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#MachineLearning #DataScience #KNN Machine Learning Basics: Bitesize machine learning concept about K Nearest Neighbors algorithm! Instagram: 🤍
Machine learning and Data Mining sure sound like complicated things, but that isn't always the case. Here we talk about the surprisingly simple and surprisingly effective K-nearest neighbors algorithm. 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 0:21 K-NN overview 0:44 K-NN applied to scatterplot data 2:44 K-NN applied to a heatmap 4:12 Thoughts on how to pick 'K' #statquest #KNN #ML
"🔥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 ""KNN Algorithm in Machine Learning"" tutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works using Python. You will learn how do we choose the factor 'K', when do we use KNN, with proper hands on demonstration to predict whether a person will have diabetes or not, using the KNN algorithm. Below topics are explained in this K-Nearest Neighbor Algorithm (KNN Algorithm) tutorial: 00:00 Introduction to KNN(K Nearest Neighbor) 00:57 Why do we need KNN? 02:33 What is KNN? 03:51 How do we choose the factor 'K'? 05:46 When do we use KNN? 06:42 How does the KNN algorithm work? 09:19 Use case - Predict whether a person will have diabetes or not? Dataset Link - 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 You can also go through the slides here: 🤍 #KNNAlgorithmInMachineLearning #KNNAlgorithm #KNN #KNearestNeighbor #KNNMachineLearning #KNNAlgorithmPython #KNearestNegighborMachineLearning #MachineLearningAlgorithm #MachineLearning #Simplilearn When Do We Use the KNN Algorithm? The KNN algorithm is used in the following scenarios: ✅Data is labeled ✅Data is noise-free ✅Dataset is small, as KNN is a lazy learner Pros and Cons of Using KNN ✅Pros: Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly, which will not impact the accuracy of the algorithm. KNN is very easy to implement. There are only two parameters required to implement KNN—the value of K and the distance function (e.g. Euclidean, Manhattan, etc.) ✅Cons: The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance. Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset. Otherwise, KNN may generate wrong predictions. ➡️ 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: 🤍 🔥🔥 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 - 🤍
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In the first lesson of the Machine Learning from Scratch course, we will learn how to implement the K-Nearest Neighbours algorithm. Being one of the simpler ML algorithms, it is a great way to kick off our deep dive into ML algorithms. You can find the code here: 🤍 Previous lesson: 🤍 Next lesson: 🤍 Welcome to the Machine Learning from Scratch course by AssemblyAI. Thanks to libraries like Scikit-learn we can use most ML algorithms with a couple of lines of code. But knowing how these algorithms work inside is very important. Implementing them hands-on is a great way to achieve this. And mostly, they are easier than you’d think to implement. In this course, we will learn how to implement these 10 algorithms. We will quickly go through how the algorithms work and then implement them in Python using the help of NumPy. ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Are KNN and K-means the same thing? No. KNN is a supervised learning algorithm whereas K-means is a clustering algorithm. #MachineLearning #DeepLearning
In this video we will understand how K nearest neighbors algorithm work. Then write python code using sklearn library to build a knn (K nearest neighbors) model. The end, I have an exercise for you to practice concepts you learnt in this video. Code: 🤍 Exercise: 🤍 ⭐️ Timestamps ⭐️ 00:00 Theory 03:51 Coding 14:09 Exercise Do you want to learn technology from me? Check 🤍 for my affordable video courses. Machine learning tutorial playlist for beginners: 🤍 🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. 🎥 Codebasics Hindi channel: 🤍 #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Dhaval's Personal Instagram: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin (Personal): 🤍 📝 Linkedin (Codebasics): 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.
1. Solved Numerical Example of KNN (K Nearest Neighbor Algorithm) Classifier to classify New Instance IRIS Example by Mahesh Huddar 1. Solved Numerical Example of KNN: 🤍 2. Solved Numerical Example of KNN: 🤍 Tutorials: Machine Learning - 🤍 Big Data Analysis - 🤍 Data Science and Machine Learning - 🤍 Python Tutorial - 🤍
KNN (K — Nearest Neighbors) is one of many (supervised learning) algorithms used in data mining and machine learning, it’s a classifier algorithm where the learning is based on “how similar” is a data (a vector) from other. Implementation: 🤍 🌐For more info : 🤍 #machinelearning #KNearestNeighbours Follow me on Instagram 👉 🤍 Visit my Profile 👉 🤍 Support my work on Patreon 👉 🤍
Об основах машинного обучения расскажет Татьяна Гайнцева — преподаватель Deep Learning School при МФТИ, исследователь в лаборатории LAMBDA (НИУ ВШЭ) и группе Video Intelligence Huawei. Рассматриваем алгоритм k-ближайших соседей и его особенности. Иллюстрируем его примером подбора параметров и обработки признаков для kNN. В рамках стипендиальной программы VK Fellowship мы провели образовательный курс по машинному обучению для преподавателей информатики в школах. Теперь делимся полезными материалами с миром 🤗 Первый блок образовательного курса для преподавателей посвятим машинному обучению. Познакомимся с инструментами, которые пригодятся для занятий, и основными языками. Задания к модулю найдёте по ссылке: 🤍 Больше информации о курсе — в сообществе VK Education ВКонтакте: 🤍
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Let's code the KNN (K Nearest Neighbours) algorithm in Python from scratch! 🎥Detailed Tutorial: 🤍 💻Code with tests: 🤍 Get your Free Token for AssemblyAI Speech-To-Text API 👇🤍 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning #shorts
This Video explains KNN with a very simple example
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. Github link: 🤍 You can buy my book where I have provided a detailed explanation of how we can use Machine Learning, Deep Learning in Finance using python Packt url : 🤍 Amazon url: 🤍
#knn #machinelearning #python In this video, I've explained the concept of KNN algorithm in great detail. I've also shown how you can implement KNN from scratch in python. For more videos please subscribe - 🤍 Support me if you can ❤️ 🤍 🤍 Source code - 🤍 ML algorithms from scratch - 🤍 Facebook page - 🤍
2. Solved Example KNN Classifier to classify New Instance Height and Weight Example by mahesh Huddar In this video, I have discussed how to apply the KNN - k nearest neighbor machine learning algorithm to predict the class label for the new instance give the height and weight dataset. 1. Solved Numerical Example of KNN: 🤍 2. Solved Numerical Example of KNN: 🤍 1. Blog / Website: 🤍 2. Like Facebook Page: 🤍 3. Follow us on Instagram: 🤍 4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
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Solved Example K Nearest Neighbors Algorithm Weighted KNN to classify New Instance by Dr. Mahesh Huddar The following concepts are discussed: K Nearest Neighbors Algorithm, K Nearest Neighbors Algorithm, K Nearest Neighbors Algorithm Solved Example, Weighted K Nearest Neighbors Algorithm, Weighted KNN, Weighted KNN Solved Example, 1. Blog / Website: 🤍 2. Like Facebook Page: 🤍 3. Follow us on Instagram: 🤍 4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
Oi gente, estamos de volta! E nesse primeiro vídeo de 2020, vamos aprender mais um algoritmo de aprendizado de máquina \o/ O algoritmo dos vizinhos mais próximos é bastante conhecido e simples de entender. Logo, é ótimo para recomeçarmos nossos estudos por aqui. Bora estudar? Link do gráfico 3D do dataset Iris com Plot.ly: 🤍 *E-mail: contato🤍programacaodinamica.com.br *Instagram: 🤍dinamicaprogramacao 🤍kizzy_terra 🤍 hallpaz *Twitter: 🤍pgdinamica 🤍kizzyterra 🤍hallpaz * Curta a Programação Dinâmica no facebook: 🤍 *Nosso repositório no Github: 🤍 * Confira o nosso Medium: 🤍 * Confira os artigos no Python Café: 🤍
Quer saber mais sobre o nosso Curso Completo de Ciência de Dados? Clique no link abaixo para garantir sua vaga na próxima turma: 🤍 PARA BAIXAR O MINICURSO GRATUITO DE CIÊNCIA DE DADOS: 🤍 - ► Arquivos Utilizados no Vídeo: 🤍 ► Como sair do ZERO em Ciência de Dados em Apenas UMA AULA 🤍 - Caso prefira o vídeo em formato de texto: 🤍 - Fala Impressionadores! Essa é mais uma aula sobre algoritmos de aprendizado de máquinas e hoje vamos falar sobre o KNN! Essa sigla vem do inglês K-Nearest Neighbors, que traduzindo seria o algoritmo dos vizinhos mais próximos. E é exatamente o que vamos fazer com esse algoritmo, fazer uma classificação de dados com bases nos vizinhos, ou seja, com base nos dados mais próximos. O problema é que esse classificador KNN possui o processamento no momento da previsão, então temos o chamado aprendizado preguiçoso (lazy learning). Ele acaba demorando mais, então não é em todos os casos que vamos utilizar esse algoritmo. Além disso vou falar sobre o RadiusNeighborClassifier, que é a classificação por raio (distância) para classificar por distância ao invés de pegar os pontos. E aí, vamos aprender mais esse algoritmo de aprendizado de máquina? Então vem comigo que eu vou te ensinar! - Hashtag Programação ► Inscreva-se em nosso canal: 🤍 ► Ative as notificações (clica no sininho)! ► Curta o nosso vídeo! - Redes Sociais ► Blog: 🤍 ► YouTube: 🤍 ► Instagram: 🤍 ► Facebook: 🤍 Aqui nos vídeos do canal da Hashtag Programação ensinamos diversas dicas de Python para que você consiga se desenvolver nessa linguagem de programação! - # cienciadedados #hashtagprogramacao
Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - You may be wondering why kNN is called 'k' Nearest Neighbors, what exactly is 'k'? The letter k is a variable that specifies the number of neighbors to consider when making the classification. You can imagine it as determining the size of the neighborhoods. Until now, we've ignored k, and thus R has used the default value of '1'. This means that only the single nearest, most similar, neighbor was used to classify the unlabeled example. While this seems OK on the surface, let's work through an example to see why the value of k may have a substantial impact on the performance of our classifier. Suppose our vehicle observed the sign at the center of the image here. Its five nearest neighbors are depicted. The single nearest neighbor is a speed limit sign, which shares a very similar background color. Unfortunately, in this case, a kNN classifier with k set to one would make an incorrect classification. Slightly further away are the second, third, and fourth nearest neighbors, which are all pedestrian crossing signs. Suppose we set k to three. What would happen? The three nearest neighbors, a speed limit sign and two pedestrian crossing signs, would take a vote. The category with the majority of nearest neighbors, in this case the pedestrian crossing sign, is the winner. Increasing k to five allows the five nearest neighbors to vote. The pedestrian crossing sign still wins with a margin of 3-to-2. Note that in the case of a tie, the winner is typically decided at random. In the previous example, setting k to a higher value resulted in a correct prediction. But it is not always the case that bigger is better. A small k creates very small neighborhoods; the classifier is able to discover very subtle patterns. As this image illustrates, you might imagine it as being able to distinguish between groups even when their boundary is somewhat "fuzzy." On the other hand, sometimes a "fuzzy" boundary is not a true pattern, but rather due to some other factor that adds randomness into the data. This is called noise. Setting k larger, as this image shows, ignores some potentially-noisy points in an effort to discover a broader, more general pattern. So, how should you set k? Unfortunately, there is no universal rule. In practice, the optimal value depends on the complexity of the pattern to be learned, as well as the impact of noisy data. Some suggest a rule of thumb starting with k equal to the square root of the number of observations in the training data. For example, if the car had observed 100 previous road signs, you might set k to 10. An even better approach is to test several different values of k and compare the performance on data it has not seen before. In the next coding exercise, you'll have an opportunity to see the impact of k on the vehicle's ability to correctly classify signs. #R #RTutorial #DataCamp #kNN #Supervised #Classification
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