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Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 395731 sentdex
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: 193124 APMonitor.com
Learn to Analyze Text Data in Bash Shell and Linux (Course Organization)
 
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1000+ students have taken this innovative project-based data learning course (includes video lectures and an eBook with source codes and data sets) "Learn to Analyze Text Data in Bash Shell and Linux " https://school.scientificprogramming.io/learn-to-analyze-text-data-in-bash-shell-and-linux-video-lectures Can you build a script to count the number of sequences in a Big data consisting hundred thousands of nucleotide sequences in 30 seconds? You may wonder to know, this wouldn't take more a than a few words in Bash! Three simple projects to demonstrate the use of Bash shell in processing csv formatted text data sets. This course starts with some practical bash-based flat file data mining projects involving: University ranking data Facebook data Crime Data There are several examples of practical data mining that will have a flow of importing specific data resources into flat text-type files. Bash can run different programs (grep, sort, sed, and so on) on those files, clean, optimise and extract preliminary views (cut, csvlook, view, cat, head, etc.) of the data. There is one part of data mining, which involves unstructured data and then transforming it into a structured one (awk, shell). A scripting language like Bash can be very useful for doing the transformation.
mining text data projects
 
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Views: 68 PHD Projects
Natural Language Processing and Automated Speech Recognition for Data Analytics
 
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Learn more at - https://amzn.to/2Hfy4FZ Gain insights in your customers and data by utilising machine learning techniques to analyse customer contact centers call recordings, translate them into different languages, and use them for further analysis of what drives positive outcomes. Using Amazon Transcribe for speech to text, Amazon Translate for language translation and Amazon Comprehend for insights in unstructured text. Speaker: Shaun Ray, Head of Solutions Architect, Amazon Web Services, ASEAN
Views: 675 Amazon Web Services
Python Tutorial - Data extraction from raw text
 
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This tutorial focuses on very basic yet powerful operations in Python, to extract meaningful information from junk data. The overall video is covers these 4 points. 1. Basic string operations for data extraction 2. How to open a text file 3. How to read rows line by line 4. Data extraction from junk Feel free to write to me with suggestions and feedback. Stay connected!
Views: 1677 xtremeExcel
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 430122 Brandon Weinberg
How to extract text from an image in python | pytesseract | Image to text processing
 
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In this tutorial, we shall demonstrate you how to extract texts from any image in python. So we shall write a program in python using the module pytesseract that will extract text from any image like .jpg, .jpeg, .png etc. Please subscribe to my youtube channel for such tutorials Watch the same tutorial on how to extract text from an image in Linux below: https://youtu.be/gLUQ8uaaw8A Please watch the split a file by line number here: https://youtu.be/ADRmbu3puCg Split utility in Linux/Unix : to break huge file into small pieces https://www.youtube.com/watch?v=ADRmbu3puCg How to keep sessions alive in terminal/putty infinitely in linux/unix : Useful tips https://www.youtube.com/watch?v=ARIgHdpxaU8 Random value generator and shuffling in python https://www.youtube.com/watch?v=AKwnQQ8TBBM Intro to class in python https://www.youtube.com/watch?v=E6kKZXHS5hM Lists, tuples, dictionary in python https://www.youtube.com/watch?v=Axea1CSewzc Python basic tutorial for beginners https://www.youtube.com/watch?v=_JyjbZc0euY Python basics tutorial for beginners part 2 -variables in python https://www.youtube.com/watch?v=ZlsptvP69NU Vi editor basic to advance part 1 https://www.youtube.com/watch?v=vqxQx-NNyFM Vi editor basic to advance part 2 https://www.youtube.com/watch?v=OWKp2DLaFyY Keyboard remapping in linux, switching keys as per your own choice https://www.youtube.com/watch?v=kJz7uKDyZjs How to install/open an on sceen keyboard in Linux/Unix system https://www.youtube.com/watch?v=d71i9SZX6ck Python IDE for windows , linux and mac OS https://www.youtube.com/watch?v=-tG54yoDs68 How to record screen or sessions in Linux/Unix https://www.youtube.com/watch?v=cx59c15-c8s How to download and install PAGE GUI builder for python https://www.youtube.com/watch?v=dim725Px2hM Create a basic Login page in python using GUI builder PAGE https://www.youtube.com/watch?v=oCAWWUhwEUQ Working with RadioButton in python in PAGE builder https://www.youtube.com/watch?v=YJbQvpzJDr4 Basic program on Multithreading in python using thread module https://www.youtube.com/watch?v=RGm3989ekAc
Views: 15790 LinuxUnixAix
Parsing Text Files in Python
 
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A short program to read lines from a text file and extract information, patterns, from each line.
Views: 91330 Dominique Thiebaut
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 105880 UiPath
Twitter Sentiment Analysis using Hadoop on Windows
 
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This is a demonstration based session which will show how to use a HDInsight (Apache Hadoop exposed as an Azure Service) cluster to do sentiment analysis from live Twitter feeds on a specific keyword or brand. Sentiment analysis is parsing unstructured data that represents opinions, emotions, and attitudes contained in sources such as social media posts, blogs, online product reviews, and customer support interactions. The demo uses Hadoop Hive and MapReduce to schematize, refine and transform raw Twitter data. It will also focuses on the Hive endpoint that HDInsight exposes for client applications to consume HDInsight data through the Hive ODBC interface. Finally, this session will show the present day self-service BI tools (Power View, Power Query and Power Map) to demonstrate how you can generate powerful and interactive visualization on your twitter data to enhance your brand promotion/productivity with just a few mouse clicks.
Views: 34733 Debarchan Sarkar
How to recognize text from image with Python OpenCv OCR ?
 
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Recognize text from image using Python+ OpenCv + OCR. Buy me a coffe https://www.paypal.me/tramvm/5 if you think this is a helpful. Source code: http://www.tramvm.com/2017/05/recognize-text-from-image-with-python.html Relative videos: 1. Recognize answer sheet with mobile phone: https://youtu.be/82FlPaQ92OU 2. Recognize marked grid with USB camera: https://youtu.be/62P0c8YqVDk 3. Recognize answers sheet with mobile phone: https://youtu.be/xVLC4WdXvhE
Views: 89660 Tram Vo Minh
An Introduction to GPU Programming with CUDA
 
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If you can parallelize your code by harnessing the power of the GPU, I bow to you. GPU code is usually abstracted away by by the popular deep learning frameworks, but knowing how it works is really useful. CUDA is the most popular of the GPU frameworks so we're going to add two arrays together, then optimize that process using it. I love CUDA! Code for this video: https://github.com/llSourcell/An_Introduction_to_GPU_Programming Alberto's Winning Code: https://github.com/alberduris/SirajsCodingChallenges/tree/master/Stock%20Market%20Prediction Hutauf's runner-up code: https://github.com/hutauf/Stock_Market_Prediction Please Subscribe! And like. And comment. That's what keeps me going. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://supercomputingblog.com/cuda-tutorials/ http://www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.pdf https://devblogs.nvidia.com/parallelforall/even-easier-introduction-cuda/ https://developer.nvidia.com/cuda-education-training https://llpanorama.wordpress.com/cuda-tutorial/ https://www.udacity.com/course/intro-to-parallel-programming--cs344 http://lorenabarba.com/gpuatbu/Program_files/Cruz_gpuComputing09.pdf http://cuda-programming.blogspot.nl/p/tutorial.html https://www.cc.gatech.edu/~vetter/keeneland/tutorial-2011-04-14/02-cuda-overview.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ No, Nvidia did not pay me to make this video lol. I just love CUDA. 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/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 149867 Siraj Raval
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
 
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Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analyzing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyze text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.
Views: 2337 node.js
Python Tutorial: Anaconda - Installation and Using Conda
 
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In this Python Tutorial, we will be learning how to install Anaconda by Continuum Analytics. Anaconda is a data science platform that comes with a lot of useful features right out of the box. Many people find that installing Python through Anaconda is much easier than doing so manually. Also, we will look at Conda. Conda is Continuum's package, dependency and environment manager. Let's get started. Anaconda Download Page: https://www.anaconda.com/download/ If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 506424 Corey Schafer
How to do real-time Twitter Sentiment Analysis (or any analysis)
 
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This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too. If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/ Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 69250 sentdex
BASH scripting lesson 10 working with CSV files
 
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More videos like this online at http://www.theurbanpenguin.com We now have some more great fun and see how much we can use the shell for; creating reports easily from the command line against CSV files. The script should be quite easy to read now as we use a while loop to read in the CSV file. We change the file delimiter to be the comma and then we have the line that we read in broken up into the schema elements we need. A report then is easy with colours and search ability. This is very usable
Views: 44424 theurbanpenguin
s2 Text Processing
 
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Views: 364 Ammar Bader
Text To Speech Using festival NLP
 
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Text To Speech using festival tool to convert text to speech from browser using pipe xsel. how to convert Text to speech using Natural Language Processing.
Views: 4082 Wise Flame
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - 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] Call us at US: +18336900808 (Toll Free) or India: +918861301699 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 26241 edureka!
Large Scale Processing of Unstructured Text
 
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Suneel Marthi, Red Hat
Views: 260 DataWorks Summit
NaCTeM's Search for Clinical Trials powered by Text Mining
 
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The NaCTeM Clinical Trials Protocols Demonstrator shows how, by using text mining techniques, we can make it easier for you to search protocols, and, if you are interested in designing your own protocol, easier for you to generate lists of important eligibility criteria associated with similar protocols to the one you have in mind.
Views: 402 Yannis Korkontzelos
Data Science Interview Questions | Data Science Tutorial | Data Science Interviews | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Data Science Interview Questions and Answers video will help you to prepare yourself for Data Science and Big Data Analytics interviews. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science, Big Data Analytics and Machine Learning. Below are the topics covered in this tutorial: 1. Data Science Job Trends 2. Data Science Interview Questions A. Statistics Questions B. Data Analytics Questions C. Machine Learning Questions D. Probability Questions 3. Conclusion Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #DataScienceInterviewQuestions #BigDataAnalytics #DataScienceTutorial #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: 65780 edureka!
07  Unix Shell Scripting Tutorial   Text Processing Part 1)
 
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Original Source : http://www.youtube.com/watch?v=aWxG8TqudTU This video contains the video tutorial for shell scripting in Linux. Part 1
Views: 225 LinuxVideoLectures
Python Trainer Tip: Parsing Data Into Tables from Text Files with Python's Pandas Library
 
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To parse text files into tables for analysis you'd need to build a custom parser, use a loop function to read text chunks, then use an if/then statement or regular expressions to decide what to do with the data. Or, you can simply use Python's Pandas library to read the text into a DataFrame (table) with a single function! Download the set of 8 Pandas Cheat Sheets for more Python Trainer Tips: https://www.enthought.com/pandas-mastery-workshop.
Views: 6832 Enthought
Perl Tutorial - 51: Reading Text from a File
 
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Thanks for watching! Check out my other tutorials at: https://www.youtube.com/user/madhurbhatia89?feature=guide
Views: 9554 The Bad Tutorials
Introduction/tutorial to visual programming in Orange (python-based) a Data Mining Tool
 
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Sumaiya Iqbal, Broad Institute of MIT and Hardvard & MGH is giving a overview of Orange a python-based Data Mining Tool. This tool is useful for individuals with and without programming background. Sumaiya gives examples for hierarchical clustering, PCA, prediction and text mining.
Views: 1593 Dennis Lal
PDF file: Reading and Extracting data using Python
 
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This a basic program for understanding PyPDF2 module and its methods. Simple program to read data in a PDF file.
Views: 1514 P Prog
Why you should use Python 3 for text processing
 
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David Mertz Python is a great language for text processing. Each new version of Python--but especially the 3.x series--has enhanced this strength of the language. String (and byte) objects have grown some handy methods and some built-in functions ha
Views: 9543 Next Day Video
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka
 
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** Flat 20% Off (Use Code: YOUTUBE) TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka TensorFlow Tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial: 1. What are Tensors? 2. What is TensorFlow? 3. TensorFlow Code-basics 4. Graph Visualization 5. TensorFlow Data structures 6. Use-Case Naval Mine Identifier (NMI) Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? 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. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. Please write back to us at [email protected] or call us at +91 88808 62004 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 273797 edureka!
Python Basics - 28 part 1 - Check if Specific Words Exist in A File
 
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Master Tkinter by Building 5 Apps! For the first 15 students there is %50 discount. This discount will not last for so long. Here is the link: https://www.udemy.com/master-tkinter-...
Views: 9493 HYPED247
How to use the Twitter API v1.1 with Python to stream tweets
 
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Part 1: http://youtu.be/pUUxmvvl2FE Part 2: http://youtu.be/d-Et9uD463A Part 3: http://youtu.be/AtqqVXZ365g In this video, you are shown how to use Twitter's API v1.1 to stream tweets using Python. Twitter's on-site documentation for their API is massive, but I found it to be a bit overboard for the simple task I wanted to achieve. If you have been having trouble figuring out how to stream twitter in python, this should help you. Sentdex.com Facebook.com/sentdex Twitter.com/sentdex Example code: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/
Views: 148338 sentdex
Data Processing with Python, SciPy2013 Tutorial, Part 3 of 3
 
01:25:42
Presenters: Ben Zaitlen, Clayton Davis Description This tutorial is a crash course in data processing and analysis with Python. We will explore a wide variety of domains and data types (text, time-series, log files, etc.) and demonstrate how Python and a number of accompanying modules can be used for effective scientific expression. Starting with NumPy and Pandas, we will begin with loading, managing, cleaning and exploring real-world data right off the instrument. Next, we will return to NumPy and continue on with SciKit-Learn, focusing on a common dimensionality-reduction technique: PCA. In the second half of the course, we will introduce Python for Big Data Analysis and introduce two common distributed solutions: IPython Parallel and MapReduce. We will develop several routines commonly used for simultaneous calculations and analysis. Using Disco -- a Python MapReduce framework -- we will introduce the concept of MapReduce and build up several scripts which can process a variety of public data sets. Additionally, users will also learn how to launch and manage their own clusters leveraging AWS and StarCluster. Outline *Setup/Install Check (15) *NumPy/Pandas (30) *Series *Dataframe *Missing Data *Resampling *Plotting *PCA (15) *NumPy *Sci-Kit Learn *Parallel-Coordinates *MapReduce (30) *Intro *Disco *Hadoop *Count Words *EC2 and Starcluster (15) *IPython Parallel (30) *Bitly Links Example (30) *Wiki Log Analysis (30) 45 minutes extra for questions, pitfalls, and break Each student will have access to a 3 node EC2 cluster where they will modify and execute examples. Each cluster will have Anaconda, IPython Notebook, Disco, and Hadoop preconfigured Required Packages All examples in this tutorial will use real data. Attendees are expected to have some familiarity with statistical methods and familiarity with common NumPy routines. Users should come with the latest version of Anaconda pre-installed on their laptop and a working SSH client. Documentation Preliminary work can be found at: https://github.com/ContinuumIO/tutorials
Views: 2402 Enthought
RapidMiner Ubuntu install
 
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Short video showing how to download, extract and run RapidMiner with Ubuntu.
Views: 16238 Bradley Nixon
Data Processing: Missing Data (Last Part)
 
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Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP 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. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. The next video: https://www.youtube.com/watch?v=BnmqT8ABvbg&index=5&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe Data Proessing Complete Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe The previous video:https://www.youtube.com/watch?v=gOLgidPEclA&index=3&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe 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? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g 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 #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #MissingData #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 394 Planeter
A Tour of the KNIME Node Repository
 
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This video explores the KNIME Node Repository to show the features and modules available in the KNIME Analytics Platform. We start with IO, moving to Mining and Statistics, through ETL and Data Manipulation, Data Views, Tool & Script Integration, and many more. - Installation of KNIME Analytics Platform on Linux available at https://youtu.be/wibggQYr4ZA - Installation of KNIME Analytics Platform on Windows available at https://youtu.be/yeHblDxakLk - Installation of KNIME Analytics Platform on Mac available at https://youtu.be/1jvRWryJ220 - "What is a node, what is a workflow" https://youtu.be/M4j5jQBTEsM Next: - "The EXAMPLES Server" https://youtu.be/CRa_SbWgmVk - "Workflow Coach: The Wisdom of the KNIME Crowd" https://youtu.be/RusMXn-shsQ
Views: 3641 KNIMETV
9. Data mining. Softmax слой. Различные топологии
 
02:28:41
Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы обработки больших объемов данных" (осень 2015) Лекция №9 - "Softmax слой. Различные топологии" Лектор - Павел Нестеров Другие лекции курса | https://www.youtube.com/playlist?list... -- Официальный канал образовательных проектов Mail.Ru Group | http://www.youtube.com/user/TPMGTU?su... НАШИ ПРОЕКТЫ: "Технопарк" при МГТУ им. Баумана | https://park.mail.ru/ "Техносфера" при МГУ им. Ломоносова | https://sphere.mail.ru/ "Технотрек" при МФТИ | https://track.mail.ru/ Мы готовим квалифицированных специалистов для российского рынка веб-разработки. У нас - бесплатное практико-ориентированное обучение под руководством лучших специалистов Mail.Ru Group. Преподавание строится на примерах из реальной практики, существующих проектов, с анализом их достоинств и недостатков. Лучшие студенты получают возможность стажировки в Mail.Ru Group. Отбор в проекты проходит каждые полгода. МЫ В СОЦ. СЕТЯХ: Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Технотрек в ВКонтакте | https://vk.com/trackmailru Блог на Хабре | http://habrahabr.ru/company/mailru/
Process big text file, extract all lines containing specified sub-strings
 
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String Master (by http://ChaosCoder.Com) is designed to split one string data source file into two or more files in accordance with the chosen mask or substrings. Main application areas of the software are the search of the required entries in the database, forum and web log dumps, text files of large volume, automation of the reference database sorting by domains and names. String Master reads the source file information string by string, verifies its compliance with the specified substrings or mask before putting in buffer. As the buffer fills the sorted data is stored in files, the names of which duplicate the name of the initial file with the addition of the sorting numbers of masks in the program list. Strings that do not match any of the user-defined masks are recorded in the file with a zero. When Split by Masks - Single (F3) is selected, files are created according to the number of masks / substrings; each file is given a name composed of the source file’s name and the sorting number of the mask or substring. When naming the base.txt source file and all the links displayed in the default substrings containing «. Com /», will be saved in a file named base.txt.1.txt; containing «. Net /» - to file base.txt .2. txt; containing «. org /» - to file base.txt.3.txt, and so on. If a line corresponding to several substrings or masks is found, then it will be placed only in the file with a mask placed higher in the list. If in addition, if there are lines not suiting any mask or substring, then will be they will be placed in a file bearing the source name and the index 0. Of course, String Master is not only suitable for working with lists of links, and a variety of databases of forums and websites. Through a stable and fast work with large files this program can have lots of other uses. Attention! StringMasters reduces the processing speed when executed the second time without being restarted. In order to avoid sudden drop in speed, simply change the buffer size like shown in the present video and the speed of processing will raise to a possible maximum!
Views: 3250 ChaosCoder.Com
Data Science Training | Introduction to Data Science | Data Science Certification | Edureka
 
01:23:56
** Data Science Master's Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka "Data Science Training" video (Data Science Blog Series: https://goo.gl/1CKTyN) will help you understand all the concepts of Data Science. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial: 1. What is Data Science? 2. Job Roles in Data Science 3. Components of Data Science 4. Concepts of Statistics 5. Power of Data Visualization 6. Introduction to Machine Learning using R 7. Supervised & Unsupervised Learning 8. Classification, Clustering & Recommenders 9. Text Mining & Time Series 10. Deep Learning Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #Datasciencetraining #Datasciencetutorial #Datasciencecourse #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: 10681 edureka!
Basic Splunk for Log Analysis
 
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Splunk is one of my favorite tools for doing quick log analysis. Here I demonstrate just a couple of basic searches to show how it's used. http:--www.infoseccertified.com-
Views: 45461 Infosec Certified
Scammed on ebay... Testing the 56 CORE system!
 
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Thanks so much for Audible for sponsoring this video! Get Your FREE 30 Day Audible Trial Today! Head to http://audible.com/linus or text Linus to 500-500. Enter our competition to win a free 6-month subscription by Tweeting @LinusTech screenshots of your favorite Audio Books! #NewYearNewMe Buy Intel Xeon processors on Amazon: http://geni.us/9deQri Discuss on the forum: https://linustechtips.com/main/topic/890948-scammed-on-ebay-testing-the-56-core-system/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 5631633 Linus Tech Tips
Log File Frequency Analysis with Python
 
01:00:53
Information Security professionals often have reason to analyze logs. Whether Red Team or Blue Team, there are countless times that you find yourself using "grep", "tail", "cut", "sort", "uniq", and even "awk"! While these powerful UNIX methods take us far, there is always that time when you want more power! In this webcast, Joff Thyer will discuss using Python regular expressions, and dictionaries to extract useful data for frequency analysis. If you want to learn even more about Python, join Joff for SANS SEC573 - "Automating Information Security with Python" www.sans.org/sec573 Slides available here: https://www.blackhillsinfosec.com/webcast-log-file-frequency-analysis-python/
OpenCV Programming with Python on Linux Ubuntu 14.04 Tutorial-1 OpenCV Installation
 
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Tutorial on how to install OpenCV on 64bit Linux Ubuntu 14.04 ########## INSTALLATION STEPS BELOW ############### OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Adopted all around the world, OpenCV has more than 47 thousand people in their user community and estimated number of downloads exceeding 6 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. **************************************************************************** Source Code Steps For Installation #get the latest download wget http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.10/opencv-2.4.10.zip #download dependencies sudo apt-get install build-essential checkinstall cmake pkg-config yasm sudo apt-get install libtiff4-dev libjpeg-dev libjasper-dev sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev sudo apt-get install python-dev python-numpy sudo apt-get install libtbb-dev cmake -D WITH_XINE=ON -D WITH_OPENGL=ON -D WITH_TBB=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_V4L=ON.. #ready for building sudo make #after building install for all users sudo make install #open a text editor and edit the ld.so.conf file by changing directory into it with privileges sudo nano /etc/ld.so.conf #and enter the following line into it /usr/local/lib #close and save and exit and enter the following command sudo ldconfig sudo nano /etc/bash.bashrc PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig export PKG_CONFIG_PATH #Now you are ready for testing cd ~/OpenCV-2.4.6/samples/c chmod +x build_all.sh ./build_all.sh #try the python examples and make sure they work python ~/OpenCV-2.4.10/samples/python2/turing.py any problems following this tutorial you can shoot me an email. My website: http://www.stemapks.com/opencv_python.html My email: [email protected] My Twitter -https://twitter.com/Cesco345
Views: 52810 Francesco Piscani
Sentiment Analysis Using Python
 
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This video tutorial teaches you how to do sentiment ananlysis using Python and get started into the field of Natural Language Processing and Machine learning. In the part 2 of this video, I will be showing you how to do twitter sentiment analysis and will also show you how to use twitter api. Dependencies: * Textblob * nltk * python3 * python-pip In the video I used: * Text Editor - Vim * Operating System - Manjaro Linux * Browser - Firefox Follow me on twitter - https://twitter.com/_teeej__
Views: 1894 Djangodude
Text Mining Project Topics | Text Mining Thesis Topics | Text Mining Code Project Topics
 
02:50
Contact Best Matlab Projects Visit us: https://matlabprojects.org/
Views: 41 Matlab Projects

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