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Learn more about WatsonX: 🤍 Learn about a data and AI platform that’s built for business → 🤍 What is Artificial Intelligence (AI)? → 🤍 What is Machine Learning? → 🤍 What is really the difference between Artificial intelligence (AI) and machine learning (ML)? Are they actually the same thing? In this video, Jeff Crume explains the differences and relationship between AI & ML, as well as how related topics like Deep Learning (DL) and other types and properties of each. Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #ai #ml #dl #artificialintelligence #machinelearning #deeplearning #watsonx
Learn 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: 🤍
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
Become An AI & ML Expert Today: 🤍 *Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥AI & Machine Learning Bootcamp(US Only): 🤍 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 This Machine Learning basics video will help you understand what Machine Learning is, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into self-learning mode without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. The below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? ( 00:21 ) 2. Types of Machine Learning ( 02:43 ) 2. What is Supervised Learning? ( 02:53 ) 3. What is Unsupervised Learning? ( 03:46 ) 4. What is Reinforcement Learning? ( 04:37 ) 5. Machine Learning applications ( 06:25 ) 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 Caltech Post Graduate Program In AI And Machine Learning Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions. ✅ Key Features - Simplilearn's JobAssist helps you get noticed by top hiring companies - PGP AI & ML completion certificate from Caltech CTME - Masterclasses delivered by distinguished Caltech faculty and IBM experts - Caltech CTME Circle Membership - Earn up to 22 CEUs from Caltech CTME - Online convocation by Caltech CTME Program Director - IBM certificates for IBM courses - Access to hackathons and Ask Me Anything sessions from IBM - 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more - Seamless access to integrated labs - Capstone projects in 3 domains - 8X higher interaction in live online classes by industry experts ✅ Skills Covered - Statistics - Python - Supervised Learning - Unsupervised Learning - Recommendation Systems - NLP - Neural Networks - GANs - Deep Learning - Reinforcement Learning - Speech Recognition - Ensemble Learning - Computer Vision 👉Learn More at: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
Learn about a next-generation enterprise studio for AI builderss → 🤍 Learn more about Machine Learning → 🤍 Learn more about Deep Learning → 🤍 Learn more about Supervised Learning → 🤍 What is Machine Learning and how do businesses leverage it today? How does Machine Learning differ from Artificial Intelligence (AI) and Deep Learning, or are they all the same? In this lightboard video, Luv Aggarwal with IBM Cloud, answers these questions and many more as he visually explains what Machine Learning is, how it compares to AI and Deep Learning, as well as why and how an enterprise would use a Machine Learning solution. Chapters 0:00 - Intro 0:17 - Differences between Machine Learning, AI, and Deep Learning 1:32 - Supervised Learning 4:26 - Unsupervised Learning 6:38 - Reinforcement Learning 7:44 - Summary Get started on IBM Cloud at no cost → 🤍 Subscribe to see more videos like this in the future → 🤍 #MachineLearning #AI #DeepLearning #watsonx
Introduction to Statistical Learning: 🤍 Take my courses at 🤍 Best Courses for Analytics: - + IBM Data Science (Python): 🤍 + Google Analytics (R): 🤍 + SQL Basics: 🤍 Best Courses for Programming: - + Data Science in R: 🤍 + Python for Everybody: 🤍 + Data Structures & Algorithms: 🤍 Best Courses for Machine Learning: - + Math Prerequisites: 🤍 + Machine Learning: 🤍 + Deep Learning: 🤍 + ML Ops: 🤍 Best Courses for Statistics: - + Introduction to Statistics: 🤍 + Statistics with Python: 🤍 + Statistics with R: 🤍 Best Courses for Big Data: - + Google Cloud Data Engineering: 🤍 + AWS Data Science: 🤍 + Big Data Specialization: 🤍 More Courses: - + Tableau: 🤍 + Excel: 🤍 + Computer Vision: 🤍 + Natural Language Processing: 🤍 + IBM Dev Ops: 🤍 + IBM Full Stack Cloud: 🤍 + Object Oriented Programming (Java): 🤍 + TensorFlow Advanced Techniques: 🤍 + TensorFlow Data and Deployment: 🤍 + Generative Adversarial Networks / GANs (PyTorch): 🤍 Become a Member of the Channel! 🤍 Follow me on LinkedIn! 🤍 #machinelearning #datascience #statistics #datascience
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
Learn about WatsonX: 🤍 What is Machine Learning → 🤍 What is Deep Learning → 🤍 Get a unique perspective on what the difference is between Machine Learning and Deep Learning - explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen. Download a free AI ebook → 🤍 Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #AI #Software #ITModernization #DeepLearning #MachineLearning
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
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
In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources. All courses: 🤍 Get your Free Token for AssemblyAI Speech-To-Text API 👇 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning 0:00 Introduction 1:01 MATH 1:58 PYTHON PYTHON 2:37 ML TECH STACK ML TECH STACK 3:35 ML COURSES ML COURSES 4:44 HANDS-ON & DATA PREPARATION 5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO 6:16 SPECIALIZE & CREATE BLOG
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
What are the neurons, why are there layers, and what is the math underlying it? Help fund future projects: 🤍 Written/interactive form of this series: 🤍 Additional funding for this project provided by Amplify Partners Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: 🤍 There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! 🤍 I also highly recommend Chris Olah's blog: 🤍 For more videos, Welch Labs also has some great series on machine learning: 🤍 🤍 For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: 🤍 Lion photo by Kevin Pluck - Timeline: 0:00 - Introduction example 1:07 - Series preview 2:42 - What are neurons? 3:35 - Introducing layers 5:31 - Why layers? 8:38 - Edge detection example 11:34 - Counting weights and biases 12:30 - How learning relates 13:26 - Notation and linear algebra 15:17 - Recap 16:27 - Some final words 17:03 - ReLU vs Sigmoid Correction 14:45 - The final index on the bias vector should be "k" Animations largely made using manim, a scrappy open source python library. 🤍 If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: 🤍 Stream the music on Spotify: 🤍 If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍
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: 🤍
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python. ✏️ Daniel Bourke developed this course. Check out his channel: 🤍 🔗 Code: 🤍 🔗 Ask a question: 🤍 🔗 Course materials online: 🤍 🔗 Full course on Zero to Mastery (20+ hours more video): 🤍 Some sections below have been left out because of the YouTube limit for timestamps. 0:00:00 Introduction 🛠 Chapter 0 – PyTorch Fundamentals 0:01:45 0. Welcome and "what is deep learning?" 0:07:41 1. Why use machine/deep learning? 0:11:15 2. The number one rule of ML 0:16:55 3. Machine learning vs deep learning 0:23:02 4. Anatomy of neural networks 0:32:24 5. Different learning paradigms 0:36:56 6. What can deep learning be used for? 0:43:18 7. What is/why PyTorch? 0:53:33 8. What are tensors? 0:57:52 9. Outline 1:03:56 10. How to (and how not to) approach this course 1:09:05 11. Important resources 1:14:28 12. Getting setup 1:22:08 13. Introduction to tensors 1:35:35 14. Creating tensors 1:54:01 17. Tensor datatypes 2:03:26 18. Tensor attributes (information about tensors) 2:11:50 19. Manipulating tensors 2:17:50 20. Matrix multiplication 2:48:18 23. Finding the min, max, mean & sum 2:57:48 25. Reshaping, viewing and stacking 3:11:31 26. Squeezing, unsqueezing and permuting 3:23:28 27. Selecting data (indexing) 3:33:01 28. PyTorch and NumPy 3:42:10 29. Reproducibility 3:52:58 30. Accessing a GPU 4:04:49 31. Setting up device agnostic code 🗺 Chapter 1 – PyTorch Workflow 4:17:27 33. Introduction to PyTorch Workflow 4:20:14 34. Getting setup 4:27:30 35. Creating a dataset with linear regression 4:37:12 36. Creating training and test sets (the most important concept in ML) 4:53:18 38. Creating our first PyTorch model 5:13:41 40. Discussing important model building classes 5:20:09 41. Checking out the internals of our model 5:30:01 42. Making predictions with our model 5:41:15 43. Training a model with PyTorch (intuition building) 5:49:31 44. Setting up a loss function and optimizer 6:02:24 45. PyTorch training loop intuition 6:40:05 48. Running our training loop epoch by epoch 6:49:31 49. Writing testing loop code 7:15:53 51. Saving/loading a model 7:44:28 54. Putting everything together 🤨 Chapter 2 – Neural Network Classification 8:32:00 60. Introduction to machine learning classification 8:41:42 61. Classification input and outputs 8:50:50 62. Architecture of a classification neural network 9:09:41 64. Turing our data into tensors 9:25:58 66. Coding a neural network for classification data 9:43:55 68. Using torch.nn.Sequential 9:57:13 69. Loss, optimizer and evaluation functions for classification 10:12:05 70. From model logits to prediction probabilities to prediction labels 10:28:13 71. Train and test loops 10:57:55 73. Discussing options to improve a model 11:27:52 76. Creating a straight line dataset 11:46:02 78. Evaluating our model's predictions 11:51:26 79. The missing piece – non-linearity 12:42:32 84. Putting it all together with a multiclass problem 13:24:09 88. Troubleshooting a mutli-class model 😎 Chapter 3 – Computer Vision 14:00:48 92. Introduction to computer vision 14:12:36 93. Computer vision input and outputs 14:22:46 94. What is a convolutional neural network? 14:27:49 95. TorchVision 14:37:10 96. Getting a computer vision dataset 15:01:34 98. Mini-batches 15:08:52 99. Creating DataLoaders 15:52:01 103. Training and testing loops for batched data 16:26:27 105. Running experiments on the GPU 16:30:14 106. Creating a model with non-linear functions 16:42:23 108. Creating a train/test loop 17:13:32 112. Convolutional neural networks (overview) 17:21:57 113. Coding a CNN 17:41:46 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:29:02 118. Training our first CNN 18:44:22 120. Making predictions on random test samples 18:56:01 121. Plotting our best model predictions 19:19:34 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4 – Custom Datasets 19:44:05 126. Introduction to custom datasets 19:59:54 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:59 129. Becoming one with the data 20:39:11 132. Turning images into tensors 21:16:16 136. Creating image DataLoaders 21:25:20 137. Creating a custom dataset class (overview) 21:42:29 139. Writing a custom dataset class from scratch 22:21:50 142. Turning custom datasets into DataLoaders 22:28:50 143. Data augmentation 22:43:14 144. Building a baseline model 23:11:07 147. Getting a summary of our model with torchinfo 23:17:46 148. Creating training and testing loop functions 23:50:59 151. Plotting model 0 loss curves 24:00:02 152. Overfitting and underfitting 24:32:31 155. Plotting model 1 loss curves 24:35:53 156. Plotting all the loss curves 24:46:50 157. Predicting on custom data
In this video, we'll be discussing the steps you can take to get started in machine learning. Whether you're a complete beginner or have some experience with coding and data analysis, this roadmap will give you a clear path forward to begin building your skills. 💻 Register for NVIDIA GTC and attend an event to be entered to win a RTX 4080: 🤍 💻 Enter to win a RTX 4080 or NVIDIA e-learning course discount code: 🤍 💻 Fundamental Machine Learning Algorithms Tutorial: 🤍 💻 Neural Networks Tutorial: 🤍 💻 FreeCodeCamp Machine Learning Tutorial: 🤍 💻 Flappy Bird AI Tutorial: 🤍 💻 Master Blockchain and Web 3.0 development today by using BlockchainExpert: 🤍 - use code "tim" for a discount! 💻 ProgrammingExpert is the best platform to learn how to code and become a software engineer as fast as possible! 🤍 - use code "tim" for a discount! ⭐️ Timestamps ⭐️ 00:00 | Learning ML & AI 01:03 | Machine Learning Steps 03:45 | RTX 4080 Giveaway! 05:26 | Learn Python! 06:31 | Math 07:37 | Python Libraries 08:55 | Fundamentals ML Algorithms 10:48 | Advanced Machine Learning 13:50 | Conclusion ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 👕 Merchandise: 🤍 📸 Instagram: 🤍 📱 Twitter: 🤍 ⭐ Discord: 🤍 📝 LinkedIn: 🤍 🌎 Website: 🤍 📂 GitHub: 🤍 🔊 Podcast: 🤍 🎬 My YouTube Gear: 🤍 💵 One-Time Donations: 🤍 💰 Patreon: 🤍 ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ ⭐️ Tags ⭐️ - Tech With Tim - Machine Learning - AI Roadmap ⭐️ Hashtags ⭐️ #techwithtim #machinelearning #python
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
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
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: 🤍
🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): 🤍 This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video: 00:00 Introduction 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hierarchical Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions -Edureka Machine Learning Training 🔵 Machine Learning Course using Python: 🤍 🔵 Machine Learning Engineer Masters Program: 🤍 🔵Python Masters Program: 🤍 🔵 Python Programming Training: 🤍 🔵 Data Scientist Masters Program: 🤍 PG Diploma in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 🔴 Subscribe to our channel to get latest video updates: 🤍 ⏩ NEW Top 10 Technologies To Learn In 2023 - 🤍 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: 🤍 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: 🤍 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: 🤍 📌𝐌𝐞𝐞𝐭𝐮𝐩: 🤍 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: 🤍 For more information, please write back to us at sales🤍edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free).
This video we walk through a roadmap of how to get started in machine learning and AI. It can seem like a lot at first, but in this video Rob Mulla, kaggle grandmaster, breaks down his suggestions for anyone looking to start in this field. This is a good entry into anyone looking to start a career in data science or machine learning. We break it down into a few steps. This will help you map out your path to becoming a machine learning master including: which programming language to use, what courses to take, and the rest. Timeline: 00:00 Starting your journey 00:46 Picking a programming language 02:00 Math & Statistics 03:10 Data Wrangling 03:44 Learn Algorithms 04:45 Picking a Focus 06:50 Tools 08:06 Learn through Doing (Kaggle) Follow me on twitch for live coding streams: 🤍 My other videos: Speed Up Your Pandas Code: 🤍 Speed up Pandas Code: 🤍 Intro to Pandas video: 🤍 Exploratory Data Analysis Video: 🤍 Working with Audio data in Python: 🤍 Efficient Pandas Dataframes: 🤍 * Youtube: 🤍 * Discord: 🤍 * Twitch: 🤍 * Twitter: 🤍 * Kaggle: 🤍 #machinelearning #datascience #python
Welcome to this core Machine Learning course for beginners! This course is designed to help you build a solid foundation in machine learning, focusing on core concepts and in-depth explanations of regression analysis, which is often overlooked in other courses. Whether you're a beginner or someone looking to strengthen your understanding of machine learning, this course is for you! ✏️ Course created by 🤍AyushSinghSh 🔗 Course information: 🤍 🔗 Lecture notes and resources: 🤍 ⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:03:42) ML Foundation ⌨️ (1:04:47) Regression Foundation ⌨️ (2:51:53) Regression intermediate ⌨️ (3:48:48) MLR Intermediate ⌨️ (4:52:33) Regression Advance ⌨️ (6:31:04) Regression Project 1 ⌨️ (7:04:15) Regression Project 2 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
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 🤍
Become An AI & ML Expert Today: 🤍 *Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥AI & Machine Learning Bootcamp(US Only): 🤍 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. ✅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: 🤍 🔥Free Machine Learning Course: 🤍 ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥AI & Machine Learning Bootcamp(US Only): 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
Machine Learning Specialization from Coursera 👉🏼 🤍 Books for Data Nerds 👇🏼 📕 Machine Learning (Python) 👉🏼 🤍 📘 Machine Learning (Concepts) 👉🏼 🤍 📗 Data Science Must Read 👉🏼 🤍 📚 Books I’ve read 👉🏼 🤍 Certificates & Courses Coursera Courses: 📜 Google Data Analytics Certificate (START HERE) 👉🏼 🤍 💿 SQL for Data Science 👉🏼 🤍 🧾 Excel Skills for Business 👉🏼 🤍 🐍 Python for Everybody 👉🏼 🤍 📊 Data Visualization with Tableau 👉🏼 🤍 🏴☠️ Data Science: Foundations using R 👉🏼 🤍 Coursera Plus Subscription (7-day free trial) 👉🏼 🤍 👨🏼🏫 All courses 👉🏼 🤍 Tech for Data Nerds ⚙️ Tech I use 👉🏼 🤍 🪟Windows on a Mac (Parallels VM) 👉🏼 🤍 👨🏼💻 M1 Macbook Air (Mac of choice) 👉🏼 🤍 💻 Dell XPS 13 (PC of choice) 👉🏼 🤍 💻 Asus Vivo Book (Lowest Cost PC) 👉🏼 🤍 💻Lenovo IdeaPad (Best Value PC)👉🏼 🤍 Build a Portfolio Online 👩🏻💻Build portfolio here 👉🏼 🤍 Rebate Code: "LUKE" My Portfolio 👉🏼 🤍 Social Media / Contact Me 🙋🏼♂️Newsletter: 🤍 🌄 Instagram: 🤍 ⏰ TikTok: 🤍 📘 Facebook: 🤍 📥 Business Inquiries: luke🤍lukebarousse.com As a member of the Amazon, Coursera, Hostinger, Parallels, Interview Query, and Data Camp Affiliate Programs, I earn a commission from qualifying purchases on the links above. It costs you nothing but helps me with content creation. #datanerd #dataanalyst #datascience
Get notified of the free Python course on the home page at 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 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! #machinelearning #python #datascience
Become An AI & ML Expert Today: 🤍 *Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥AI & Machine Learning Bootcamp(US Only): 🤍 In this video on Machine Learning with Python full course, you will understand the basics of machine learning and Python. In this Machine Learning tutorial for beginners, we will cover essential machine learning topics like applications of machine learning and machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, and PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap. Below are the topics covered in this video: 00:00:00 Machine Learning With Python Full Course 2023 00:08:36 Introduction to Machine Learning 00:16:14 Top 10 Applications of Machine Learning 00:32:38 Types of Machine Learning 00:37:46 Machine Learning Algorithms 00:38:14 Linear Regression 00:46:52 Decision Tree 01:23:25 Clustering 01:26:11 K-Means Clustering 02:18:03 Data and its types 03:29:22 Probability 04:07:53 Multiple Linear Regression 04:45:55 Confusion Matrices 05:59:54 KNN 06:23:40 Support Vector Machine 07:14:40 Principle Component Analysis(PCA) 07:53:01 Corona Virus Analysis 🔥Free Machine Learning Course With Completion Certificate: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #MachineLearningCourse #MachineLearningFullCourse #MachineLearningWithPython #MachineLearningWithPythonFullCourse #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #MachineLearningTraining #Simplilearn Dataset Link -🤍 ➡️ About Caltech Post Graduate Program In AI And Machine Learning Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions. ✅ Key Features - Simplilearn's JobAssist helps you get noticed by top hiring companies - PGP AI & ML completion certificate from Caltech CTME - Masterclasses delivered by distinguished Caltech faculty and IBM experts - Caltech CTME Circle Membership - Earn up to 22 CEUs from Caltech CTME - Online convocation by Caltech CTME Program Director - IBM certificates for IBM courses - Access to hackathons and Ask Me Anything sessions from IBM - 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more - Seamless access to integrated labs - Capstone projects in 3 domains - 8X higher interaction in live online classes by industry experts ✅ Skills Covered - Statistics - Python - Supervised Learning - Unsupervised Learning - Recommendation Systems - NLP - Neural Networks - GANs - Deep Learning - Reinforcement Learning - Speech Recognition - Ensemble Learning - Computer Vision 👉 Learn More At: 🤍 Get the Simplilearn app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 : 🤍 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners and professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Roadmap course: 00:00:00 Introduction 00:01:08 Agenda 00:02:45 What is Machine learning? 00:06:28 Supervised Machine Learning 00:11:49 Un-Supervised Machine Learning 00:16:03 Reinforcement Machine Learning 00:32:21 How to Become a Machine Learning Engineer? 00:41:53 Machine Learning Algorithm 01:03:46 Linear Regression Algorithm 01:06:40 What is Linear Regression 01:11:13 Linear Regression Use Cases 01:12:24 Use Case- How to Implement Linear Regression using Python 01:30:22 Logistic Regression Algorithm 01:35:44 Logistic Regression Use cases 02:17:36 Linear Regression Vs Logistic Regression 02:21:05 Decision Tree Algorithm 02:25:53 Types of Classification 02:34:57 What is Decision Tree? 02:58:25 What is Pruning? 02:58:36 Hands-on 03:06:42 Random Forest 03:10:46 Working of Random Forest 03:17:45 Splitting Methods 03:20:32 Advantages & Disadvantages of Random Forest 03:23:52 Hands-on Random Forest 03:35:18 KNN Algorithm 03:37:39 Features of the KNN Algorithm 03:45:54 How KNN works 03:51:21 Hands-on KNN Algorithm 04:07:45 Naive Bayes Classifier 04:29:25 Support Vector Machine 04:31:13 How do SVM work 04:55:00 K- Means Clustering Algorithm 04:58:26 K Means Clustering 05:07:16 Agglomerative Clustering 05:09:16 Division Clustering 05:09:41 Mean shift Clustering 05:18:21 Hierarchical Clustering 05:25:10 How Agglomerative Clustering Works 05:32:59 Applications of Hierarchical Clustering 05:38:34 Apriori Algorithm Explained 05:52:58 Demo 06:30:26 Linear Algebra Application 06:54:00 Probability 07:07:01 Statistics 07:12:47 Types of Statistics 07:38:40 How to select the correct predictive modeling techniques 07:50:54 ML Model Deployment with Flask on Heroku 08:28:32 Azure Machine Learning 08:54:49 AWS Machine Learning 09:35:24 Machine learning Engineer Skills 09:43:30 Machine Learning Engineer Job Trend, Salary & Resume 09:59:20 Top Machine Learning Tools & Frameworks 10:09:12 ML Roadmap 10:22:20 ML Interview Question & Answers 🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 DevOps Online Training: 🤍 🌕 AWS Online Training: 🤍 🔵 React Online Training: 🤍 🌕 Tableau Online Training: 🤍 🔵 Power BI Online Training: 🤍 🌕 Selenium Online Training: 🤍 🔵 PMP Online Training: 🤍 🌕 Salesforce Online Training: 🤍 🔵 Cybersecurity Online Training: 🤍 🌕 Java Online Training: 🤍 🔵 Big Data Online Training: 🤍 🌕 RPA Online Training: 🤍 🔵 Python Online Training: 🤍 🌕 Azure Online Training: 🤍 🔵 GCP Online Training: 🤍 🌕 Microservices Online Training: 🤍 🔵 Data Science Online Training: 🤍 🌕 CEHv12 Online Training: 🤍 🔵 Angular Online Training: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: 🤍 🌕 Cloud Architect Masters Program: 🤍 🔵 Data Scientist Masters Program: 🤍 🌕 Big Data Architect Masters Program: 🤍 🔵 Machine Learning Engineer Masters Program: 🤍 🌕 Business Intelligence Masters Program: 🤍 🔵 Python Developer Masters Program: 🤍 🌕 RPA Developer Masters Program: 🤍 🔵 Web Development Masters Program: 🤍 🌕 Computer Science Bootcamp Program : 🤍 🔵 Cyber Security Masters Program: 🤍 🌕 Full Stack Developer Masters Program : 🤍 🔵 Automation Testing Engineer Masters Program : 🤍 🌕 Python Developer Masters Program : 🤍 🔵 Azure Cloud Engineer Masters Program: 🤍 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🌕 Professional Certificate Program in DevOps with Purdue University: 🤍 🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: 🤍 🌕 Artificial and Machine Learning PGD with E&ICT Academy NIT Warangal: 🤍 Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
AI Teaches Itself to Walk! In this video an AI named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time. Check the pinned comment for more information on how the AI was trained! Current Subscribers: 135,027
All the materials are available in the below link 🤍 Visit 🤍 for data sscience blogs Time Stamp: 00:00:00 Introduction 00:01:25 AI Vs ML vs DL vs Data Science 00:07:56 Machine LEarning and Deep Learning 00:09:05 Regression And Classification 00:18:14 Linear Regression Algorithm 01:07:14 Ridge And Lasso Regression Algorithms 01:33:08 Logistic Regression Algorithm 02:13:52 Linear Regression Practical Implementation 02:28:30 Ridge And Lasso Regression Practical Implementation 02:54:21 Naive Baye's Algorithms 03:16:02 KNN Algorithm Intuition 03:23:47 Decision Tree Classification Algorithms 03:57:05 Decision Tree Regression Algorithms 04:02:57 Practical Implementation Of Deicsion Tree Classifier 04:09:14 Ensemble Bagging And Bossting Techniques 04:21:29 Random Forest Classifier And Regressor 04:29:58 Boosting, Adaboost Machine Learning Algorithms 04:47:30 K Means Clustering Algorithm 05:01:54 Hierarichal Clustering Algorithms 05:11:28 Silhoutte Clustering- Validating Clusters 05:17:46 Dbscan Clustering Algorithms 05:25:57 Clustering Practical Examples 05:35:51 Bias And Variance Algorithms 05:43:44 Xgboost Classifier Algorithms 06:00:00 Xgboost Regressor Algorithms 06:19:04 SVM Algorithm Machine LEarning Algorithm - ►Data Science Projects: 🤍 ►Learn In One Tutorials Statistics in 6 hours: 🤍 Machine Learning In 6 Hours: 🤍 Deep Learning 5 hours : 🤍 ►Learn In a Week Playlist Statistics:🤍 Machine Learning : 🤍 Deep Learning:🤍 NLP : 🤍 ►Detailed Playlist: Stats For Data Science In Hindi : 🤍 Machine Learning In English : 🤍 Machine Learning In Hindi : 🤍 Complete Deep Learning: 🤍
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*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥AI & Machine Learning Bootcamp(US Only): 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression, K Means Clustering, Decision Tree, and Support Vector. Dataset Link - 🤍 Below topics are explained in this Machine Learning course for beginners: 0:00 Table of contents 01:46 Basics of Machine Learning 09:18 Why Machine Learning 13:25 What is Machine Learning 18:32 Types of Machine Learning 18:44 Supervised Learning 21:06 Reinforcement Learning 22:26 Supervised VS Unsupervised 23:38 Linear Regression 25:08 Introduction to Machine Learning 26:40 Application of Linear Regression 27:19 Understanding Linear Regression 28:00 Regression Equation 35:57 Multiple Linear Regression 55:45 Logistic Regression 56:04 What is Logistic Regression 59:35 What is Linear Regression 01:05:28 Comparing Linear & Logistic Regression 01:26:20 What is K-Means Clustering 01:38:00 How does K-Means Clustering work 02:15:15 What is Decision Tree 02:25:15 How does Decision Tree work 02:39:56 Random Forest Tutorial 02:41:52 Why Random Forest 02:43:21 What is Random Forest 02:52:02 How does Decision Tree work- 03:22:02 K-Nearest Neighbors Algorithm Tutorial 03:24:11 Why KNN 03:24:24 What is KNN 03:25:38 How do we choose 'K' 03:27:37 When do we use KNN 03:48:31 Applications of Support Vector Machine 03:48:55 Why Support Vector Machine 03:50:34 What Support Vector Machine 03:54:54 Advantages of Support Vector Machine 04:13:06 What is Naive Bayes 04:17:45 Where is Naive Bayes used 04:54:48 Top 10 Application of Machine Learning Subscribe to our channel for more Machine Learning Tutorials: 🤍 #MachineLearning #CompleteMachineLearningCourse #MachineLearningForBeginners #MachineLearningTutorial #MachineLearningWithPython #LearnMachineLearning #MachineLearingBasics #MachineLearningAlgorithms #MachineLearningEngineer #MachineLearningEngineerSalary #MachineLearningEngineerSkills #SimplilearnMachineLearning #MachineLearningCourse ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… Learn more at: 🤍 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍
*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥AI & Machine Learning Bootcamp(US Only): 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 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. 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 - 🤍 Subscribe to our channel for more 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: 🤍 Learn more at: 🤍 #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI, such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 👉 Learn More At: 🤍 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
Get notified of the free Python course on the home page at 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 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! #machinelearning #python #datascience
in this 2 part series Andrew Ng explains how he would learn machine learning Follow me on tiktok: 🤍 My instagram: 🤍 #lexfridman #lexfridmanpodcast #datascience #machinelearning #deeplearning #study
Free monthly learning resources and insights 🤍 Here are the links to the machine learning resources mentioned: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 That's 16 in total! Learn Data Science (affiliate) 🎓 Data Quest - 🤍 Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas - 🤍 🎓 Complete Python Programmer Bootcamp - 🤍 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition 🤍 📘 Automate the Boring Stuff with Python 🤍 📙 Python Basics - A Practical Introduction to Python 3 🤍 📕 Python Programming An Introduction to Computer Science 🤍 📗 Invent Your Own Computer Games with Python 🤍 🆓 Free Python Resource 🤍 (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - 🤍 🎧 Sony Noise Cancelling Headphones - 🤍 📱 Social Media 🤍 🤍 👌 SUBSCRIBE to ME!👌 🤍 #learnmachinelearning #machinelearning #learnpython
Get notified of the free Python course on the home page at 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 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! #machinelearning #python #datascience
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