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The Best Way to Prepare a Dataset Easily
 
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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/
Views: 131926 Siraj Raval
How to download Dataset from UCI Repository
 
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The video has sound issues. please bare with us. This video will help in demonstrating the step-by-step approach to download Datasets from the UCI repository.
Views: 4267 Santhosh Shanmugam
Naive Bayes Classifier in R
 
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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
Text Classification using Machine Learning : Part 1 - Preprocessing the data
 
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Join me as I build a spam filtering bot using Python and Scikit-learn. In this video, we are going to preprocess some data to make it suitable to train a model on. Code is optimised for Python 2. Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz Part 2: https://youtu.be/6Wd1C0-3RXM Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f
Views: 6252 Sourav Johar
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 55224 Nathan Kutz
Data MiningLabor Dataset
 
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This video is about labor dataset being solved by using WEKA Toll. Its for the classification of their data to be more manageable. Hope all of you can enjoy watching this video.
Views: 79 nurul atiqah
Data Mining For Automated Personality Classification
 
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
Views: 4358 Nevon Projects
20-Newsgroups Classification and Prediction by Zihao Ren and Sihan Peng
 
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Machine Learning 2017 final project: 20-Newsgroups Classification and Prediction by Zihao Ren and Sihan Peng
student data r miner classification
 
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data mining.classification of student math data using RapidMiner tool.classification using Decision Tree Model and Naive Bayes Model.
Views: 1831 nur 'awatif
persiapan dataset data mining
 
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Dataset yang digunakan berupa dataset iris dari repository UCI yang pada kesempatan ini dirubah tipe datanya dari CSV ke Text. Hal ini dilakukan agar dataset dapat digunakan untuk menguji algoritma machine learning yang akan saya demokan pada video tutorial berikutnya.
Views: 464 Umar Ghoni
Random Forest with R : Classification with The South African Heart Disease Dataset
 
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Random Forest with R : Classification with The South African Heart Disease Dataset
Datasets : How to Download?
 
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Datasets : How to Download?
Views: 3915 Social Networks
Weka Text Classification for First Time & Beginner Users
 
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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: 129023 Brandon Weinberg
Data Mining Project - Analysis on Car Dataset
 
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In this video, I have demonstrated the analysis performed on the car dataset (dataset source: UCI repository) by using SAS Enterprise Miner.
Getting started in scikit-learn with the famous iris dataset
 
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Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning terminology, and discuss the four key requirements for working with data in scikit-learn. This is the third video in the series: "Introduction to machine learning with scikit-learn". Read more about the video here: http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/ The IPython notebook shown in the video is available on GitHub: https://github.com/justmarkham/scikit-learn-videos == RESOURCES == Iris dataset in UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/Iris scikit-learn dataset loading utilities: http://scikit-learn.org/stable/datasets/ Fast Numerical Computing with NumPy (slides): https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015 Fast Numerical Computing with NumPy (video): https://www.youtube.com/watch?v=EEUXKG97YRw Introduction to NumPy (PDF): http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf == SUBSCRIBE! == https://www.youtube.com/user/dataschool?sub_confirmation=1 == LET'S CONNECT! == Blog: http://www.dataschool.io Newsletter: http://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham GitHub: https://github.com/justmarkham
Views: 126728 Data School
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 7783 Tutorial Spot
Oracle data mining tutorial, data mining techniques: classification
 
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What is data mining? The Oracle Data Miner tutorial presents data mining introduction. Learn data mining techniques. More lessons, visit http://www.learn-with-video-tutorials.com/oracle-data-mining-tutorial-video
First time Weka Use : How to create & load data set in Weka : Weka Tutorial # 2
 
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This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Views: 22959 HowTo
Data Mining with Weka (1.3: Exploring datasets)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 72994 WekaMOOC
Intrusion Detection based on KDD Cup Dataset
 
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Final Presentation for Big Data Analysis
Views: 6747 Qiankun Zhuang
CS2401 Tool Demo: RapidMiner for Classification
 
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RapidMiner classification tutorial for CS2401 by Team Wobbles. Jay Yeo Ng Yan Xiang Magnus Pang Dionne Lee Theresia Marten Downloads: https://my.rapidminer.com/nexus/account/index.html#downloads Compare versions: https://rapidminer.com/products/comparison/ German Credit Data Set: http://www.learnpredictiveanalytics.com/uploads/4/2/1/5/42154413/pa_dm_files_dec_15_2014.zip
Views: 5390 Jay Yeo
Handling Class Imbalance Problem in R: Improving Predictive Model Performance
 
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Provides steps for carrying handling class imbalance problem when developing classification and prediction models Download R file: https://goo.gl/ns7zNm data: https://goo.gl/d5JFtq Includes, - What is Class Imbalance Problem? - Data partitioning - Data for developing prediction model - Developing prediction model - Predictive model evaluation - Confusion matrix, - Accuracy, sensitivity, and specificity - Oversampling, undersampling, synthetic sampling using random over sampling examples predictive models are important machine learning and statistical tools 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: 8833 Bharatendra Rai
Weka Tutorial 03: Classification 101 using Explorer (Classification)
 
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In this tutorial, classification using Weka Explorer is demonstrated. This is the very basic tutorial where a simple classifier is applied on a dataset in a 10 Fold CV. For more variations of classification, watch out other tutorials on this channel.
Views: 142389 Rushdi Shams
Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5
 
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Welcome to part five of the Deep Learning with Neural Networks and TensorFlow tutorials. Now that we've covered a simple example of an artificial neural network, let's further break this model down and learn how we might approach this if we had some data that wasn't preloaded and setup for us. This is usually the first challenge you will come up against afer you learn based on demos. The demo works, and that's awesome, and then you begin to wonder how you can stuff the data you have into the code. It's always a good idea to grab a dataset from somewhere, and try to do it yourself, as it will give you a better idea of how everything works and what formats you need data in. Positive data: https://pythonprogramming.net/static/downloads/machine-learning-data/pos.txt Negative data: https://pythonprogramming.net/static/downloads/machine-learning-data/neg.txt https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 104343 sentdex
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 130208 Well Academy
Data analytics CRISP-DM Project
 
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Data Analytics Classification Bank Marketing Dataset source UCI machine learning: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
Views: 127 PARTH PANDYA
Import Data and Analyze with MATLAB
 
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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: 325224 APMonitor.com
weka j48 classification tutorial
 
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This is a tutorial for the Innovation and technology course in the ePC-UCB. La Paz Bolivia
Views: 49410 Alejandro Peña
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 156295 Timothy DAuria
Naive Bayes Classifier - Multinomial Bernoulli Gaussian Using Sklearn in Python - Tutorial 32
 
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In this Python for Data Science tutorial, You will learn about Naive Bayes classifier (Multinomial Bernoulli Gaussian) using scikit learn and Urllib in Python to how to detect Spam using Jupyter Notebook. Multinomial Naive Bayes Classifier Bernoulli Naive Bayes Classifier Gaussian Naive Bayes Classifier This is the 32th 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. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 11272 TheEngineeringWorld
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 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/
Views: 215253 Siraj Raval
Run decision tree induction (J48) on the Mushroom data set using WEKA-- tutorial
 
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Step by step to show you how to run decision tree induction (J48) on the Mushroom data set, in the Classify tab. Download WEKA from https://www.cs.waikato.ac.nz/ml/weka/downloading.html Download mushroom dataset from https://github.com/renatopp/arff-datasets/blob/master/classification/mushroom.arff
Views: 246 熊志强
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 5980 WekaMOOC
R - Classification Trees (part 1 using C5.0)
 
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Classification Trees are part of the CART family of technique for prediction. Here we deploy the C5.0 algorithm in R to learn a classification tree model on the 'iris' data set available in all R installations.
Views: 52427 Jalayer Academy
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 183084 APMonitor.com
Decision Tree Analysis in R Example Tutorial
 
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Click here to download the example data set fitnessAppLog.csv: https://drive.google.com/open?id=0Bz9Gf6y-6XtTczZ2WnhIWHJpRHc
Views: 5347 The Data Science Show
K-Nearest Neighbor Classification (K-NN) Using Scikit-learn in Python - Tutorial 25
 
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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. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 9969 TheEngineeringWorld
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 41838 edureka!
Text Classification using Machine Learning : Part 2 - Training and deploying
 
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Join me as I build a spam filtering bot using Python and Scikit-learn. In this video, we train our model using the dataset and make a simple program which uses it to classify text. Code is optimised for Python 2. Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz Part 1:https://youtu.be/xm-wmBwJLww Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f
Views: 2601 Sourav Johar
Gaurang Panchal - Data Mining/Machine Learning Project
 
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Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 145 Gaurang Panchal
How to do the Titanic Kaggle competition in R - Part 1
 
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As part of submitting to Data Science Dojo's Kaggle competition you need to create a model out of the titanic data set. We will show you how to do this using RStudio. Titanic Data Set: https://www.kaggle.com/c/titanic Download RStudio: https://www.rstudio.com/products/rstu... -- At Data Science Dojo, we're extremely passionate about data science. We've helped educate and train 3200+ employees from nearly 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2mD6Grl See what our past attendees are saying here: http://bit.ly/2ocfaqj -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 43533 Data Science Dojo
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a dataset for model creation and validation and how you can create a model using any machine learning algorithm! In this Machine Learning Algorithms Tutorial video you will understand: 1) What is an Algorithm? 2) What is Machine Learning? 3) How is a problem solved using Machine Learning? 4) Types of Machine Learning 5) Machine Learning Algorithms 6) Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #MachineLearningAlgorithms #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 128750 edureka!
K Nearest Neighbour (KNN) Example
 
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Download the dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is kNN theory? In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. 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 majority 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 its 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 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.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". the next part: https://www.youtube.com/watch?v=DIRDA5-lY2k&list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t&index=3 the full Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t the previous part: https://www.youtube.com/watch?v=h99yt5Y2r4M&index=2&t=29s&list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DeepLearning # #robotics #রোবোটিক্স #প্ল্যানেটার #Planter #Pleneter #প্লেনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificationModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #KNNThoery #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #Planter #Pleneter #প্লেনেটার #machinelearningcrashcourse #imageprocessing #SpyderIDE
Views: 170 Planeter
Machine Learning with Small Data Sets in the Age of Deep Learning
 
01:14:49
Dr. Lei Tang and Dr. Xin Xu will talk about how they apply machine learning with small data sets in sales management and forecast. The recent successes of machine learning and deep learning can be largely attributed to three factors: emergence of abundant data, development of innovative algorithms, and availability of machine learning tools and computing resources. Unfortunately, not all application spaces provide data sets large enough to be used in the usual or obvious ways. In this talk, Lei and Xin focus on one specific domain, enterprise sales, where data is often limited in volume, always noisy, and constantly evolving. They describe how machine learning, and in particular deep learning, can help, and how we address the data challenges described. They specifically discuss how to select model architectures appropriate for these limited data situations, for example, how deep our networks should be. By sifting through sales records and associated sales activities Lei and Xin enable identification of at-risk opportunities as well as project and estimated the time required to close each deal. This, in turn, contributes to the generation of a reliable business forecast for sales managers and executives. Lessons and findings learned through the process is shared. Speaker Bios: Dr. Lei Tang is the Chief Data Scientist at Clari Inc., a startup backed by Sequoia Capital and Bain Capital ventures, focusing on predictive analytics for sales execution and forecasting. Lei received his Ph.D. in computer science from Arizona State University in 2010, and B.S. from Fudan University, China. He is passionate about reshaping variety of businesses, driving business growth and decision through data science and machine learning. From 2012-2014, Lei was the lead data scientist at Demand Generation of @WalmartLabs, where he worked closely with marketing team to drive traffic to site, impacting hundreds of revenue each year. Before that, Lei had 2-year stint at advertising sciences in Yahoo! Labs, working on targeting, user profiling/segmentation by mining user behavioral, social and content information. Lei has co-authored one book on “community detection and mining in social media” (top-download in the corresponding data mining lecture series), held 4 patents, published over 30 papers at top-notch conferences and journals on data mining/machine learning, with over 4000 citations. Dr. Xin Xu is currently working as a data scientist in Clari. Before this, She received her Ph.D degree in Computer Engineering from North Carolina State University in 2015. She also did summer intern in Bell Labs and Akamai Technology in 2014 and 2015 respectively. Her current research interest mainly focuses on applying data mining, machine learning and advanced analytics to solve practical problems in sales domain.
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 61135 WekaMOOC
Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35
 
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In this machine learning tutorial, we cover how to work with non-numerical data. This useful with any form of machine learning, all of which require data to be in numerical form, even when the real world data is not always in numerical form. Titanic Dataset: https://pythonprogramming.net/static/downloads/machine-learning-data/titanic.xls https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 34640 sentdex
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
01:04:06
( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 35671 edureka!