How to implement knn algorithm in python смотреть последние обновления за сегодня на .

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Get my Free NumPy Handbook: 🤍 In this Machine Learning from Scratch Tutorial, we are going to implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm. ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ 🪁 Code faster with Kite: 🤍 * ✅ Write cleaner code with Sourcery: 🤍 * 📓 Notebooks available on Patreon: 🤍 ⭐ Join Our Discord : 🤍 If you enjoyed this video, please subscribe to the channel! The code can be found here: 🤍 You can find me here: Website: 🤍 Twitter: 🤍 GitHub: 🤍 #Python #MachineLearning * This is a sponsored link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏

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Description: In this video, we'll implement K-Nearest Neighbours algorithm using scikit-learn. The K-nearest neighbors (KNN) algorithm is a type of supervised machine learning algorithms. KNN is extremely easy to implement in its most basic form, and yet performs quite complex classification tasks. It is a lazy learning algorithm since it doesn't have a specialized training phase. Rather, it uses all of the data for training while classifying a new data point or instance. KNN is a non-parametric learning algorithm, which means that it doesn't assume anything about the underlying data. This is an extremely useful feature since most of the real world data doesn't really follow any theoretical assumption e.g. linear-separability, uniform distribution, etc. Blog reference - 🤍 About Me - Website: 🤍 GitHub: 🤍 LinkedIn: 🤍

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Python for Data Science: 🤍 This Edureka video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist for more videos: 🤍 Subscribe to our channel to get video updates. Hit the subscribe button above. #KNNAlgorithm #MachineLearningUsingPython #MachineLearningTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍

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This KNN Algorithm in Machine Learning tutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works. You will learn how do we choose the factor 'K', when do we use KNN, and you will also see a use case demo 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 - 00:57 Introduction to KNN(K Nearest Neighbor) 00:57 - 02:33 Why do we need KNN? 02:33 - 03:51 What is KNN? 03:51 - 05:46 How do we choose the factor 'K'? 05:46 - 06:42 When do we use KNN? 06:42 - 09:19 How does the KNN algorithm work? 09:19 - 27:42 Use case - Predict whether a person will have diabetes or not 🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: 🤍 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 What is KNN? K-Nearest Neighbors is one of the simplest supervised machine learning algorithms used for classification. It classifies a data point based on its neighbors’ classifications. It stores all available cases and classifies new cases based on similar features. 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. ✅KNN Algorithm Uses in Real World In the real world, the KNN algorithm has applications for both classification and regression problems. KNN is widely used in almost all industries, such as healthcare, financial services, eCommerce, political campaigns, etc. Healthcare companies use the KNN algorithm to determine if a patient is susceptible to certain diseases and conditions. Financial institutions predict credit card ratings or qualify loan applications and the likelihood of default with the help of the KNN algorithm. To learn more about KNN, check out Machine Learning course: 🤍 Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course 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. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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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 Machine learning tutorial playlist for beginners: 🤍 🌎 Website: 🤍 🎥 Codebasics Hindi channel: 🤍 #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin (Personal): 🤍 📝 Linkedin (Codebasics): 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

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KNN Machine Learning Algorithm Tutorial theory and explanation. KNN implementation in python and parameter tuning. Like what I am doing? Buy me a Coffee to re-energize - 🤍 Dataset link - 🤍 Code file used in this tutorial - 🤍

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In this python machine learning tutorial I go through implementing the K nearest neighbor algorithm. I show how to train and test a KNN model and then how to look at unique data and see the neighbors for individual data points. ⭐ Kite is a free AI-powered coding assistant for Python that will help you code smarter and faster. Integrates with Atom, PyCharm, VS Code, Sublime, Vim, and Spyder. I've been using Kite for 6 months and I love it! 🤍 Text-Based Tutorial & Code: 🤍 WEBSITE: 🤍 proXPN VPN: 🤍 Use the Code "SAVE6144" For 50% Off! One-Time Donations: 🤍 Support the Channel: 🤍 Twitter: 🤍 Join my discord server: 🤍 Please leave a LIKE and SUBSCRIBE for more content! Tags: tech with tim,python tutorials,sklearn python,sklearn tutorial,tensorflow python,tensorflow python tutorial,tensorflow python tutorial for beginners,machine learning with python,python machine learning for beginners,python machine learning tutorial,machine learning,artificial intelligence,machine learning tutorial 2019

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In this tutorial we are going to learn about the theory of K-NN Machine Learning Algorithm and Implement the theory with Python Programming Language. This iTech combo video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. Credit: Andrew Nagi, Killian Weinberger, Sentdex #KNearestNeighbor #CustomProgrammed #MachineLearning

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In this K Nearest Neighbor algorithm in python tutorial I've talked about how the KNN machine learning algorithm work within python using pandas and sklearn libraries. We have seen how to set the value of k in KNN and how to run the KNN algorithm on entire data set as well as train and test dataset. K-Nearest Neighbors, or KNN for short, is one of the simplest machine learning algorithms. KNN is a non-parametric, lazy learning algorithm. KNN Being a lazy learning algorithm implies that there is little to no training phase. Therefore, we can immediately classify new data points as they present themselves. For this tutorial, we’ll be using this dataset - Other python project and tutorials.

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In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to apply a simple example of the algorithm using Scikit-Learn, and then in the subsquent tutorials we'll build our own algorithm to learn more about how it works under the hood. To exemplify classification, we're going to use a Breast Cancer Dataset, which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository. 🤍 🤍 🤍 🤍

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This is the link to my previous video wherein I have given an intuition about the kNN algorithm - 🤍 This video is about implementing kNN in python using the Iris Flower Dataset. We implement the algorithm from scratch using python and also look at how to use sklearn library for the same! The link to the colab file - 🤍 Stay tuned for more such videos!

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In this video, we will show how to implement knn in python using scikit-learn. KNN is one of the most popular machine learning algorithm.To implement this ml algorithm in code, we've used iris data set here. Actually, here we haven't cover how to implement knn in python from scratch.From this video, you'll also get to know some use of numpy and pandas. Career knowledge has a plan of making a series of machinelearning algorithms. Hopefully, this video will be helpfull for you. #knn #numpy #scikit-learn #machine learning #python #ml #python #machinelearning #data #code How to install jupyter notebook: 🤍 Iris dataset download link: 🤍 00:01 Introduction 00:07 New file creation in jupyter notebook 00:59 Iris file load through code 03:55 Splitting data into training and testing set 10:10 K-Neighbor model building 13:14 Generating classification report 14:49 Generating confusion matrix

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How to write python codes to run a KNN classification algorithm using Jupyter notes. #MLWITHTRAINFIRM , #MLWITHMATHEW KD - Trees - 🤍 KNN - 🤍 Distances - 🤍 Ball Trees - 🤍

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#python #ml #pandas #sklearn Github :- 🤍

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Hi guys, In this video, you are going to learn about the k-Nearest Neighbors algorithm. This is a powerful machine learning algorithm. I have explained the algorithm and haven't used any scikit learn library to code it. I showed it from scratch. Hope everyone will find it helpful. Code: 🤍 Buy me a coffee: 🤍 Check my other videos on Deep Learning: 🤍 If you have any queries about this video, please leave a comment below. Please like, comment, share and subscribe! This will keep me motivated to do such kinds of stuff. Find me on: LinkedIn: 🤍 GitHub: 🤍 Website: 🤍 YouTube channel: 🤍 #ML #MachineLearning #DataScience #Job #Python #AI #ML2021 #WhatIsMachineLearning #MachineLearningTutorial #MachineLearningBasics #MachineLearningTutorialForBeginners

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Introduction to kNN: k Nearest Neighbors Classification and Regression in Python using sklearn with 10 fold cross validation Hi there! I'm a Machine Learning PhD student based in Ireland. I am studying case-based reasoning and recommender systems techniques in sports science and marathon running. I mentioned how I actually use kNN a lot for implementing CBR since we can use the nearest neighbors as the cases retrieved from our case-base and use them to find a solution for our new case. This video provides a very basic introduction to k Nearest Neighbors which may not be too useful for people who are very familiar with machine learning. But a lot of people who watch my channel are not. I give examples of kNN classification and regression using sklearn, and I explain and use cross-validation. Next time I will be explaining how feature extraction and rigorous evaluation can be achieved for these types of algorithms. If you are interested, be sure to subscribe! I am posting 1-2 videos per week about ML/RecSys and the rest of my content is more general PhD. Python Notebook available for download here: 🤍 Purchase my PhD Student & Productivity Notion Template: 🤍 Try the Notion template before you buy: 🤍 Join my email list for regular PhD and Productivity advice and a 10% discount on my Products: 🤍 Support My Channel: If you would like my content and want to support my channel and get access to exclusive content, then join the channel membership here, starting from €1.99 a month: 🤍 Connect with Me Instagram: 🤍 Twitter: 🤍 For business enquiries only: phdproductivity🤍gmail.com Shop my Favourites for Working from Home and Productivity: 🤍 Check out my Startup Daysier website: Instagram: 🤍 * Disclaimer * Some of the links are affiliate links meaning that if you make a purchase using the link, I earn a small commission with no extra charge to you. If you do decide to use one of these links then thank you for you support. machine learning,nearest neighbors,k nearest neighbors,knn algorithm,scikit learn,k-nearest neighbors algorithm,knn algorithm example in python,how to implement knn algorithm in python,k-nearest neighbor classification algorithm,machine learning tutorial,machine learning basics,machine learning python,cross validation,knn regression,knn regression python,knn classification python,sklearn,knn sklearn,knn scikit learn,cbr knn,case-based reasoning,cbr, sklearn tutorial, machine learning basics for beginners.

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#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 - 🤍

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In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors. 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. The k-NN algorithm is among the simplest of all machine learning algorithms. Both for classification and regression, a useful technique can be used to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor. References : Jose Portilla Project Implementation. This video is dedicated to him. thank you for serving the community github url: 🤍 You can buy my book on Finance with ML and DL using python from the below link amazon url: 🤍

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This video is a part of my Machine Learning Using Python Playlist - 🤍 ►Click here to subscribe - 🤍 Best Hindi Videos For Learning Programming: ►Learn Python In One Video - 🤍 ►Learn JavaScript in One Video - 🤍 ►Learn PHP In One Video - 🤍 ►Machine Learning Using Python - 🤍 ►Creating & Hosting A Website (Tech Blog) Using Python - 🤍 ►Advanced Python Tutorials - 🤍 ►Object Oriented Programming In Python - 🤍 ►Python Data Science and Big Data Tutorials - 🤍 Follow Me On Social Media ►Website (created using Flask) - 🤍 ►Facebook - 🤍 ►Instagram - 🤍 ►Personal Facebook A/c - 🤍 Twitter - 🤍

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Here is a python implementation of KNN, make sure to leave your questions down below! CSV File: 🤍 Try my FAVORITE coding resource: 🤍?tap_a=5644-dce66f&tap_s=1065405-0291e3&utm_medium=affiliate&utm_source=noamyakar Consider... Subscribing: 🤍 i would love to hear any suggestions you have, leave them down in the comments!! (video ideas, editing tips, anything!!)

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In this video, I've constructed a KNN model without the use of sklearn ml library. For this, the dataset included is the diabetes dataset-where in the target variable denotes whether the person will have diabetes or not. Link to the dataset used : 🤍

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IN THIS VIDEOK NEAREST NEIGHBOR KNN ALGORITHM IS EXPLAINED VERY CLEARLY IN THIS SESSION HOW KNN WORKS MOST IMPORTANT HOW TO CHOOSE K VALUE IN KNN ASWELLAS IMPLEMENTATION KNN IN PYTHON IN THIS MACHINE LEARNING SERIES EVERY CHAPTER IS COVER UP WITH CLEAR EXPLANATION OF EVERY HOOK AND NOOK OF THE TOPIC PCA PRINCIPAL COMPONENT ANALYSIS EASY EXPLANATION PART 1 🤍 PCA PART 2 🤍 PLEASE LIKE SHARE SUBSCRIBE AND PRESS BELL ICON

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This is a KNN classifier Bangla tutorial. Here I will show you how to analyse a numeric data using KNN. This will help to how build a data mining project easily. You can also learn the implementation and skicit or sklearn libary in this tutorial. This is a basic KNN example which can be used for other dataset as well. Jupyter notebook is used in this platform but you can do it using google colab too. This KNN python code will give you a basic idea on how to implement knn on python. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. #KNN #K_nearest_neighbor #data_mining

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Leave a comment if you'd like to see more of this! In part 5 of this KNN (K Nearest Neighbor) tutorial series, we finally train and test our machine learning algorithm, and we even accidentally get a 100% training accuracy. This tutorial imports KNN from the sklearn Python library which makes it super easy to implement different versions of k nearest neighbor and other algorithms. To find more videos on this KNN algorithm check out my website below, it also has tutorials on different machine learning/computer vision algorithms. Website: 🤍 Discord: 🤍 Reddit: 🤍 Twitter: 🤍

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This video show the implementation of KNN Classifier in Python.

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Implementation of a Use Case for KNN Classification Algorithm If you like this video, please do like, share, comment and don't forget to subscribe. #Positive_Learning #Nearest_Neighbours #KNN #KNN_Classifier #Algorithms #KNN_Algorithms #Classification #Iris_Dataset #Beginner_Project #Machine_Learning #Matpotlib #SKlearn #Python

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Welcome back to another machine learning in python tutorial! This video is on implementing the KNN model, we've already understood how KNN works, we've already loaded in the data, time to actually use the model! You're also not only going to be getting the accuracy of our model, you're also gonna know how to tweak the model with a few parameters. So yeah, hope you enjoy! Scikit-learn documentation: 🤍 Code: 🤍 🎥 Last Video(Loading & Cleaning Data): 🤍 🔗 Social Media: Instagram: 🤍 Github: 🤍 Twitter: 🤍 Facebook: 🤍

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Hi Guys, In this video, we will see how to implement the KNN algorithm with python and scikit learn. We will also see how to find the right k value using the Elbow method. All the code and data used in this video are available on our git repo below. 🤍 Thank you, Regards, TechCore Easy

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In this video, we will see how KNN can be implemented with Python's Scikit-Learn library. K Nearest Neighbor or KNN solves both classification and regression problem and in this K Nearest Neighbor case study or project we have looked at how you can use the KNN classification to classify the values in categories. This video is a part of Introduction to Classification series (Episode-3) Want to see Episode-1 and Episode-2 !! here's the links: Classification in Machine learning (Episode-1) : 🤍 KNN algorithm in Machine learning (Episode-2): 🤍 Contact us: 🌟Subscribe now🌟: 🤍 Follow us on Instagram📱: 🤍 Connect us on Linkedin📱: 🤍 Email us 💌 : aijvrae🤍gmail.com Do subscribe to our channel and hit the bell icon to never miss an update from us in the future. Feel free to leave your doubts or suggestions regarding this topic in the comments below. And support us by subscribing our channel👆👆👆 Leave us a thumbs up 👍👍 #AIJRVAE #KNNalgorithm #python

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Now that we understand the intuition behind how we calculate the distance/proximity between feature sets, we're ready to begin building our own version of K Nearest Neighbors in code from scatch. 🤍 🤍 🤍 🤍

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In this tutorial, you will learn, how to do Instance based learning and K-Nearest Neighbor Classification using Scikit-learn and pandas in python using jupyter notebook. K-Nearest Neighbor Classification is a supervised classification method. This is the 25th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets. Data Sets: 🤍

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In this machine learning python tutorial I will be introducing K-Nearest Neighbors Algorithm. This is a classification algorithm that attempts to classify data points based upon its closest "neighbors". ⭐ Kite is a free AI-powered coding assistant for Python that will help you code smarter and faster. Integrates with Atom, PyCharm, VS Code, Sublime, Vim, and Spyder. I've been using Kite for 6 months and I love it! 🤍 Text-Based Tutorial & Code: 🤍 Data Set Available Above ^ WEBSITE: 🤍 proXPN VPN: 🤍 Use the Code "SAVE6144" For 50% Off! One-Time Donations: 🤍 Support the Channel: 🤍 Twitter: 🤍 Join my discord server: 🤍 Please leave a LIKE and SUBSCRIBE for more content! Tags: - Tech With Tim - Python Tutorials - Machine learning tutorial - Python machine learning - Python machine learning tutorial - K-Nearest Neighbors

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In this video we will see how the K Nearest Neighbors (KNN) Machine Learning algorithm work. Then we will implement KNN algorithm in python using sklearn library. We will build and KNN model. Then use this model to prediction. We will go over techniques of finding the optimal K value, evaluating the model and improving accuracy of predictions. This KNN tutorial will help you understand what is KNN, why do we need to use KNN, and how KNN algorithm works. You will also see a use case demo to predict whether a user will make a purchase or not using the KNN algorithm. To download data (data is under data folder) and Notebook go to: 🤍 Click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file. Link to Exploratory Data Analysis with Python video: 🤍 Link to Multiple Linear Regression video with Label Encoder example: 🤍 Confusion Matrix Link (Tabular summary of predictions made by a classifier): 🤍 #Python #Coding #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #KNN #sklearntutorials #scikitlearntutorials #K Nearest Neighbors Topics covered in this KNN Machine Learning Video: 00:00 - 00:40 Introduction to KNN(K Nearest Neighbors) 00:40 - 01:00 What is KNN? 01:01 - 02:04 How does the KNN algorithm work? 02:05 - 02:49 Implement KNN in Python (Jupyter Notebook) 02:50 - 03:26 Import dataset 03:27 - 04:18 Label Encoding 04:19 - 04:40 Split data into train and test 04:41 - 04:40 Create and Train Model 05:36 - 06:05 Use case - Predict whether a person a purchase or not 06:06 - 06:56 Find optimal value of K 06:59 - 08:59 Evaluate the model 09:00 - 09:15 Looking Forward Subscribe: 🤍 Github: 🤍 Instagram: 🤍

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In this video, we will show you how to use Python’s Scikit-Learn package to implement the K-Nearest Neighbors (KNN) algorithm. The KNN model is a supervised machine learning algorithm used for classification tasks. - For more great tutorials on machine learning, AI and Python, click SUBSCRIBE!

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In this video, I have implemented KNN(k-nearest neighbors) in python from scratch. The Github link to code - 🤍

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00:40:50

06.06.2020

Implementing KNN algorithm in Python is an essential concept in the world of Digitalisation. Know more about it in this video. Get access to all our fun and exciting quizzes, discussion forums, and study materials designed specially by IIT-Delhi students by visiting the Intellify website today! Link to our website - 🤍 About Intellify: Intellify is the education initiative by IIT-Delhi students and alumni, to provide students with the platform to learn, grow, and develop their inner potential. In its journey, Intellify has worked with Delhi government, NITI Aayog, MHRD (DIC), CBSE, Teach for India, CCL-IIT Gandhinagar, and Design Department of IIT Delhi to support 60,000+ students and more than 1500 teachers across 150+ cities. We’ll be happy to cater to your feedback! Feel free to drop a mail to us, in case you’d like to share a thought with us, or are interested to work with us. Email Address: info🤍intellify.in Check out Intellify’s social media handles: Instagram: 🤍 Facebook: 🤍 For many more educational videos, hit the subscribe button and keep Learning with Intellify! #intellify #cbse #ncertsolutions #learnwithintellify #ncert

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00:24:50

08.12.2020

K - Nearest Neighbor Image Classification Example with Euclidean Distance in Python

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00:10:14

09.07.2020

This video is about the knn (K -Nearest neighbour) Algorithm. I discuss the following topics in the video: 1)Intuition behind knn. 2)Maths and formulae used(Euclidean distance) 3)Hamming distance for string variables 3)Coding and implementation of knn in python using sklean library

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how to implement knn algorithm in python
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