In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 193038 Siraj Raval
A short Python tutorial for KNN, by Sooan Han (Creative Technology Management, Underwood International College, Yonsei University, South Korea)
Views: 824 Kee Heon Lee
Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository. You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default. Music - Daft Punk - Instant Crush ft. Julian Casblancas
Views: 16517 NISHANT KAUSHIK 14BCE0398
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 399781 APMonitor.com
While creating a machine learning model, very basic step is to import a dataset, which is being done using python Dataset downloaded from www.kaggle.com
Views: 31554 4am Code
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. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 121277 sentdex
The Wolfram Data Repository is a system for publishing data and making it available in the Wolfram Language for immediate computation. This presentation explains the motivation behind the repository, describes its components and provides examples of how to use it. Notebook Link: https://wolfr.am/s5qdNcAJ
Views: 445 Wolfram
In this video I have implemented LIBSVM which is a library for SVM (Support Vector Machine) on a dataset. It is performed on cancer dataset which I have taken from UCI repository. After tuning of parameters I found best parameters for accuracy and then fit it in the model.
Views: 355 Deep insight of AI
International Open Data Day is a global celebration of data openness, aiming to increase awareness and suggest potential uses of the powerful open data sets. It is celebrated all around the world and members of out Data Science team, Esena and Nermin, attended civic innovation weekend “CodeAcross & OpenDataDay” and won the hackathon! The aim was to create an app through which citizens can use available data published by national authorities. Esena and Nermin developed an app for students, where they can find out about demand in the labor markets for certain areas of studies.
Views: 591 Atlantbh
Machine Learning In Julia With Decision Tree In this lesson, we discussed how to use DecisionTree.jl in Julia for predictive analysis and machine learning. Packages Used Pkg.add("DecisionTree") Pkg.add("DataFrame") Pkg.add("Gadfly") Optional Pkg.add("GraphViz") For Flow chart -like Decision Tree Data Used: https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/ Github: https://goo.gl/FQNvuA If You have any question,comment or suggestion please don't forget to share. Stay Blessed J-Secur1ty JCharisTech @ Jesus Saves
Views: 454 J-Secur1ty
Kaggle Kernel : https://www.kaggle.com/adepvenugopal/predicting-heart-disease-using-ml-xgboost #kaggle #data #python #science #machine #learning
Views: 207 Naman Adep
In this video, I introduce the UCI Machine Learning Repository's wine quality dataset . I talk about the tasks that we will do with this dataset. I walk you through downloading the datasets, storing them and launching your Jupyter Notebook in your project folder. Note: You may have to adjust the visual on the gear in the right lower corner of the video by clicking on the gear and adjusting it HD (1080) resolution.
Views: 49 Adam Morris
Important: (1) Where should I upload source data to https://try.jupyter.org (2) How can I find the source data in https://try.jupyter.org The right location to upload data to and read data from in Jupyter import pandas as pd showcase
Views: 8970 The Data Science Show
In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. Let's consider that we're multi-billionaires, or multi-millionaires, but it's more fun to be billionaires, and we're trying to diversify our portfolio as much as possible. We want to have all types of asset classes, so we've got stocks, bonds, maybe a money market account, and now we're looking to get into real estate to be solid. You've all seen the commercials right? You buy a CD for $60, attend some $500 seminar, and you're set to start making your 6 figure at a time investments into property, right? Okay, maybe not, but we definitely want to do some research and have some sort of strategy for buying real estate. So, what governs the prices of homes, and do we need to do the research to find this out? Generally, no, you don't really need to do that digging, we know the factors. The factors for home prices are governed by: The economy, interest rates, and demographics. These are the three major influences in general for real estate value. Now, of course, if you're buying land, various other things matter, how level is it, are we going to need to do some work to the land before we can actually lay foundation, how is drainage etc. If there is a house, then we have even more factors, like the roof, windows, heating/AC, floors, foundation, and so on. We can begin to consider these factors later, but first we'll start at the macro level. You will see how quickly our data sets inflate here as it is, it'll blow up fast. So, our first step is to just collect the data. Quandl still represents a great place to start, but this time let's automate the data grabbing. We're going to pull housing data for the 50 states first, but then we stand to try to gather other data as well. We definitely dont want to be manually pulling this data. First, if you do not already have an account, you need to get one. This will give you an API key and unlimited API requests to the free data, which is awesome. Once you create an account, go to your account / me, whatever they are calling it at the time, and then find the section marked API key. That's your key, which you will need. Next, we want to grab the Quandl module. We really don't need the module to make requests at all, but it's a very small module, and the size is worth the slight ease it gives us, so might as well. Open up your terminal/cmd.exe and do pip install quandl (again, remember to specify the full path to pip if pip is not recognized). Next, we're ready to rumble, open up a new editor. http://pythonprogramming.net https://twitter.com/sentdex
Views: 106049 sentdex
Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 41624 Bharatendra Rai
Learn how to create a neural network with Keras and data from a smartphones accelerometer and gyroscope in order to determine what activity the user is engaging in. ► Subscribe To My New Artificial Intelligence Newsletter! https://goo.gl/qz1xeZ Code: https://github.com/jg-fisher/phoneMovementNeuralNetwork Dataset: https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones (you will find training and test data within a folder in the after unzipping) Keras Docs: https://keras.io/ -- Highly recommended for theoretical and applied ML -- Deep Learning: https://amzn.to/2LomU4y Hands on Machine Learning: https://amzn.to/2JSxhIv Hope you guys enjoyed this video! Be sure to leave any comments or questions below, thumbs up and subscribe for more neural networks and machine learning!
Views: 2355 John G. Fisher
Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 79048 Google Developers
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17775 Microsoft Research
Full Python + Pandas + Sentiment analysis Playlist: http://www.youtube.com/watch?v=0ySdEYUONz0&list=PLQVvvaa0QuDdktuSQRsofoGxC2PTSdsi7&feature=share In this video, we learn how to access specific data from our dataset. This series uses python with Pandas for data analysis. Our data set will be a database dump from Sentdex.com sentiment analysis, containing about 600 stocks, mostly S&P 500 stocks. Pandas is used to work with our data quickly and efficiently. The ideas of Pandas is to act as a sort of framework for quickly analyzing data and modeling it. Sentiment Analysis data: http://sentdex.com/downloads/stocks_sentdex.csv.gz Python Module downloads: (Get all of the listed dependencies, or at least the major ones like NumPy, Dateutils, Matplotlib, ) http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas https://www.python.org/downloads/ http://matplotlib.org/downloads.html http://www.numpy.org/ Matplotlib Styles video: https://www.youtube.com/watch?v=WmhdQdx8Gjo http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 5240 sentdex
Building a Naive Bayes Text Classifier with scikit-learn [EuroPython 2018 - Talk - 2018-07-26 - PyCharm [PyData]] [Edinburgh, UK] By Obiamaka Agbaneje Machine learning algorithms used in the classification of text are Support Vector Machines, k Nearest Neighbors but the most popular algorithm to implement is Naive Bayes because of its simplicity based on Bayes Theorem. The Naive Bayes classifier is able to memorise the relationships between the training attributes and the outcome and predicts by multiplying the conditional probabilities of the attributes with the assumption that they are independent of the outcome. It is popularly used in classifying data sets that have a large number of features that are sparse or nearly independent such as text documents. In this talk, I will describe how to build a model using the Naive Bayes algorithm with the scikit-learn library using the spam/ham youtube comment dataset from the UCI repository. Preprocessing techniques such as Text normalisation and Feature extraction will be also be discussed. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
Views: 243 EuroPython Conference
In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 206837 Influxity
In this lesson, we start with a quick prediction using a classifier that predicts if someone make more or less than $50K annually. This classifier uses a dataset from UCI known as Adult Census. We start with an introduction to scikit-learn then we go through the three main types of predictive algorithms: classification, regression and clustering. Then we discuss some public sources Data Sets. Then we explain how we build the predictive model that we showed int he beginning of the video. It is built using a kNN (Known Nearest Neighbors) Classifier. We discuss also how to do a simple grid search for fine tune your algorithm parameters. Links used in the video: Amazon AWS: http://goo.gl/RIeSjK/ Roshan Project: http://goo.gl/oFmMc1/
Views: 7699 Roshan
Hello and welcome to part 9 of the Python for Finance tutorial series. In the previous tutorials, we've covered how to pull in stock pricing data for a large number of companies, how to combine that data into one large dataset, and how to visually represent at least one relationship between all of the companies. Now, we're going to try to take this data and do some machine learning with it! https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 37157 sentdex
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Support vector machines http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 46347 WekaMOOC
** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. 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 Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 48046 edureka!
Heart disease prediction system in python using Support vector machine and PCA. Data collect from:https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Please let me know your valuable feedback on the video by means of comments. Please like and share the video. Do not forget to subscribe to my channel for more educational videos. Any type of problem you can comment down. Want more education? Connect with me here: Twitter:https://twitter.com/Noorkhokhar10 Github:https://github.com/noorkhokhar99 Google:https://plus.google.com/u/0/110628307450669829245 Subscribe:https://www.youtube.com/channel/UCyB_7yHs7y8u9rONDSXgkCg?view_as=subscriber programming:https://rmkdvuwnwybml22dwukvpa-on.drv.tw/web/
Views: 24 programming
Full Python + Pandas + Sentiment analysis Playlist: http://www.youtube.com/watch?v=0ySdEYUONz0&list=PLQVvvaa0QuDdktuSQRsofoGxC2PTSdsi7&feature=share Welcome to the introduction video for my Python and Pandas for sentiment analysis and investing series. This series will be using python with Pandas for data analysis. Our data set will be a database dump from Sentdex.com sentiment analysis, containing about 600 stocks, mostly S&P 500 stocks. Pandas will be used to work with our data quickly and efficiently. The ideas of Pandas is to act as a sort of framework for quickly analyzing data and modeling it. Sentiment Analysis data: http://sentdex.com/downloads/stocks_sentdex.csv.gz Python Module downloads: (Get all of the listed dependencies, or at least the major ones like NumPy, Dateutils, Matplotlib, ) http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas https://www.python.org/downloads/ http://matplotlib.org/downloads.html http://www.numpy.org/ http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 23625 sentdex
# Breast-cancer-detection * An application for breast cancer detection. The app can tell whether the breast mass is benign(non-cancerous cells) or malignant(cancerous cells). * is a Python3 project to classify cancer data using Google's TensorFlow library and Neural Networks. The goal of this project was to validate and demonstrate that modern machine learning techniques in neural nets could prove to be useful in classifying cancer datasets. * Link of the dataset used: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29 and http://portals.broadinstitute.org/cgi-bin/cancer/datasets.cgi * This repo contains dnn_data_classifier - A Deep Neural Network implementation to classify breast cancer tumours as benign or malignant depending on measurements taken directly from tumours. * The motivation for applying neural nets at cancer in particular came from Cancer Research's Citizen Science. This is a project that relied on volunteers to classify images of breast cancer tumours. The images themselves contained a mixture of different looking cells. Despite having over 2,000,000 contributions, the project struggled to differentiate cancer cells from non-cancer cells. Relying on volunteers to manually classify cancer seemed both inefficient and ineffective, and I believed that neural nets could provide a better method for classifying cancer. * The attributes in the sample are in the range (1-10). So, doctors will enter the values of attributes and clicking on the compute button can tell whether it's benign or malignant. * Video : (will be available soon) Source Code :: https://github.com/harrypotter0/hackdata2.0
Views: 182 Akash Kandpal
#scikitlearn #python #normalizednerd In this video I've explained the concept of feature scaling and how to implement it in the popular library known as scikit learn. We'll be dealing with three kinds of transformations : Standardscaler, Minmax scaling and logarithmic scaling. Stay tuned more scikit learn videos are coming! For more videos please subscribe - http://bit.ly/normalizedNERD Data Source - http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant
Views: 74 Normalized Nerd
In this video, I'm going to show you how to download your GitHub data. You can request an archive of your GitHub data. That archive will contain your repositories, reviews, releases, attachments, comments, events, pull requests, GitHub issues, settings related to your GitHub repositories, etc., in JSON and GIT formats. To do this, log in to your GitHub account. After that, click on the Profile icon and select Settings. Under Settings, click on the Account option. Now scroll down the Account page and you will see Export account data. There is a Start export button. Click that button and your GitHub data will be prepared in some hours. You will receive the email in the email address registered with GitHub account and that email will contain the link to download your GitHub data. This way, you can download all your GitHub data to your PC.
Views: 107 ilovefreesoftwareTV
Full Python + Pandas + Sentiment analysis Playlist: http://www.youtube.com/watch?v=0ySdEYUONz0&list=PLQVvvaa0QuDdktuSQRsofoGxC2PTSdsi7&feature=share This video tutorial is dedicated to teaching the basics of using Pandas with Python. In this example we grab stock prices from Yahoo Finance, learn how to access specific columns, how to modify columns, add columns, delete columns, and perform basic math on them. This series uses python with Pandas for data analysis. Our data set will be a database dump from Sentdex.com sentiment analysis, containing about 600 stocks, mostly S&P 500 stocks. Pandas is used to work with our data quickly and efficiently. The ideas of Pandas is to act as a sort of framework for quickly analyzing data and modeling it. Sentiment Analysis data: http://sentdex.com/downloads/stocks_sentdex.csv.gz Matplotlib Styles video: https://www.youtube.com/watch?v=WmhdQdx8Gjo Python Module downloads: (Get all of the listed dependencies, or at least the major ones like NumPy, Dateutils, Matplotlib, ) http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas https://www.python.org/downloads/ http://matplotlib.org/downloads.html http://www.numpy.org/ http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 14502 sentdex
Demonstration of Bayes' Theorem using "Weka Tool".
Views: 44 Tarah Technologies
#NeuralNetworks #BackPropogation #ScikitLearn #MachineLearning Neural Networks also called Multi Layer perceptrons in scikit learn library are very popular when it comes to machine learning algorithms. The Backpropogation algorithms helps train the neural network. In this tutorial we apply Neural Networks to using scikit learn library on the MNIST handwriting dataset and check the accuracy. The sigmoid activation function helps in containing the output between 0 and 1. We use sklearn.neural_networks class to implement neural networks using scikit learn. When there are many hidden layers, it is called deep Neural Network. Neural Networks can help learn many things and different variants of the Artificial Neural Network help in various specific tasks. Find the code here Github : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Scikit-learn official documentation on MLPClassifier : http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html Check out the machine learning, deep learning and developer products USA: https://www.amazon.com/shop/thesemicolon India: https://www.amazon.in/shop/thesemicolon
Views: 17249 The Semicolon
Open Data and Data Management. This event was held as part of Open Access Week. Even in a world drowning in data, it can often be a challenge for researchers to locate, analyze, and maintain relevant data sets effectively and efficiently. This talk by Data Librarian David Lowe addresses openly accessible data sets, their project life cycles, and current best practices that researchers should follow.
Views: 2184 Evans Library
59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 139470 Brandon Weinberg
In this project we classify movement of a person into WALKING, RUNNING, SHAKING LEGS and RESTING and based on the labour made by the person we control the room temperature. This uses: 1) Android App that tracks Accelerometer data and classifies it( Fuzzy classifier) 2) Android App pushes data to local MqTT server ( windows) 3) Dot Net client fetches the data from local MqTT server and finds desired speed based on Multi-metric projection 4) Dot Net client pushes the desired speed to iot.eclipse.org 5) Edison javascipt app monitors temperature and displays in LCD along with color ( red- no mobility, YELLOW- slight mobility, GREEN- too much mobility) 7) Edison app also listens to iot.eclipse.org and controls fan speed using PWM according to speed value sent by .Net
Views: 387 rupam rupam
I am going to learn Random Forest in python from start. Site - "https://machinelearningmastery.com/implement-random-forest-scratch-python/" greedy algo-"https://www.tutorialspoint.com/data_structures_algorithms/greedy_algorithms.htm" Dataset-"https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)" You can also check out the link for reference. Understanding the Random Forest Concept and then we will dive into scratch to create it. Wish me all the best.
Views: 45 Rahul Kumar
Video ini di tunjukan untuk memenuhi tugas dan untuk pembelajaran
Views: 108 Aldignwn _
Demo of loading files (csv, txt, excel, etc.) and data from a database into Jupyter Notebook. Sample Code: https://github.com/xbwei/machine_learning_in_python/blob/master/read_files_and_data.ipynb More about operating files and database in python: https://www.youtube.com/watch?v=57lmPaijWCo&list=PLHutrxqbP1BxK4KzYL8tJmMhV2yhGZNlY
Views: 5312 Xuebin Wei