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Learn Python - Full Course for Beginners
 
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗The Mike's website: https://www.mikedane.com/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 2746519 freeCodeCamp.org
Jordi Torrents - Analyzing code contributions to the CPython project using NetworkX and Matplotlib
 
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Filmed at PyData Barcelona 2017 https://pydata.org/barcelona2017/schedule/presentation/15/ www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 232 PyData
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 880522 David Langer
Complete Python: Go from zero to hero in Python
 
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Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ http://vrl.to/c4099b4d9f Next Generation Shorten Platform. Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and earn big money ☞ https://viralroll.com/ Get Free 15 Geek ☞ https://geekcash.org/ CodeGeek's Discuss ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://vrl.to/d5fc7d45 Learn to code for free and get a developer job ☞ http://vrl.to/ee8f135b Complete Python Bootcamp: Go from zero to hero in Python ☞ http://deal.codetrick.net/p/rkbzrt_Sl Complete Python Masterclass ☞ http://deal.codetrick.net/p/SkCfL7xbe The Python Bible™ | Everything You Need to Program in Python ☞ http://deal.codetrick.net/p/Skc18mgbl Learning Python for Data Analysis and Visualization ☞ http://deal.codetrick.net/p/HywrG7e-l Python for Financial Analysis and Algorithmic Trading ☞ http://deal.codetrick.net/p/BkBWKHZtb Python A-Z™: Python For Data Science With Real Exercises! ☞ http://deal.codetrick.net/p/HkzuOBrEg Are you brand new to coding? Want to see how fun and easy it can be? Watch engaging experts Susan Ibach and Christopher Harrison for an entertaining introduction to programming with Python. Susan and Christopher offer a step-by-step walk-through, from a basic idea to translating that idea into code, and everything in between. Don't worry about making mistakes! Python uses simple syntax, has an easy learning curve, and is a very forgiving language. Gain a new skill or complete a task by the end of each module, and, by the end of the course, you will be programming in Python! You also learn basic principles which can make it easier for you to learn other programming languages in the future. Don't miss this opportunity to go beyond the if statement! NOTE: To get the most out of this Python training course, before the session, be sure to download these free tools: Visual Studio Community and Python Tools for Visual Studio. If you're a student, you have access to Visual Studio Professional 2013, for free, through DreamSpark. Instructor | Susan Ibach - Microsoft Canada Technical Evangelist; Christopher Harrison -Microsoft Content Development Manager Getting Started Explore applications of Python language, and create a "Hello world" application for Python in Visual Studio, as you learn the benefits of knowing Python. Get help setting up your computer, so you can start coding. Displaying Text Get an introduction to the print statement, comments, and basic formatting, so you can display and format text to a user. String Variables Learn about the input statement, string variables, and manipulate strings, so you can prompt a user for input, store values in a string, and use string functions to manipulate string values. Storing Numbers Hear an introduction to numeric datatypes and variables, how to do math operations, and datatype conversions. Learn to store numeric values and perform math operations. Working with Dates and Times Get the details on date variable storage and issues, along with date functions and formatting, so you can store and manipulate date values. Making Decisions with Code Hear an introduction to basic if/else statements and Boolean variables, so you can write code that reacts differently to different user inputs. Complex Decisions with Code Explore and/or statements, nested if statements, and elif, so you can write code that reacts differently to more complex user inputs. Repeating Events Take a look at for loops and nested for loops, so you can write programming in Python that repeats a fixed number of times. Repeating Events Until Done Play with while loops, and learn when to use for versus while loops, so you can write code that repeats as often as needed. Remembering Lists Get the details on arrays and lists, so you can store multiple values. How to Save Information in Files Hear about functions for creating and writing to files, so you can write code that saves information in a file and remember it later. Reading from Files Explore functions for reading from files, so you can read information that was saved in a file. Functions Learn about the syntax for declaring functions and how to call functions from your code, so you can use functions to avoid retyping the same code over and over. Handling Errors Get the details on syntax for error handling, so you can write code that can handle common error situations without crashing. Learn Explore programming to Python. Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/XnM72d Website: https://goo.gl/AWpXfC Playlist: https://goo.gl/hnwbLS Fanpage: https://goo.gl/o6pVzp Twitter: https://goo.gl/UrBoeq Wordpress: https://goo.gl/qAJxMe Pinterest: https://goo.gl/GrRx7B Tumblr: https://goo.gl/6fTauh
Views: 12188 coderschool
Data analysis in Python with pandas
 
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Wes McKinney The tutorial will give a hands-on introduction to manipulating and analyzing large and small structured data sets in Python using the pandas library. While the focus will be on learning the nuts and bolts of the library's features, I als
Views: 292938 Next Day Video
BioPython: Sequence Analysis (Part 1)
 
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Presented for the course COMP 364 at McGill University. Material: https://nbviewer.jupyter.org/github/cgoliver/Notebooks/blob/master/COMP_364/L25/L25.ipynb Webpage:http://cs.mcgill.ca/~cgonza11/COMP_364/
Views: 4460 Carlos G. Oliver
Real-time visualization with Python and d3.js (PyCon APAC 2014)
 
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Speaker: Muyueh Lee http://blog.muyueh.com/real-time-visualization-with-python-and-d3-js/ Pyhton has great power in scrapping and analyzing data, and D3.js is a great tool for building visual interface. In the first part of the talk, I will demonstrate how to set up D3.js as an interactive layer on top of Python. In the second part of the talk, I will show what it can achieve, by using the "Taiwan Vegetable Auction" dataset (past 10 years transaction data of 127 kinds of vegetable, 1GB). The dataset is too large for human to see through, a machine learning algorithm will be able to fit a regression model on the dataset, but it can't make sense of it. For example, in the following graph: You can see the average price of green onion in different markets for the past 20 years, the fluctuation between different markets are similar, yet since 2010, there is a perfectly horizontal blue line: while price in the other markets have changes, price in "Taitung" has remained exactly the same, suggesting a possible case of monopoly. One can then asks the system to detect other cases of monopoly. This process of exploratory data analysis can only be possible with both machine and human. From a technical perspective, this talk can benefit front-end developer/data scientist to set up such a system. Yet a more profound value of the talk will be to explore how machine and human can work together. About the speaker Muyueh Lee (李慕約) is a programmer focus on Data Visualization, for profit and for fun, he hosts "Visualization Lighting Talk", a gathering for programmers doing data visualization work in Taiwan, and has been teaching Visualization in the graduate school of Journalism at National Taiwan University, in the DataScience Program by CfT and SYSTEX, and held various workshop in Guangzhou and Hong Kong. A list of his work can be found at http://muyueh.com/1314/
Views: 17283 PyCon Taiwan
yts python downloader
 
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made with ezvid, free download at http://ezvid.com See how fast we can download movie torrents with yts python downloader.Using browser is a time taking task.
Views: 682 Naren Arya
Data Science from Scratch by Joel Grus: Review | Learn python, data science and machine learning
 
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This is a review of Data Science from Scratch by Joel Grus. This book will teach you the methods used for data science and machine learning. First it will show you the basics of the python language, then how to visualize data with matplotlib. It moves on to probability and statistics and then machine learning methods. After explaining the methods it starts to build up functions in python that will apply what has been learnt. It's an excellent introduction to Data Science. You can buy the book here:- https://amzn.to/2KIXzyo (USA) https://amzn.to/2FSIuqs (UK) (Affiliate links)
Views: 7558 Python Programmer
Data Science Tutorial | Data Science for Beginners | Data Science with Python Tutorial | Simplilearn
 
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This Data Science Tutorial will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist. This Data Science tutorial will cover the following topics: 1. What is Data Science? ( 00:43 ) 2. Who is a Data Scientist? ( 02:02 ) 3. What does a Data Scientist do? ( 02:25 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/V4Zn8i Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-bTTxei-Data-Sciene-Tutorial-jNeUBWrrRsQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 28685 Simplilearn
The Four Pillars of OOP in Python 3 for Beginners
 
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Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ http://vrl.to/c4099b4d9f Next Generation Shorten Platform. Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and earn big money ☞ https://viralroll.com/ Get Free 15 Geek ☞ https://geekcash.org/ CodeGeek's Discuss ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://vrl.to/d5fc7d45 Learn to code for free and get a developer job ☞ http://vrl.to/ee8f135b Complete Python Bootcamp: Go from zero to hero in Python 3 ☞ http://deal.codetrick.net/p/S15_M7e-l Complete Python Masterclass ☞ http://deal.codetrick.net/p/SkCfL7xbe The Python Bible™ | Everything You Need to Program in Python ☞ http://deal.codetrick.net/p/Skc18mgbl Python and Django Full Stack Web Developer Bootcamp ☞ http://deal.codetrick.net/p/r1-quFMgce Learning Python for Data Analysis and Visualization ☞ http://deal.codetrick.net/p/HywrG7e-l Python for Financial Analysis and Algorithmic Trading ☞ http://deal.codetrick.net/p/BkBWKHZtb Python OOP Simplified: Learn Object Oriented Programming using Python in a way that you really understand Learn to structure your Python code by making use of Classes and Objects. In this course you will learn how to achieve object oriented programming in Python by learning how to bundle attributes and methods within a class and instantiating them through an object. You will learn about the four pillars that hold together the object oriented programming, which are: Abstraction Encapsulation Polymorphism Inheritance At the end of this course, you will be able to write your own object oriented programs in Python! Who is the target audience? Students who would like to enhance their Python skills by learning the basics of object oriented programming Video source viva: Udemy ---------------------------------------------------- Website: http://bit.ly/2pN2aXx Playlist: http://bit.ly/2Eyn3dI Website: http://bit.ly/2Hay229 Fanpage: http://bit.ly/2qi5j1A Twitter: http://bit.ly/2GOyTlA Pinterest: http://bit.ly/2qihWtz Tumblr: http://bit.ly/2qjBcGo
Views: 1041 Learn4Startup
Ion Reporter Software and Server – Simplify your bioinformatics path to research results
 
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From human variant detection to microbial diversity, Ion Reporter™ Software provides an optimized suite of simple data analysis tools that streamline Ion PGM™ and Ion Proton™ System data analysis, so you can focus on finding the biological meaning of your data. Learn more at: http://www.lifetechnologies.com/us/en/home/life-science/sequencing/next-generation-sequencing/ion-torrent-next-generation-sequencing-workflow/ion-torrent-next-generation-sequencing-data-analysis-workflow/ion-reporter-software.html?icid=COAS?ICID=ta-lm-ion%20reporter%20software-Ion%20Reporter%20Software
Machine Learning Tutorial 2 - Intro to Predictive Data Analytics
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 8639 Caleb Curry
4) Next Generation Sequencing (NGS) - Data Analysis
 
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For more information on Next Generation Sequencing analyses and for a list of the sources used, please visit: ➜ Knowledge Base: https://goo.gl/Ce0M4O What is covered in this video: ➜ Previous videos in our Next Generation Sequencing (NGS) series describe the theory and technology of NGS platforms (https://youtu.be/jFCD8Q6qSTM), and the steps of library preparation for sequencing on the Illumina platform (https://youtu.be/-kTcFZxP6kM). In this installment we describe some of the common formats of NGS raw data and software that can be used for downstream analysis. Watch the other videos in this series on NGS: ➜ Introduction: https://youtu.be/jFCD8Q6qSTM ➜ Sample Preparation: https://youtu.be/-kTcFZxP6kM ➜ Coverage & Sample Quality Control: https://youtu.be/PGAfwSRYv1g ➜ NGS Playlist: https://youtu.be/jFCD8Q6qSTM?list=PLTt9kKfqE_0Gem8hIcJEn7YcesuuKdt_n Connect with us on our social media pages to stay up to date with the latest scientific discoveries: ➜ Facebook: https://goo.gl/hc9KrG ➜ Twitter: https://goo.gl/gGGtT9 ➜ LinkedIn: https://goo.gl/kSmbht ➜ Google+: https://goo.gl/5bRNwC
Visualizing twitter discussions with networkx
 
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Visualizing twitter discussions with networkx Every day, millions of people use social media like Twitter or Facebook, to catch up on news and engage in discussions with other users. Our research group at Aalto University analyzes such discussions and studies how people interact on social media. Towards this end, visualization can be a very useful tool. In this talk, we use networkx, a python library, to process and visualize user interactions on Twitter. We introduce networkx with simple examples and continue with the visualization of twitter data. During the talk, we’ll see that discussions about polarized topics (e.g., elections) look quite different than non-polarised ones." About the authors: Kiran Garimella is a PhD student at the Department of Computer Science, Aalto University. For his PhD, Kiran studies controversy and polarization on social media. Earlier in his career, he worked as Research Engineer at Yahoo! Research and the Qatar Computing Research Institute, and Machine Learning / Data Science intern at LinkedIn and Amazon. Michael Mathioudakis is a postdoctoral researcher at the Department of Computer Science, Aalto University. His research focuses on social media, social networks, and urban computing. Earlier, Michael did a PhD at the University of Toronto, and worked as Research Intern at Microsoft and Yahoo! Research. He is also currently working as a Data Scientist at Sometrik.
Views: 2047 PyCon Finland
Data analysis on PM Modi activities Kerala floods relief - TV9
 
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Watch #TelanganaElectionCounting LIVE: https://www.youtube.com/watch?v=v-bZONX2rp4 #TelanganaElectionResults2018 LIVE updates: https://goo.gl/rAxF6G #TelanganaElectionResultsOnTV9 Data analysis on PM Modi activities Kerala floods relief - TV9 ► Download Tv9 Android App: http://goo.gl/T1ZHNJ ► For More: https://goo.gl/UC3Yjq ► Circle us on G+: https://plus.google.com/+tv9 ► Like us on Facebook: https://www.facebook.com/tv9telugu ► Follow us on Twitter: https://twitter.com/Tv9Telugu ► Pin us on Pinterest: https://www.pinterest.com/Tv9telugu
Views: 953 TV9 Trending
Как сделать первое приложение в Microsoft Power BI - что такое курсы Power BI, анализ продаж
 
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Как работать с Microsoft Power BI? Как сделать первый dashboard в Power BI - информация, курсы, скачать Power BI можно на https://www.biconsult.ru Как сделать модель данных? Разработка отчетов и хранилища на MS Power BI. Уроки, материалы, учебные курсы по Microsoft Power BI power bi, power bi desktop, microsoft power bi, power bi скачать, power bi отчеты, power bi reports, power bi server, power bi report server, power bi excel, power bi обучение, ms power bi, dax power bi, power bi примеры, power bi pro, power bi desktop скачать, курс power bi, визуализации power bi, power bi на русском, аналитик power bi, power bi аналитика, power bi power query, power bi мера, power bi купить, r power bi, power bi analytics, power bi карта, power bi sql, интеграция power bi, power bi шлюз, power bi настройка, функции power bi, power bi бесплатно, power bi диаграммы, power bi формулы, power bi обновление, power bi sharepoint, power bi service, power bi table, power bi цена, дашборд power bi, power bi filter, power bi график, power bi сделать, программа power bi, power bi 1c, power bi download, power bi api, power bi для интернет маркетинга, power bi торрент, power bi google analytics, power bi метрика, отзывы power bi, power bi аналоги, power bi gateway, power bi на русском скачать, power bi директ, power bi возможности, power bi как работать, power bi стоимость, microsoft power bi desktop, power bi visuals, power bi map, power bi яндекс директ, power bi сравнение, power bi embedded, использование power bi, power bi презентация, power bi внедрение, power bi примеры отчетов, power bi установка, power bi dashboard, power bi to pdf, power bi premium, видео power bi, power bi вход, power bi и 1с, power bi torrent, power bi источники данных, power bi web, power bi уроки, power bi azure, power bi яндекс метрика, power bi уваров, power bi лицензии, power bi книга, power bi лицензирование, app power bi, power bi desktop на русском, power bi инструкция, microsoft power bi скачать, power bi учебник, power bi закладки, power bi скачать бесплатно, дата в power bi, power bi desktop на русском скачать, power bi sql server, power bi сквозная аналитика, power bi регистрация, power bi facebook, power bi облако, power bi update, power bi обновление данных, power bi учетная запись, power bi поиск, power bi создать таблицу, power bi reporting services, power bi форум, power bi blog, power bi параметры, profile powered by discuz bi basic, power bi локальный шлюз, power bi olap, power bi визуальные элементы, power bi com, power bi amocrm, bi quiet power, bi power shot, care glamour bi power, bi power keratin treatment, power bi шаблоны, майкрософт power bi, power bi python, power bi условное форматирование, power bi calculate, power bi курс скачать, power bi mobile, связи в power bi, power bi примеры дашбордов, панель мониторинга power bi, power bi публикация отчетов, power bi examples, power bi gallery, care glamour bi power keratin treatment, power bi measure, power bi office 365, power bi специалист, power bi report server лицензирование, как установить power bi, power bi svg, power bi для анализа продаж, power bi преимущества, power bi для интернет маркетинга скачать, сервер отчетов power bi, power bi r script, power bi online, power bi контекстная реклама, визуализации power bi скачать, power bi группировка, power bi on premise, руководство power bi, power bi уваров скачать, кнопки в power bi, kpi power bi, power bi сервис, power bi excel 2016, dax формулы power bi, power bi описание, обновить power bi, power bi visual gallery, power bi битрикс 24, fish power bi, power bi mysql, power bi postgresql, microsoft power bi pro, power bi desktop учебник, битрикс24 коннектор power bi, power bi visualization, power bi картинки, power bi карта россии, power bi desktop download, служба power bi, вакансии power bi, power bi википедия, работа в power bi, power bi mac os, comagic power bi, power bi для интернет маркетинга торрент, power bi svg map, power bi календарь, power bi vk, power bi обзор, курсы по power bi в москве, компании power bi, power bi concatenate, power bi настройка шлюза, power bi publisher, power bi видео уроки, power bi youtube, adwords power bi, power bi русский язык, сравнение периодов power bi, power bi report server скачать, безопасность в power bi, power bi онлайн, power bi детализация, интеграция битрикс24 с power bi, power bi для seo, power bi custom visuals, power bi обучение на русском, mcsa сертификат power bi
download torrents with Python and Scrapy
 
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download torrents with Python and Scrapy
Views: 1560 shefali Mahadevan
Processing tutorial: Overview of data visualization | lynda.com
 
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This Processing tutorial introduces the software and provides a background on the field of data visualization. Watch more at http://www.lynda.com/Processing-tutorials/Interactive-Data-Visualization-Processing/97578-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-dev-T5lRLA_Vn7o. This tutorial is a single movie from the first chapter of the Interactive Data Visualization with Processing course presented by lynda.com author Barton Poulson. The complete course is 7.75 hours long and shows how read, map, and illustrate data with Processing, an open-source drawing and development environment Introduction 1. Basics of Visualization 2. Basics of Processing 3. Basics of Drawing 4. Variables 5. Drawing Attributes 6. Dynamic Drawings 7. Interaction 8. Media 9. Grouping Code 10. Reading Data 11. Varieties of Data Visualizations 12. Elements of Design for Visualization 13. Elements of Interaction 14. Publishing and Sharing Conclusion
Views: 154522 LinkedIn Learning
Course Preview: Introduction to Data Visualization with Python
 
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View full course: https://www.pluralsight.com/courses/data-visualization-with-python-introduction Join Pluralsight author [NAME] as he/she walks you through a preview of his/her "COURSE TITLE" course found only on Pluralsight.com. Become smarter than yesterday with [AUTHORS]’s help by... Visit Pluralsight.com to start your free trial today to view this course in its entirety. Visit us at: Facebook: https://www.facebook.com/pluralsight Twitter: https://twitter.com/pluralsight Google+: https://plus.google.com/+pluralsight LinkedIn: https://www.linkedin.com/company/pluralsight Instagram: http://instagram.com/pluralsight Blog: https://www.pluralsight.com/blog
Views: 196 Pluralsight
Tableau как анализировать данные в программе как работать в Tableau первые отчеты в Tableau
 
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Анализ данных в Tableau, много обучающих материалов на http://www.biconsult.ru Ответы на вопросы по Tableau и русское коммьюнити https://tableau-forum.ru/ уроки, курсы, программа обучения Tableau, что такое Табло, скачать Tableau бесплатно tableau, tableau desktop, tableau public, https zen yandex ru tableau android, tableau software, tableau скачать, tableau 9.3, tableau server, tableau com, tableau widget, tableau torrent, tableau bi, tableau бесплатно, tableau desktop 10, yandex tableau widget, tableau online, tableau 9, tableau купить, tableau desktop torrent, spotfire tableau, tableau reader, tableau download, tableau crack, tableau desktop serial, tableau скачать бесплатно, tableau визуализация, tableau pro 9, tableau online how much viewers, работа tableau, tableau desktop professional edition, tableau free, tableau software скачать бесплатно, tableau software ключ,
Introduction to For Loops in Python (Python Tutorial #5)
 
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For loops Python tutorial. This entire series in a playlist: https://goo.gl/eVauVX Keep in touch on Facebook: https://www.facebook.com/entercsdojo Download the sample file: https://www.csdojo.io/python5 Subscribe to my newsletter: https://www.csdojo.io/news Support me on Patreon: https://www.patreon.com/csdojo
Views: 277342 CS Dojo
The Complete MATLAB Course: Beginner to Advanced!
 
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Get The Complete MATLAB Course Bundle for 1 on 1 help! https://josephdelgadillo.com/product/matlab-course-bundle/ Get the courses directly on Udemy! Go From Beginner to Pro with MATLAB! http://bit.ly/2v1e0lL Machine Learn Fundamentals with MATLAB! http://bit.ly/2v3sQs6 The Ultimate Guide for MATLAB App Development! http://bit.ly/2GOodDN MATLAB for Programming and Data Analysis! http://bit.ly/2IIwpWL Enroll in the FREE Teachable course! http://jtdigital.teachable.com/p/matlab Time Stamps 00:51 What is Matlab, how to download Matlab, and where to find help 07:52 Introduction to the Matlab basic syntax, command window, and working directory 18:35 Basic matrix arithmetic in Matlab including an overview of different operators 27:30 Learn the built in functions and constants and how to write your own functions 42:20 Solving linear equations using Matlab 53:33 For loops, while loops, and if statements 1:09:15 Exploring different types of data 1:20:27 Plotting data using the Fibonacci Sequence 1:30:45 Plots useful for data analysis 1:38:49 How to load and save data 1:46:46 Subplots, 3D plots, and labeling plots 1:55:35 Sound is a wave of air particles 2:05:33 Reversing a signal 2:12:57 The Fourier transform lets you view the frequency components of a signal 2:27:25 Fourier transform of a sine wave 2:35:14 Applying a low-pass filter to an audio stream 2:43:50 To store images in a computer you must sample the resolution 2:50:13 Basic image manipulation including how to flip images 2:57:29 Convolution allows you to blur an image 3:02:51 A Gaussian filter allows you reduce image noise and detail 3:08:55 Blur and edge detection using the Gaussian filter 3:16:39 Introduction to Matlab & probability 3:19:47 Measuring probability 3:26:53 Generating random values 3:35:40 Birthday paradox 3:43:25 Continuous variables 3:48:00 Mean and variance 3:55:24 Gaussian (normal) distribution 4:03:21 Test for normality 4:10:32 2 sample tests 4:16:28 Multivariate Gaussian
Views: 962626 Joseph Delgadillo
Big Data tutorial by Marko Grobelnik
 
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VideoLectures.Net View the talk in context: http://videolectures.net/eswc2012_grobelnik_big_data/ View the complete ESWC summer school: http://videolectures.net/eswc2012_summer_school/ View blog: http://blog.videolectures.net/deconstructing-big-data/. Speaker: Marko Grobelnik Artificial Intelligence Laboratory, Jožef Stefan Institute License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ Big data applies to information that can't be processed or analyzed using traditional processes or tools. IBM claims in its 2012 report that every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. 0:00 Big - Data Tutorial 0:35 Outline 1:18 Big data - a growing torrent 3:00 Big data - capturing its value 3:38 What is Big - Data? 4:41 Characterization of Big - Data 5:39 Big - Data popularity on the Web 7:47 Big - Data in Gartner Hype - Cycle 2011 9:05 Why Big - Data? 9:34 Enabler: Data storage 10:35 Enabler: Computation capacity 12:04 Enabler: Data availability 13:27 Type of available data 14:40 Data available from social networks and mobile devices 16:09 Data available from ''Internet of Things'' 17:43 Big - Data value chain 18:52 Gains from Big - Data per sector 21:54 Predicted lack of talent for Big - Data related technologies 23:16 Big - Data value chain 24:40 Tools 24:43 Types of tools typically used in Big - Data scenarios 27:02 Distributed infrastructure 28:31 Distributed processing 30:52 MapReduce 32:30 High - performance schema - free databases 35:31 Techniques 35:33 When Big - Data is really a hard problem? 38:39 What matters when dealing with data? 42:39 Meaningfulness of Analytic Answers (1/2) 43:58 Meaningfulness of Analytic Answers (2/2) 48:09 What are ''atypical'' operators on Big - Data 51:12 Analytical operators on Big - Data 51:28 What are ''atypical'' operators on Big - Data 53:06 Analytical operators on Big - Data 53:51 ...guide to Big - Data algorithmics 54:34 Applications 54:46 Application: Recommendation 55:47 The context of each click on the web site used for recommendation 56:33 Application: Online Advertising for NYTimes 56:59 Scale of one day NYTimes data 57:21 Application: Telecommunication Network Monitoring 59:07 Application: Monitoring global main stream news 59:12 http://newsfeed.ijs.si/ 59:51 Semantic text enrichment (DBpedia, OpenCyc, ...) with Enrycher 60:10 Application: Text visualization 60:15 Application: Analysis of MSN - Messenger Social - network 61:00 Data Statistic: Total activity 61:50 Who talks to whom: Number of conversations 62:33 Who talks to whom: Conversation duration 63:14 Geography and communication 63:48 How is Europe talking 64:10 Network: Small - word 66:28 Literature on Big - Data 67:42 ...to conclude
Views: 7963 VideoLecturesChannel
Data Science Tutorial for Beginners - 1 | What is Data Science? | Data Analytics Tools | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) Data Science Blog Series: https://goo.gl/1CKTyN http://www.edureka.co/data-science Please write back to us at [email protected] or call us at +91-8880862004 for more information. Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy. Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics. Data science is a steadily growing discipline that is driving significant changes across industries and in companies of every size. It is emerging as a critical source for insights for enterprises dealing with massive amounts of data. About the Data Science Course at edureka! - This Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study. Course Objectives After the completion of the Data science Course at Edureka, you should be able to: Gain an insight into the 'Roles' played by a Data Scientist. Analyse Big Data using Hadoop and R. Understand the Data Analysis Life Cycle. Use tools such as 'Sqoop' and 'Flume' for acquiring data in Hadoop Cluster. Acquire data with different file formats like JSON, XML, CSV and Binary. Learn tools and techniques for sampling and filtering data, and data transformation. Understand techniques of Natural Language Processing and Text Analysis. Statistically analyse and explore data using R. Create predictive using Hadoop Mappers and Reducers. Understand various Machine Learning Techniques and their implementation these using Apache Mahout. Gain insight into the visualisation and optimisation of data. Who should go for this course? This course is designed for all those who want to learn machine learning techniques and wish to apply these techniques on Big Data. The course is amalgamation of two powerful open source tools: 'R' language and Hadoop software framework. You will learn how to explore data quantitatively using tools like Sqoop and Flume, write Hadoop MapReduce Jobs, perform Text Analysis and implement Language Processing, learn Machine Learning techniques using Mahout, and optimize and visualise the results using programming language 'R' and Apache Mahout. This course is for you if you are: A SAS, SPSS Analytics Professional. A Hadoop Professional working on Database management and streaming of Big Data. An 'R' professional who wants to apply Statistical techniques on Big Data. A Statistician who wants to understand Data Science methodologies to implement the statistics methods and techniques on Big data. Any Business Analyst who is working on creating reports and dashboards. Pre-requisites Some of the prerequisites for learning Data Science are familiarity with Hadoop, Machine Learning and knowledge of R (recommended not mandatory as these concepts will also be covered during the course). Also, having a statistical background will be an added advantage. Why Learn Data Science? 'Data Science' is a term which came into popularity in past decade. Data Science is the process of extracting valuable insights from "data". It is the right time to learn Data science because: We are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. A data scientist has a dual role -- that of an "Analyst" as well as that of an "Artist"! Data scientists are very curious, who love large amount of data, and more than that, they love to play with such huge data to reach important inferences and spot trends. You could be one of them! As 'Data Science' is an emerging field, there is a plethora of opportunities available world across. Just browse through any of the job portals; you will be taken aback by the number of job openings available for Data scientists in different industries, whether it is IT or healthcare, Retail or Government offices or Academics, Life Sciences, Oceanography, etc. Read this blog post on Data Science to know more. http://www.edureka.co/blog/who-is-a-data-scientist/
Views: 208655 edureka!
How to build Interactive Excel Dashboards
 
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Download file used in the video with step by step instructions and links to more tutorials: https://www.myonlinetraininghub.com/workbook-downloads In this video you will learn how to create an interactive dashboard from scratch using the built in Excel tools. No add-ins or VBA/Macros. Just plain Excel. Applies to Excel 2007 onward for Windows & Excel 2016 onward for Mac. Subscribe to my free newsletter and get my 100 Tips & Tricks eBook here: https://www.myonlinetraininghub.com/sign-up-for-100-excel-tips-and-tricks
Views: 1561438 MyOnlineTrainingHub
The Python Mega Course To Learn How To Write Python Code and Build 10 Real World Applications
 
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http://bit.ly/PythonOnlineCourse The Python Mega Course To Learn How To Write Python Code and Build 10 Real World Applications. The Python Mega Course: Build 10 Real World Applications The only Python course covering web, databases, web scraping, data science, web visualizations, image processing & more! This is not just another Python tutorial that shows how to write Python code. This is a carefully designed course that will train you to develop real life applications with Python. Through a combination of videos, real world code examples, quizzes, exercises, and a final project, this course makes sure you are able to think Python, and design and build real world applications by the end of it. After you buy the course, you will have lifetime access to it and to the course cheat sheet ebook containing all the code consumed throughout the course. You can use that book for quick look-up of Python commands. The course is designed for all student levels. The first 5% of the course teaches Python basics for beginners and can serve as a refresher crash course for post-beginner students. After completing the first 5%, you will be guided in building 10 real world applications in a wide range of areas that include: Web applications Desktop applications Database applications Web scraping Web mapping Data analysis Interactive web visualization Computer vision for image and video processing Object Oriented Programming By the end of the course you will have built 10 useful applications in the above areas. The applications you are going to build are as follows: A name generator A website URL timed blocker A web map generator A portfolio website with Flask A GUI-based desktop application A webcam motion detector A web scraper of property An interactive web-based financial chart A data collector web application A geocoding web service. What will you get from this course?: Become a Python professional able to independently develop complex applications on Python 3. Build 10 real world Python applications in a wide range of areas. Use Python for building web applications with Flask. Use Python for building desktop applications with Tkinter. Use Python for database applications. Use Python for scientific computing with Numpy. Use Python for data analysis and interactive data visualizations with Pandas and Bokeh. Use Python for building interactive web maps with Folium. Use Python for scraping data from websites with Beautiful Soup. Use Python for computer vision with OpenCV. Use Python for sending automated emails with Smtplib. Use Python to download data from various data service APIs. Use Python to analyze and visualize stock market data. Use Python for batch geocoding of addresses with Geopy. Use Python to schedule programs based on computer events. Use and fully understand object-oriented design. To Take The Course Please Visit: http://bit.ly/PythonOnlineCourse
Views: 579 Renata Diva
Using IGV Browser for Variants and Next Generation Sequencing (Part 1)
 
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This video demonstrates how to use IGV browser for a custom genome and dataset, how to use the launch tool, how to import a genome and annotations, and how to import a dataset. Also shown: how to navigate, search, and annotate regions of interest.
Views: 17508 NIAID Bioinformatics
Sampling & Probability | Learning Statistics: Concepts and Applications in R | The Great Courses
 
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Study sampling and probability, which are key aspects of how statistics handles the uncertainty inherent in all data. See how sampling aims for genuine randomness in the gathering of data, and probability provides the tools for calculating the likelihood of a given event based on that data. Solve a range of problems in probability, including a case of medical diagnosis that involves the application of Bayes’ theorem. This free full-length lecture comes from the course Learning Statistics: Concepts and Applications in R. Learn more about this course and start your FREE trial of The Great Courses Plus here: https://www.thegreatcoursesplus.com/show/learning_statistics_concepts_and_applications_in_r?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=151291 About this course: “Show me the data!” This is coin of the realm in science, medicine, business, education, journalism, and countless other fields. Of course, it’s more complicated than that, because raw data without interpretation is useless. What they mean is “Show me the statistics”—well-founded, persuasive distillations of data that support a claim under discussion. The ability of statistics to extract insights from a random collection of facts is one of the most astonishing and useful feats of applied mathematics. That power is all the more accessible today through the statistical programming language R, a free, open-source computer language with millions of users worldwide—everyone from students and nonprofessionals to managers and researchers at the forefront of their disciplines. In this era of big data, with a solid understanding of statistics and the tools for interpreting data, you don’t have to trust someone else’s analysis of medical treatments, financial returns, crop yields, voting trends, home prices, or any other interpretation of data. You can do it yourself. Designed for those who appreciate math or want an introduction to an essential toolkit for thinking about the uncertainty inherent in all sorts of information, Learning Statistics: Concepts and Applications in R teaches you elementary statistical methods and how to apply them in R, which is made even more powerful when combined with the user interface of RStudio. (Both R and RStudio are free and downloadable for multiple platforms.) In 24 challenging and in-depth half-hour lectures, award-winning Professor Talithia Williams of Harvey Mudd College walks you through major concepts of an introductory college-level statistics course, and beyond, using examples developed and presented in R. Compared with “canned” statistics packages, R brings users into a more hands-on, mind-engaging approach that is becoming the standard at top-tier statistics programs throughout the country. An Associate Professor of Mathematics and the Associate Dean for Research and Experiential Learning at Harvey Mudd, Dr. Williams is a nationally recognized innovator in statistics education, noted for her popular TED Talk, “Own Your Body’s Data,” and she is cohost of the PBS NOVA series NOVA Wonders. Learn more about this course and start your FREE trial of The Great Courses Plus here: https://www.thegreatcoursesplus.com/show/learning_statistics_concepts_and_applications_in_r?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=151291
Complex Network Analysis in Python: Recognize → Construct → Visualize → Analyze → Interpret
 
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Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive—such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.
Views: 1537 PragProg
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn
 
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This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work. Below topics are explained in this Decision tree in R tutorial : 1. What is Decision tree? 2. What problems can be solved using Decision Trees? 3. How does a Decision Tree work? 4. Use case: Survival prediction in R Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/WsM21R Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-in-R-HmEPCEXn-ZM&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 1342 Simplilearn
Central Analysis Server (Technical/Analysis) - Mark DePristo
 
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June 5-6, 2012 - Establishing a Central Resource of Data from Genome Sequencing Projects Workshop More: http://www.genome.gov/27549169
Genome Assembly Scaffold Bridging Visualization
 
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Bridging of genome assembly scaffolds with long PacBio reads. Red nodes: mitochondrial genome scaffolds; blue nodes: PacBio reads. Lines connect DNA sequences with 1200 nt overlap or longer. Visualized with PhyloGrapher http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html
Views: 424 Alexander Kozik
SARscape Tools for ArcGIS - набор инструментов для работы
 
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Дата проведения: 25.10.2012 г. Ведущий: Кантемиров Юрий Игоревич
Views: 626 SOVZOND
Bioinformatics Core
 
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Description This tool recursively align sequenced files to any number of index files in given order. When the sequenceces are aligned to an index file, the rest of the sequences are going to align to the next index file. For each alignment, the pipeline produces a stats file, an alignment file, quant file for each fasta sequence of given index file and the rest of the data. Input Dir: Full path of the input directory that includes the libraries. Input Params: Input files are given in two or three columns for the libraries. If the files are paired end enter three columns. First column is the name of the library, second column is the location of the first pair and third is the the second pair. If there is single end libraries, use two columns. The name of the library and the location of the file in the cluster. You can use 'DIR' keyword to denote 'Input Dir' to prevent repetition of entering full path for each libraries. If you give the same name for more than one library, the pipeline will merge them. Index File Full Path: This is the index file that is prepared using bowtie-build. It requires the fullpath and the name of the index file. without extensions. Name of the step: The name of the index file. This name will be used generated outputs. Ex:outdir/libname.indexname.stats Bowtie Parameters: Bowtie parameters for this individual step. Description: This is the description of the step. This naming will be used to produce summary file and calculation of the total counts to mapped to this step. Filter Out: To filter out the reads and move on to next step. This should be selected yes. If it is no, the sequences will be used without filtered out in the next step. So, if this step selected no, there can be overcount in summary tables.
Phase 1 - Recon - Part3 Appreciating Hacking Tools
 
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Before we start off on our journey of learning how to pentest a network, we need to gain an understanding of what it takes to create a pen test tool. We should not strive to be script kiddies, but understand and appreciate the tools that get created for us by some very clever people. The first few videos in this series will focus on creating a recon tool that scans a website and extracts email addresses. Very easy to do, but considering the small footprint we are covering it should show you just how much more is required to create something useful for the communit baring in mind that this is only one phase of pentesting that requires tools, there are many more areas where awesome tools are created. The purpose of these introductory videos will to, hopefully, help you to see just what it really takes to create great tools, make sure you atleast get the concept behind the recon tools we will use on Kali, instead of just running something and getting a result... and entice you to join the community in a more productive manner. Some of the Links I used to code the python-webcrawler.py program: http://null-byte.wonderhowto.com/inspiration/basic-website-crawler-python-12-lines-code-0132785/ http://jakeaustwick.me/python-web-scraping-resource/ http://scraping.pro/simple-email-crawler-python/ http://docs.python-guide.org/en/latest/scenarios/scrape/ https://www.crummy.com/software/BeautifulSoup/bs4/doc/ http://www.pythonforbeginners.com/beautifulsoup/scraping-websites-with-beautifulsoup http://resources.infosecinstitute.com/search-engine-hacking-manual-and-automation/ Disclaimer: You learn and use the information in these videos at your own discretion. You are responsible for anything you do with the information you learn in these videos. I am not responsible for any choices you make! Just be smart okay. own up to your own shitty mistakes that you choose to make. Code from the Video: #!/usr/bin/python from bs4 import BeautifulSoup import sys import re import requests # Lets create a class and function relevant to this application class MyWebPageCrawler(): 'Custom Webpage Crawler' # Globals DOMAINTOSEARCH = "" # Constructor # Functions # Get user input. (make sure to do the correct checks on user input !) def getDomainFromUser(self): global DOMAINTOSEARCH DOMAINTOSEARCH = raw_input("Enter the link: ") # call openUrl Function to open a URL ( we will get to this now) soup = self.openUrl(DOMAINTOSEARCH) return soup # Open the URL requested from the user def openUrl(self, urlToOpen): # Call requests to get the page page = requests.get(urlToOpen) # Convert the page into something BS4 can understand soup = BeautifulSoup("".join(page), 'html.parser') return soup # Find the links on the page def findLinks(self, soup, domainToSearch): print "[*][*] Please wait. Searching Site for links..." # find all links using the soup variable we created from our page allHref = soup.find_all("a") hrefList = [] for href in allHref: # get the link text from the actual "a" tag href = href.get('href') # add it to the list hrefList.append(href) # Helps if you print the results to screen print href return hrefList # Find all the emails on each link def findEmails(self, hrefList): print "[*][*] Please wait. Finding Emails...." for href in hrefList: page = self.openUrl(href) allEmails = set(re.findall( r"[a-z0-9\.\-+_][email protected][a-z0-9\.\-+_]+\.[a-z]+", page.text, re.I )) print "[*][*] -: Seaching Links Complete...." print "[*][*] -: Printing matched Regular Expressions" for email in set(allEmails): print email # put all your code here in main def main(self): # Get user input soup = self.getDomainFromUser() # find links from users input linksFound = self.findLinks(soup, DOMAINTOSEARCH) # find emails from links self.findEmails(linksFound) # Some basic stuff here calling main if __name__=='__main__': crawler = MyWebPageCrawler() crawler.main() sys.exit
Python Processing Magnet Part III
 
07:19
Recorded with http://screencast-o-matic.com
Views: 23 Christopher Roche
Internet Traffic Graph: #1 Way To Promote And Boost Web Traffic
 
01:10
Need Help promoting your website link? http://www.SpinSuccess3.com SpinSuccess is the #1 method of marketing Website links and any other url that needs traffic, sales and signups. http://www.SpinSuccess3.com Internet Traffic Graph
CPLEX Asignación en un Cine
 
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Un video que resume un proyecto realizado por tres estudiantes industriales de tercer año en el ramo de metodos de optimizacion. En resumen aplicamos CPLEX para encontrar la asignación optima de 7 películas que se encuentran en el cine (2012) , y considerando que tenemos 5 salas de normales y 4 3D. También las salas poseen distintas capacidades. Profesora de asignatura: Lorena Pradena Integrantes del grupo: Alexis Salas Cruz Leonardo Ortiz Germán González Produccion de video: Germán González Musica: Basespañola - Germán González ( http://soundcloud.com/studiocanelos) Gracias !
Views: 114 snowsk8life
Matplotlib Tutorial 4 - Scatter Plots
 
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Next up, we cover scatter plots! The idea of scatter plots is usually to compare two variables, or three if you are plotting in 3 dimensions, looking for correlation or groups. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 102771 sentdex
Programming for Robotics (ROS) Course 1
 
54:24
The slides are available here: https://www.ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/ROS2017/lecture1.pdf The recording of this course is part of the Programming for Robotics (ROS) Lecture at ETH Zurich: http://www.rsl.ethz.ch/education-students/lectures/ros.html Lecturers: Péter Fankhauser, Dominic Jud, Martin Wermelinger Course 1 covers following topics: – ROS architecture & philosophy – ROS master, nodes, and topics – Console commands – Catkin workspace and build system – Launch-files – Gazebo simulator About the course: This course gives an introduction to the Robot Operating System (ROS) including many of the available tools that are commonly used in robotics. With the help of different examples, the course should provide a good starting point for students to work with robots. They learn how to create software including simulation, to interface sensors and actuators, and to integrate control algorithms. Objective: – ROS architecture: Master, nodes, topics, messages, services, parameters and actions – Console commands: Navigating and analyzing the ROS system and the catkin workspace – Creating ROS packages: Structure, launch-files, and best practices – ROS C++ client library (roscpp): Creating your own ROS C++ programs – Simulating with ROS: Gazebo simulator, robot models (URDF) and simulation environments (SDF) – Working with visualizations (RViz) and user interface tools (rqt) – Inside ROS: TF transformation system, time, bags
Views: 124446 Robotic Systems Lab
Webinar: Introducing the New Python Integration Toolkit for LabVIEW from Enthought
 
45:28
LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. With the release of Enthought's new Python Integration Toolkit for LabVIEW, LabVIEW users can now have a “bridge” between the LabVIEW and Python environments. This webinar illustrates how the Python-LabVIEW "bridge" works, as well as examples of how you can extend LabVIEW with Python, such as using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more. Learn more: https://www.enthought.com/python-for-labview/
Views: 5276 Enthought
Tutorial - An Introduction to Data Visualization with Python
 
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Access +100 programming courses in Zenva: https://academy.zenva.com/?zva_src=youtube This course will provide you with an introduction to Data Visualization through Python. We’ll cover different techniques that will allow us to visualize data using Matplotlib. The course begins with an introduction to statistics — which we’ll need to understand some of the plots taught later. Following that, we’ll move on to learning about several types of plots that should cover most use-cases. Types of plots that will be covered in this course include Bar charts Line plots Scatter plots Advanced plots such as Quiver plots, 3D lines, and 3D surfaces Subplots Our tutorial blogs: GameDev Academy: https://gamedevacademy.org HTML5 Hive: https://html5hive.org Android Kennel: https://androidkennel.org Swift Ludus: https://swiftludus.org De Idea A App: https://deideaaapp.org Twitter: @ZenvaTweets
Views: 3099 Zenva
Fun and Easy Machine Learning Course in Keras and Python (Coupon Code in Description)
 
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Fun and Easy Machine Learning Course in Keras and Python Promotional Video (Coupon Code in Description) https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing world of Machine Learning. Each section consists of fun and intriguing white board explanations like this one with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. So who are we to teach you, well my Name is Ritesh Kanjee and I have a Masters Degree in Electronic engineering majoring in computer and machine vision. I have over 26000 students on Udemy teaching people from 128 countries around the world. I will be teaching you the theoretic side of Machine Learning On the Practical Side, Minerva Singh is a Bestselling Udemy Instructor & Data Scientist with a PhD from Cambridge University. Minerva is proficient in statistical analysis, machine learning and data mining. She also enjoys general programming, data visualization and web development. So you can see that you will be taught Machine learning by two qualified professionals. So We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretical lecture is uniquely designed using whiteboard animations like this which can maximise concentration and engagement in the lectures which improves knowledge retention. This ensures that you absorb as much of the content than you would traditionally would watching other videos and or reading books on this topic. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 16972 Augmented Startups
Tutorial: Applied Data Science in Python
 
01:26:40
Tennessee Leeuwenburg https://linux.conf.au/schedule/30020/view_talk Ever tried to get into data science or machine learning, but struggled with getting your tech stack working, or found the maths off-putting? Curious about the limits of what your laptop or desktop really are when it comes to Big Data and predictive analytics? Ever wondered if these tools were really accessible to a general developer? This tutorial will provide attendees with a walkthrough on getting set up for this work, and an overview of a good tech stack / software ecosystem for beginning work. We'll cover some of the standard data sets in machine learning, and how to apply interesting algorithms. Random Forests and neural networks will be included, but with a minimum of fuss and jargon. There will be a focus on the application of technology, with only the most relevant theoretical aspects included. This is about actually getting things done. This tutorial would be suitable for intermediate developers of any background, or experienced developers who would like an introduction to data science that gets to the point fast. Prerequisites: the ability to install Python modules on your laptop, the ability to set up a new virtual environment, and an interest in applying new techniques. The tutorial will include clear walkthroughs, as well as allowing adequate time for discussion and individual learning. Please contact Tennessee via email ahead of time if you would like to get a head start on setting up your environment -- this may help you get more out of the tutorial.
Webinar: Using Python and LabVIEW to Rapidly Solve Engineering Problems | Enthought
 
45:26
Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software. But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive. View the live webinar and demo, as we take a LabVIEW data acquisition app and extend it with Python’s machine learning capabilities, to automatically detect and classify equipment vibration. Using a modern Python platform and the Python Integration Toolkit for LabVIEW (https://www.enthought.com/python-for-labview), we’ll show how easy and fast it is to install heavy-hitting Python analysis libraries, take advantage of them from live LabVIEW code, and finally deploy the entire solution, Python included, using LabVIEW Application Builder. In this webinar we'll demonstrate: *How Python’s machine learning libraries can simplify a hard engineering problem *How to extend an existing LabVIEW VI using Python analysis libraries *How to quickly bundle Python and LabVIEW code into an installable app
Views: 8506 Enthought
what is appliedaicourse.com?
 
05:48
visit our website at https://www.appliedaicourse.com/
Views: 10438 Applied AI Course
Analyze Stock Data with Microsoft Excel
 
17:50
Visualization of data is a powerful method to see trends and make decisions. Microsoft Excel trending capabilities are tools to visualize large data sets, such as financial information on company performance.
Views: 13999 APMonitor.com
New Python tutorial: Introduction to Deep Learning
 
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Try this course and master the basics of deep learning in python: https://www.datacamp.com/courses/deep-learning-in-python About the course: Artificial neural networks (ANN) are a biologically-inspired set of models that facilitate computers learning from observed data. Deep learning is a set of algorithms that use especially powerful neural networks. It is one of the hottest fields in data science, and most state-of-the-art results in robotics, image recognition and artificial intelligence (including the famous AlphaGo) use deep learning. In this course, you'll gain hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python. Transcript: Imagine you work for a bank, and you need to build a model predicting how many transactions each customer will make next year. You have predictive data or features like each customer’s age, bank balance, whether they are retired and so on. We'll get to deep learning in a moment, but for comparison, consider how a simple linear regression model works for this problem. The linear regression embeds an assumption that the outcome, in this case how many transactions a user makes, is the sum of individual parts. It starts by saying, "what is the average?" Then it adds the effect of age. Then the effect of bank balance. And so on. So the linear regression model isn't identifying the interactions between these parts, and how they affect banking activity. Say we plot predictions from this model. We draw one line with the predictions for retired people, and another with the predictions for those still working. We put current bank balance on the horizontal axis, and the vertical axis is the predicted number of transactions. The left graph shows predictions from a model with no interactions. In that model we simply add up the effect of the retirement status, and current bank balance. The lack of interactions is reflected by both lines being parallel. That's probably unrealistic, but it's an assumption of the linear regression model. The graph on the right shows the predictions from a model that allows interactions, and the lines don't need to be parallel. Neural networks are a powerful modeling approach that accounts for interactions like this especially well. Deep learning, the focus of this course, is the use of especially powerful neural networks. Because deep learning models account for these types of interactions so well, they perform great on most prediction problems you've seen before. But their ability to capture extremely complex interactions also allow them to do amazing things with text, images, videos, audio, source code and almost anything else you could imagine doing data science with. The first two chapters of this course focus on conceptual knowledge about deep learning. This part will be hard, but it will prepare you to debug and tune deep learning models on conventional prediction problems, and it will lay the foundation for progressing towards those new and exciting applications. You'll see this pay off in the third and fourth chapter. You will write code that looks like this, to build and tune deep learning models using keras, to solve many of the same modeling problems you might have previously solved with scikit-learn. As a start to how deep learning models capture interactions and achieve these amazing results, we'll modify the diagram you saw a moment ago. Here there is an interaction between retirement status and bank balance. Instead of having them separately affect the outcome, we calculate a function of these variables that accounts for their interaction, and use that to predict the outcome. Even this graphic oversimplifies reality, where most things interact with each in some way, and real neural network models account for far more interactions. So the diagram for a simple neural network looks like this: On the far left, we have something called an input layer. This represents our predictive features like age or income. On the far right we have the output layer. The prediction from our model, in this case, the predicted number of transactions. All layers that are not the input or output layers are called hidden layers. They are called hidden layers because, while the inputs and outputs correspond to visible things that happened in the world, and they can be stored as data, the values in the hidden layer aren't something we have data about, or anything we observe directly from the world. Nevertheless, each dot, called a node, in the hidden layer, represents an aggregation of information from our input data, and each node adds to the model's ability to capture interactions. So the more nodes we have, the more interactions we can capture.
Views: 10160 DataCamp
Learn to connect: PowerApps Excel Spreadsheet hosted in OneDrive
 
22:00
Do you want to learn to use PowerApps Excel Spreadsheets? Then this video is for you. We take an Excel workbook hosted in OneDrive for Business and use it as a data source for PowerApps. This video is the foundation that will let us explore more complicated scenarios in the future. PowerApps for SharePoint https://www.youtube.com/watch?v=BnYe_7fpZRM PowerApps Playlist https://www.youtube.com/playlist?list=PLCGGtLsUjhm2bonhBZuEhZU72QkFjOpc6
Views: 53030 Shane Young

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