🤍 Support the Channel: 🤍 PayPal(one time donation): 🤍 Instagram: 🤍 Twitter: 🤍 Join Facebook Group: 🤍 ►My Setup: Space Pictures: 🤍 Camera: 🤍 Mic: 🤍 Tripod: 🤍 Equilibrium Tube: 🤍 ►Check out the MajorPrep Amazon Store: 🤍

11. Introduction to Machine Learning

1366163
19439
406
00:51:31
19.05.2017

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 🤍

Andrew Ng’s Machine Learning Specialization 2022 | What is it and is it worth taking?

67555
2596
150
00:12:07
29.06.2022

🤖 Machine Learning Specialization by Andrew Ng 👉 🤍 📚 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 👉🤍 📚 The Master Algorithm 👉🤍 Hey data nerds 👋, in this video I'm talking about the new Machine Learning Specialization by Andrew Ng on Coursera. I'll be giving you a review of the course structure, the topics covered, and sharing some of my thoughts about whether the course is worth taking + tips and tricks for learning. Thank you for watching 🙌🏽 MY GEAR 👩🏻‍💻 My laptop and iPad for doing DS/ study 👉 🤍 ⚙️ Tech I use for making Youtube videos 👉 🤍 🔍Check out my other videos on Data Analyst/ Data Science 👉 🤍 COURSES & RESOURCES 💯 SQL Courses: Select Star SQL 👉 🤍 Bipp.io SQL tutorials 👉 🤍 📑 Excel Courses: Excel Skills for Business 👉 🤍 📊 Data Visualisation: 📚 Books I recommend: 🤍 How to create effective charts and diagrams 👉 🤍 Data Viz Catalog 👉 🤍 🤖 Programming Courses: Python for Everybody Specialization 👉🤍 Introduction to AI with Python (Harvard University) 👉 🤍 Using Python for Research (Harvard University) 👉 🤍 R Programming 👉 🤍 (this course can be tough at times especially at the assignment parts, but it's worth the challenge. I'd recommend it to someone who's already familiar with R or other programming languages.) 🙋🏻‍♀️ LET'S CONNECT! 🔔 SUBSCRIBE to my channel: 🤍 🤓 Join my Discord server: 🤍 📩 SUBSCRIBE to my Substack to get future newsletters from me: 🤍 ✍ FOLLOW me on Medium: 🤍 (I sometimes make both video and article versions of my content, by following me on Medium you will have access to the content in writing too). 🤳 VISIT my Tiktok: 🤍 🔑 TIMESTAMPS 0:00 - Intro 0:25 - New ML Specialization is launched 1:29 - Supervised ML course 1:49 - Advanced Learning Algorithms course 2:42 - Unsupervised ML course 3:03 - Changes & updates from the old course 4:26 - Course format & tools 5:01 - Time & money investment 5:57 - My likes & concerns 8:48 - Is it worth taking & Who should take it 10:18 - What I recommend for learning in the course As a member of the Amazon and Coursera Affiliate Programs, I earn a commission from qualifying purchases on some of the links above. It costs you nothing but helps me with content creation. #machinelearning #Datanerd #DataAnalysis #datascience #ThuVu #dataanalytics

Machine Learning vs Deep Learning

62843
2292
27
00:07:50
31.03.2022

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

How Machines Learn

7594661
467549
21986
00:08:55
18.12.2017

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: 🤍

Machine Learning basics

22597
752
49
00:04:43
19.04.2022

When you think of Machine Learning, what do you think of? Learn what Machine Learning is, how computers find patterns, and what parameters are given for the computer to learn from data. Watch more AI for Anyone → 🤍 Subscribe to Google Developers → 🤍 AI for Anyone is a series aimed at anyone who is interested in learning about Artificial Intelligence, tech background or no tech background! #AIforAnyone #AI #ML #GoogleDevelopers

How I use Machine Learning as a Data Analyst

72724
2542
165
00:11:50
30.08.2022

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. #dataanalyst #datascience #machinelearning

How to Get Started with Machine Learning & AI

531957
23512
578
00:10:32
29.09.2019

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

Machine Learning: Living in the Age of AI | A WIRED Film

1385426
26727
1065
00:41:17
20.06.2019

“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup. Still haven’t subscribed to WIRED on YouTube? ►► 🤍 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. Machine Learning: Living in the Age of AI | A WIRED Film

What is Machine Learning?

436639
3034
79
00:02:19
11.01.2017

Machine learning is all around us; on our phones, powering social networks, helping the police and doctors, scientists and mayors. But how does it work? In this animation we take a look at how statistics and computer science can be used to make machines that learn. Visit 🤍oxfordsparks.ox.ac.uk to find out more. Don’t forget to connect with us on Facebook 🤍OxSparks and on Twitter 🤍OxfordSparks Instagram: 🤍OxfordSparks

Intro to Machine Learning (ML Zero to Hero - Part 1)

755247
17003
374
00:07:18
30.08.2019

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 → 🤍

2020 Machine Learning Roadmap (95% valid for 2022)

871698
30152
1034
02:37:14
12.07.2020

Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction. Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps. Links: Interactive Machine Learning Roadmap - 🤍 Machine Learning Roadmap Resources - 🤍 Learn ML (beginner-friendly courses I teach) - 🤍 ML courses/books I recommend - 🤍 Read my novel Charlie Walks - 🤍 Timestamps: 0:00 - Hello & logistics 0:57 - PART 0: INTRO 1:42 - Brief overview of topics 3:05 - What is machine learning? 4:37 - Machine learning vs. traditional programming 7:41 - Why use machine learning? 8:44 - The number 1 rule of machine learning 10:45 - What is machine learning good for? 14:27 - How Tesla uses machine learning 17:57 - What we're going to cover in this video 20:52 - PART 1: Machine Learning Problems 22:27 - Categories of learning 26:17 - Machine learning problem domains 29:04 - Classification 33:57 - Regression 39:35 - PART 2: Machine Learning Process 41:57 - 6 major steps in a machine learning project 43:57 - Data collection 49:15 - Data preparation 1:04:00 - Training a model 1:23:33 - Analysis/evaluation 1:26:40 - Serving a model 1:29:09 - Retraining a model 1:30:07 - An example machine learning project 1:33:15 - PART 3: Machine Learning Tools 1:34:20 - Machine learning tools overview 1:38:36 - Machine learning toolbox (experiment tracking) 1:39:54 - Pretrained models for transfer learning 1:41:49 - Data and model tracking 1:43:35 - Cloud compute services 1:47:07 - Deep learning hardware (build your own deep learning PC) 1:47:53 - AutoML (automatic machine learning) 1:51:47 - Explainability (explaining the outputs of your machine learning model) 1:53:38 - Machine learning lifecycle (tools for end-to-end projects) 1:59:24 - PART 4: Machine Learning Mathematics 1:59:37 - The main branches of mathematics used in machine learning 2:03:16 - How I learn the math for machine learning 2:06:37 - PART 5: Machine Learning Resources 2:07:17 - A warning 2:08:42 - Where to start learning machine learning 2:14:51 - Made with ML (one of my favourite new websites for ML) 2:16:07 - Wokera ai (test your AI skills) 2:17:17 - A beginner-friendly path to start machine learning 2:19:02 - An advanced path for learning machine learning (after the beginner path) 2:21:43 - Where to learn the mathematics for machine learning 2:22:23 - Books for machine learning 2:24:27 - Where to learn cloud services 2:24:47 - Helpful rules and tidbits of machine learning 2:26:05 - How and why you should create your own blog 2:28:29 - Example machine learning curriculums 2:30:19 - Useful machine learning websites to visit 2:30:59 - Open-source datasets 2:31:26 - How to learn how to learn 2:32:57 - PART 6: Summary & Next Steps Connect elsewhere: Get email updates on my work - 🤍 Support on Patreon - 🤍 Web - 🤍 Quora - 🤍 Medium - 🤍 Twitter - 🤍 LinkedIn - 🤍 #machinelearning #datascience

Complete Machine Learning In 6 Hours| Krish Naik

156578
4162
115
06:37:52
28.05.2022

All the materials are available in the below link 🤍 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

What is Machine Learning?

44182
433
4
00:02:03
22.02.2018

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!

Don't learn machine learning

227235
5227
444
00:17:57
30.03.2020

Should you learn machine learning? Yes, you should. But how? By avoiding being a donkey. There is an abundance of machine learning resources out there, too many. The good thing is, most of them are pretty good. But remember, having too many options is the same as no options. Instead of trying to learn them all, pick something, use it, make something with it and if it doesn't work, move onto the next thing. If you're stuck being a donkey, maybe the procrastination is a sign it's time to move on or find a better option. Thank you to Sk for sending through the article. If you've got something you want to be reviewed, send it through. Links: Don't learn machine learning article - 🤍 Building an X-ray classifier - 🤍 Teachable machine - 🤍 42 days: a cure for shiny object syndrome article - 🤍 My machine learning course - 🤍 Get email updates on my work - 🤍 Support on Patreon - 🤍 Connect elsewhere: Web - 🤍 Quora - 🤍 Medium - 🤍 Twitter - 🤍 LinkedIn - 🤍 #machinelearning #datascience

What Does A Machine Learning Engineer At Amazon Do?

85975
2810
150
00:21:22
28.05.2021

In this video, I sat down with Ryan Doan, an ex-Amazon Machine Learning Engineer and the creator of MLExpert, to talk about what Machine Learning Engineers do, how hard Machine Learning is, and how different Machine Learning is from normal Software Engineering. Check out our Ryan's first YouTube video here: 🤍 Check out MLExpert here: 🤍 AlgoExpert: 🤍 SystemsExpert: 🤍 My LinkedIn: 🤍 My Instagram: 🤍 My Twitter: 🤍 Prepping for coding interviews or systems design interviews? Practice with hundreds of video explanations of popular interview questions and a full-fledged coding workspace on AlgoExpert - 🤍 - and use the promo code "clem" for a discount on the platform!

Day 1/30 Machine Learning Master Class - Sanjay

44487
2437
151
02:13:28
19.09.2022

If You Haven't Register still, Register Now: 🤍 4 in 1 Internship : - 🤍 4 In 1 internship : international payment link :🤍 Please find the attendance link 🤍 📅19th Sept to 18th Oct (30 Days FREE) ⏰5:30 to 6:30 PM 👨‍🏫Connect with Course Instructor : A P Sanjay Kumar - 🤍 👨‍🏫Connect with Dierctor :M K JEEVARAJAN - 🤍 Attendance link : 🤍 Join Telegram group : 🤍 ❓Are You a Student, Wants to Build Your Profile filled with A.I Projects before completing your studies ❓Are You a Professor/Working Professional, who wants to switch Your High Paying Job ❓Are You a Person who doesn't know anything about A.I, who wants to learn A.I from Zero to Hero Level We are offering You a 30 Days FREE Machine Learning Master Class with FREE E-Certificate What I Will Learn? ✅Day-1: Overview A.I | Machine Learning ✅Day-2: Introduction to Python | How to write code in Google Colab, Jupyter Notebook, Pycharm & IDLE ✅Day-3: Advertisement Sale prediction from an existing customer using LOGISTIC REGRESSION ✅Day-4: Salary Estimation using K-NEAREST NEIGHBOR ✅Day-5: Character Recognition using SUPPORT VECTOR MACHINE ✅Day-6: Titanic Survival Prediction using NAIVE BAYES ✅Day-7: Leaf Detection using DECISION TREE ✅Day-8: Handwritten digit recognition using RANDOM FOREST ✅Day-9: Evaluating Classification model Performance using CONFUSIONMATRIX, CAP CURVE ANALYSIS & ACCURACY PARADOX ✅Day-10: Classification Model Selection for Breast Cancer classification ✅Day-11: House Price Prediction using LINEAR REGRESSION Single Variable ✅Day-12: Exam Mark Prediction using LINEAR REGRESSION Multiple Variable ✅Day-13: Predicting the Previous salary of the New Employee using POLYNOMIAL REGRESSION ✅Day-14: Stock price prediction using SUPPORT VECTOR REGRESSION ✅Day-15: Height Prediction from the Age using DECISION TREE REGRESSION ✅Day-16: Car price prediction using RANDOM FOREST ✅Day-17: Evaluating Regression model performance using R-SQUARED INTUITION & ADJUSTED R-SQUARED INTUITION ✅Day-18: Regression Model Selection for Engine Energy prediction. ✅Day-19: Identifying the Pattern of the Customer spent using K-MEANS CLUSTERING ✅Day-20: Customer Spending analysis using HIERARCHICAL CLUSTERING ✅Day-21: Leaf types data visualization using PRINCIPLE COMPONENT ANALYSIS ✅Day-22: Finding Similar Movie based on ranking using SINGULAR VALUE DECOMPOSITION ✅Day-23: Market Basket Analysis using APIRIORI ✅Day-24: Market Basket Optimization/Analysis using ECLAT ✅Day-25: Web Ads. Click through Rate optimization using UPPER BOUND CONFIDENCE ✅Day-26: Sentimental Analysis using Natural Language Processing ✅Day-27: Breast cancer Tumor prediction using XGBOOST ✅Day-28: Bank Customer classification using ANN ✅Day-29: Pima-Indians Diabetes Classification using CONVOLUTIONAL NEURAL NETWORK ✅Day-30: A.I Snake Game using REINFORCEMENT LEARNING #artificialintelligence #machinelearning #deeplearning #ai #free

What is Machine Learning?

30974
646
25
00:08:23
14.07.2021

Learn more about Machine Learning → 🤍 Learn more about Deep Learning → 🤍 Learn more about Supervised Learning → 🤍 Blog Post: AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? → 🤍 Check out "What is NLP (Natural Language Processing)?" lightboard video → 🤍 Earn a badge with FREE interactive Kubernetes labs → 🤍 Check out IBM Cloud Pak for Data → 🤍 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

Machine Learning Explained in 5 Minutes

87604
2119
156
00:05:15
23.11.2017

Machine learning explained simply. In this video you'll learn what exactly machine learning is and machine learning basics. No knowledge of machine learning required to watch this video. Hi! I'm Jade. Subscribe to Up and Atom for new physics, math and computer science videos every week! *SUBSCRIBE TO UP AND ATOM* 🤍 *Follow me: 🤍upndatom INSTAGRAM: 🤍 TWITTER: 🤍 *Other Videos You Might Like* Math, When Are You Going To Use It? 🤍 Quantum Cryptography Explained Simply 🤍 Are Forcefields Possible? 🤍 *SOURCES* 🤍 🤍 🤍 🤍

Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn

301060
4972
1521
01:11:05
21.03.2018

🔥 Enroll For Simplilearn's Data Science Job Guarantee Program: 🤍 This Machine Learning Algorithms video will help you learn what is Machine Learning, various Machine Learning problems and algorithms, key Machine Learning algorithms with simple examples, and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest, and KNN algorithm. Boost your career with Simplilearn's 6-months Data Science Job Guarantee Program with a placement, guarantee! Land your dream job with an average pay of 8 LPA within just 180 days of graduation or get your money back! Learn from the best and get placed in a top role by investing in yourself, risk-free - a Simplilearn promise. ✅ Job Guarantee ✅ Salary hikes: 90% - 260% averages ✅ Placements with top companies 🔥 Enroll For Simplilearn's Data Science Job Guarantee Program: 🤍 *Disclaimer: Valid only for Simplilearn Job Guarantee Programs. Please read the applicable Frequently Asked Questions (FAQs) and Terms and Conditions (T&Cs) carefully prior to enrolment. Past record is no guarantee of future prospect 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 Below topics are covered in this Machine Learning Algorithms Tutorial: 00:00 - 03:39 Machine Learning example and real-world applications 03:39 - 04:40 What is Machine Learning? 04:40 - 06:14 Processes involved in Machine Learning 06:14 - 09:40 Type of Machine Learning Algorithms 09:40 - 10:04 Popular Algorithms in Machine Learning 10:04 - 29:10 Linear regression 29:10 - 52:49 Logistic regression 52:49 - 01:04:45 Decision tree and Random forest 01:04:52 - 01:10:28 K nearest neighbor Dataset Link - 🤍 What is Machine Learning? Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Various machine learning algorithms are used to train models that can solve business problems. Linear regression, Logistic regression, Decision tree, Random forest, and K nearest neighbors are some of the popular machine learning algorithms used in the industries. 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- 🤍 Machine Learning Articles: 🤍 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 - 🤍 #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse 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 knowledge and hands-on skills required for certification and job competency in Machine Learning. To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: 🤍 For more updates on courses and tips follow us on: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍

Machine Learning With Python Full Course 2022 | Machine Learning Tutorial for Beginners| Simplilearn

63893
1651
32
09:58:08
02.03.2022

🔥Free Machine Learning Course With Completion Certificate: 🤍 In this video on Machine Learning with Python full course, you will understand the basics of machine learning, essential applications of machine learning, machine learning concepts, and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap for 2021? Machine Learning Basics Top 10 applications of machine learning Machine Learning Tutorial Part-1 Why Machine Learning What is Machine Learning Types of Machine Learning Supervised Learning Reinforcement Learning Supervised vs Unsupervised Learning Decision Trees Machine Learning Tutorial Part-2 K-Means Algorithm Mathematics for Machine Learning What is Data? Quantitative/Categorical Data Qualitative/Categorical Data Linear Algebra Calculus Statistics Demo on Statistics Probability Demo on Naive Bayes Linear Regression Analysis Logistic Regression Confusion Matrix Decision Tree in Machine Learning Random Forest K Nearest Neighbors Support Vector Machine Regularization in ML PCA US Election Prediction Machine Learning roadmap 2021 ✅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 -🤍 About Machine Learning Certification Course: Explore this Machine Learning certification course to understand cutting-edge concepts in machine learning, an exciting branch of Artificial Intelligence. This Machine Learning online training will provide you the skills needed to become a successful Machine Learning Engineer today. Machine Learning Course Overview: This Machine Learning course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Machine Learning Training Key Features: ✅ 100% Money Back Guarantee ✅ Gain expertise with 25+ hands-on exercises ✅ 4 real-life industry projects with integrated labs ✅ Dedicated mentoring sessions from industry experts ✅ 58 hours of Applied Learning Benefits of Machine Learning Course: The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent. Eligibility of Machine Learning Course: The Machine Learning certification online course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning. Pre-requisites of Machine Learning Course: This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course. How do I become a Machine Learning Engineer? This course will give you a complete overview of Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. You will earn Simplilearn’s Machine Learning certification that will attest to your new skills and on-the-job expertise. 👉Learn more at: 🤍 Get the Simplilearn app: 🤍 For more information about Simplilearn’s courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 - Instagram: 🤍 - Telegram Mobile: 🤍 - Telegram Desktop: 🤍 Get the Simplilearn app: 🤍

Machine Learning Fundamentals: Bias and Variance

869448
25813
1198
00:06:36
17.09.2018

Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand. For a complete index of all the StatQuest videos, check out: 🤍 If you'd like to support StatQuest, please consider... Buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - 🤍 Paperback - 🤍 Kindle eBook - 🤍 Patreon: 🤍 ...or... YouTube Membership: 🤍 ...a cool StatQuest t-shirt or sweatshirt: 🤍 ...buying one or two of my songs (or go large and get a whole album!) 🤍 ...or just donating to StatQuest! 🤍 Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 🤍 0:00 Awesome song and introduction 0:29 The data and the "true" model 1:23 Splitting the data into training and testing sets 1:40 Least Regression fit to the training data 2:16 Definition of Bias 2:33 Squiggly Line fit to the training data 3:40 Model performance with the testing dataset 4:06 Definition of Variance 5:10 Definition of Overfit Correction: 4:06 I say that the difference in fits between the training dataset and the testing dataset is called Variance. However, I should have said that the difference is a _consequence_ of variance. Technically, variance refers to the amount by which the predictions would change if we fit the model to a different training data set. #statquest #biasvariance #ML

Machine Learning Zero to Hero (Google I/O'19)

1670383
34874
824
00:35:33
09.05.2019

This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you through understanding many of the new concepts in machine learning that you might not be familiar with including eager mode, training loops, optimizers, and loss functions. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → 🤍 TensorFlow at Google I/O 2019 Playlist → 🤍 Google I/O 2019 All Sessions Playlist → 🤍 Learn more on the I/O Website → 🤍 Subscribe to the TensorFlow Channel → 🤍 Get started at → 🤍 Speaker(s): Laurence Moroney and Karmel Allison T700B4 event: Google I/O 2019; re_ty: Publish; product: Cloud - AI and Machine Learning - AI building blocks; fullname: Karmel Allison, Laurence Moroney;

Types Of Machine Learning | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn

79110
1555
57
00:21:09
03.11.2020

Machine Learning helps you build models that can make predictions and take decisions of their own. This video on Types of Machine Learning and Algorithms will make you understand what Machine Learning is and the various types of Machine Learning. You will learn about Supervised, Unsupervised, and Reinforcement learning. You will know how they work and how to choose the right Machine Learning algorithm. Finally, we'll implement a Machine Learning project/ hands-on demo using Python. 🔥Free Machine Learning Course: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #TypesOfMachineLearning #MachineLearningAlgorithms #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #SimplilearnMachineLearning #MachineLearningCourse To learn more about this topic, visit: 🤍 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 knowledge and hands-on skills required for certification and job competency in Machine Learning. 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 which includes working on 28 projects and one capstone project. 3. Acquire 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 👉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: 🤍

Machine Learning Tutorial: From Beginner to Advanced

41497
844
13
00:31:56
04.03.2020

Explore the fundamentals behind machine learning, focusing on unsupervised and supervised learning. You’ll learn what each approach is, and you’ll see the differences between them. In addition, you’ll explore common machine learning techniques including clustering, classification, and regression. Advanced topics include: - Feature engineering for transforming raw data into features that are suitable for a machine learning algorithm. - ROC curves, for comparing and assessing machine learning results. - Hyperparameter optimization, so you can find the best set of parameters for a machine learning algorithm. - Embedded systems, including best practices for preparing your machine learning models to run on embedded devices. Learn more about using MATLAB for machine learning: 🤍 Get a machine learning MATLAB trial: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)

1479337
19597
85
01:15:20
17.04.2020

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: 🤍 Listen to the first lecture in Andrew Ng's machine learning course. This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines. Andrew Ng is an Adjunct Professor of Computer Science at Stanford University. View more about Andrew on his website: 🤍 To follow along with the course schedule and syllabus, visit: 🤍 05:21 Teaching team introductions 06:42 Goals for the course and the state of machine learning across research and industry 10:09 Prerequisites for the course 11:53 Homework, and a note about the Stanford honor code 16:57 Overview of the class project 25:57 Questions #AndrewNg #machinelearning

How Machine Learning Enhances Healthcare | Marzyeh Ghassemi | TEDxUofTSalon

12753
312
17
00:11:01
19.02.2021

Why aren't mistakes always a bad thing? And what does AI have to do with that? Find out as Marzyeh Ghassemi delves into how the machine learning revolution can be applied in a healthcare setting to improve patient care. Dr. Marzyeh Ghassemi is an assistant professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member as well as a CAnada CIFAR Chair in Aritifical Intelligence. She was named on of MITs top 35 Innovators under 35 for her groundbreaking work in AI and hospital data. Her research group, Machine Learning for Health, focuses on creating and applying machine learning to understand and improve health. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at 🤍

Active (Machine) Learning - Computerphile

104271
3650
148
00:06:11
05.04.2019

Machine Learning where you put in a fraction of the effort? What's not to like? - Dr Michel Valstar explains Active & Cooperative Learning. 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍

5 Beginner Friendly Steps to Learn Machine Learning

153144
8540
442
00:13:42
15.09.2019

This video breaks down practical steps on how to learning machine learning with Python. It's the video I wish I had watched when I started learning machine learning. My machine learning course - 🤍 Blog post version of this video - 🤍 Learn all of this in one place on DataCamp - 🤍 Bonus: A 6 step field guide for the modelling phase of machine learning projects - 🤍 Other Links Mentioned (in order) Step 1 - Learn Python, data science tools and machine learning concepts Elements of AI - 🤍 Python for Everybody on Coursera - 🤍 Learn Python by freeCodeCamp - 🤍 Corey Schafer's Anaconda Tutorial - 🤍 Bonus: My Anaconda Blog Post - 🤍 Corey Schafer's Jupyter Notebook Tutorial - 🤍 Step 2 - Learn data manipulation, analysis and visualization with pandas, NumPy and Matplotlib Applied Data Science with Python - 🤍 Codebasics Pandas series - 🤍 freeCodeCamp NumPy video - 🤍 Sentdex Matplotlib series - 🤍 Step 3 - Learn machine learning with scikit-learn Data School scikit-learn series - 🤍 Bonus: fastai machine learning course - 🤍 Step 4 - Learn deep learning and neural networks Andrew Ng’s deep learning specialization on Coursera - 🤍 fastai deep learning curriculum - 🤍 Step 5 - Extra curriculum & books My favourite machine learning books (video) - 🤍 Python for Data Analysis by Wes McKinney - 🤍 Hands-on Machine Learning with scikit-learn and TensorFlow by Aurelien Geron - 🤍 The Mechanics of Machine Learning by Terrence Parr and Jeremy Howard - 🤍 A Gentle Introduction to Exploratory Data Analysis (project example) - 🤍 How to work on your own machine learning projects (article) - 🤍 Timestamps 0:26 - Who this video is for 0:41 - My style of learning (code first) 1:51 - Step 1, learn Python, data science tools and machine learning concepts 3:10 - Step 2, learn data manipulation, analysis and visualization with Pandas, NumPy and Matplotlib 4:57 - Step 3, learn machine learning with scikit-learn 6:07 - Step 4, learn deep learning and neural networks 7:33 - Step 5, Extra curriculum & books 10:07 - Where can I learn all these skills? 10:47 - How long should all of this take? 11:45 - What about statistics? Probability? Math? 12:19 - What about certifications? Note: Affiliate links have been used where possible. This means when you click a link and purchase something I may get a percentage of the purchase price. It will not change the price of the item. Get email updates on my work - 🤍 Support on Patreon - 🤍 Connect elsewhere: Web - 🤍 Quora - 🤍 Medium - 🤍 Twitter - 🤍 LinkedIn - 🤍

Назад
Что ищут прямо сейчас на
MachineLearning orginal kalar buy rdp online admin access cheap rdp bhel share ban rdp canada buy rdp online m1x mac mini release analizando a dimash vpop zain domain name value savar duplex house sale Dhaka duplex house sale grand SMM panel Oase mini soundbar AIRBNB FREE BalenoRS for you page premiere pro inbox mailer