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Views: 27519 Alex Stoykov

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Learn Python Programming - 3 - Data Mining with Python In this video we will learn to code a program which grabs the data which is saved in a excel file. This code will be coded with python. By Kovid Raj Panthy (Coder Kovid)
Views: 3198 Coder Kovid

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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 326678 CS Dojo

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There is an abundance of data in social media sites (Wikipedia, Facebook, Instagram, etc.) which can be accessed through web APIs. But how do we know that the data from the Wikipedia article on "Golden Gate Bridge" goes along with the data from "Golden Gate Bridge" Facebook page? This represents an important question about integrating data from various sources. In this talk, I'll outline important aspects of structured data mining, integration and entity resolution methods in a scalable system.
Views: 5694 PyTexas

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This tutorial introduces users to Python for data science. From data cleaning to model building, we will work through a series of short examples together using some real-world health inspection data. Attendees will have their hands on the keyboard, using the Python standard library and pandas to clean data and scikit-learn to build some models. Speaker: Skipper Seabold Originally Published at PYCON 2018 Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides Please Subscribe my channel to motivate me . Subscribe Our Channel : https://goo.gl/BlFui4 For Fun - https://www.youtube.com/channel/UC6i0... Connect with us on social media: Facebook: https://www.facebook.com/mtechviral Pawan Kumar - https://www.facebook.com/imthepk Ask Pawan Kumar - https://www.facebook.com/thepawankumaar Instagram - https://instagram.com/codepur_ka_superhero Twitter: https://twitter.com/imthepk LIKE | SHARE | SUBSCRIBE FOR MORE VIDEOS LIKE THIS THANKS FOR WATCHING!
Views: 34262 MTECHVIRAL

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https://github.com/mnd-af/src/blob/master/2017/06/04/Uber%20Data%20Analysis.ipynb
Views: 37305 MandarinaCS

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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: 4703 Dennis Lal

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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 35564 Packt Video

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How can we get started with data analysis or data science - so for example read and change data and also create our first quick chart - in Python? Besides Python, all we need is Pandas and Matplotlib. Doesn't sound familiar to you? Let's clear things up and get started in this video! ---------- Learn Python from scratch: https://www.udemy.com/learn-python-by-building-a-blockchain-cryptocurrency/?couponCode=ACAD_M Download the source file: https://a.storyblok.com/f/42126/x/279bce29a2/revenue-profit.csv Want to learn something totally different? Check out all other courses: https://academind.com/learn/our-courses ---------- • You can follow Max on Twitter (@maxedapps). • And you should of course also follow @academind_real. • You can also find us on Facebook.(https://www.facebook.com/academindchannel/) • Or visit our Website (https://www.academind.com) and subscribe to our newsletter! See you in the videos! ---------- Academind is your source for online education in the areas of web development, frontend web development, backend web development, programming, coding and data science! No matter if you are looking for a tutorial, a course, a crash course, an introduction, an online tutorial or any related video, we try our best to offer you the content you are looking for. Our topics include Angular, React, Vue, Html, CSS, JavaScript, TypeScript, Redux, Nuxt.js, RxJs, Bootstrap, Laravel, Node.js, Progressive Web Apps (PWA), Ionic, React Native, Regular Expressions (RegEx), Stencil, Power BI, Amazon Web Services (AWS), Firebase or other topics, make sure to have a look at this channel or at academind.com to find the learning resource of your choice!

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Views: 606521 Siraj Raval

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Views: 529577 Siraj Raval

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Views: 287979 Siraj Raval

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Bilal Aslam (bilalaslam.com) gives an overview of data mining with Python at the Eastside Incubator (www.EastsideIncubator.com). Not the greatest sound quality, but if you crank it you can hear!
Views: 9582 Eastsi Incub

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A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 192190 APMonitor.com

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Views: 19215 Sukhvinder Singh

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Python untuk Data Mining | Python for Data Mining Tutorial 1 Data Mining menggunakan bahasa pemrograman Python. Dari Mulai: 1. Alasan Kenapa menggunakan Python 2. Machine Learning Library di Python 3. Komponen bahasa pemrograman Python 4. Virtual environment di Python 5. Dasar-dasar pemrograman Python 6. Numpy, scipy, pandas, matplotlib 7. Data Mining dengan Python 8. Analisis data menggunakan Python 9. Dan sebagainya #Python #DataMining #MachineLearning #DataAnalysis #scikit-learn
Views: 4916 Rischan Mafrur

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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 85570 edureka!

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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 74170 Augmented Startups

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Intro to video tutorial series for Mining Data from Social Media with Python ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 13326 Sukhvinder Singh

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Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 19579 Engineers.SG

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Views: 61038 edureka!

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**Información complementaria** 1) El IDE usado es ANACONDA (Una vez instalado ANACONDA ya tienes todas las herramientas de python, no es necesario instalar python aparte) 2) En mi caso tengo S.O. Mac, tuve que guardar los documentos *.txt como UTF-8, si se guardan en la codificación por defecto la información se guarda como 0's y 1's por tanto el programa aquí mostrado no funcionará. Link de programa del video: http://clasesdesarrollosoftware.blogspot.mx/2016/10/programa-en-python-sobre-data-mining.html Contacto: Facebook: Ulyses Rico Rea Twitter: @ulysesricord
Views: 9891 Software development

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Views: 29294 Amit Sharma

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In this Data Mining Example Tutorial, we learn how to clean our data set using Python and Pandas. We clean Billboard data set by headly. we perform several python data cleaning operations on our Data set which is csv file. This will be the best pandas tutorial in data science you will have ever watched. 🔷🔷🔷🔷🔷🔷🔷 Jupyter NOtebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 2405 TheEngineeringWorld

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Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 204101 Hitesh Choudhary

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Social media crawler/scrapper that dumps images, tweets, captions, external links and hashtags from Instagram and Twitter in an organized form. It also shows the most relevant hashtags with their frequency of occurrence in the posts. Github Link https://github.com/the-javapocalypse/Social-Media-Scrapper/blob/master/README.md Twitter App https://apps.twitter.com/ Please Subscribe! And like. And comment. That's what keeps me going. Follow Me Facebook: https://www.facebook.com/javapocalypse Instagram: https://www.instagram.com/javapocalypse
Views: 3748 Javapocalypse

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Introduction to Data Mining and Text Mining - Part 2 - Python Introduction - Anaconda Installation (Data Science Distribution of Python) - Jupyter Introduction (Next Generation Engineering Notebook) “Hello World!” in Jupyter, and so on. by Kanda Tiwatthanont (Phawattanakul)
Views: 1968 Kanda

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Web scraping is a very powerful tool to learn for any data professional. With web scraping the entire internet becomes your database. In this tutorial we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup. In this example, we web scrape graphics cards from NewEgg.com. Sublime: https://www.sublimetext.com/3 Anaconda: https://www.anaconda.com/distribution/#download-section If you are not seeing the command line, follow this tutorial: https://www.tenforums.com/tutorials/72024-open-command-window-here-add-windows-10-a.html -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5HN0 Watch the latest video tutorials here: https://hubs.ly/H0hz5SV0 See what our past attendees are saying here: https://hubs.ly/H0hz5K20 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo #webscraping #python
Views: 560537 Data Science Dojo

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Welcome! “Mastering Data Analysis With Python Pandas & Matplotlib 2018” is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field.Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.Mastering Data Analysis With Python Pandas & Matplotlib 2018 offers in-depth video tutorials in which we’ll dive into tons of different datasets, short and long, broken and pristine. I’ll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! . tutorials include: Installing. Creating. Accessing. Applying arithmetic operations. Reindexing. Slicing. Tidying up. Handling missing data. Handling duplicated data. Concatenating. Grouping. Aggregating. deleting. visualizing.
Views: 19267 Tech Giant

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This data cleaning tutorial will introduce you to Python's Pandas Library in 2018. Check out our website for the best Data Science tips in 2018: https://www.dataoptimal.com Subscribe for even more Data Science tutorials! https://bit.ly/2J2O5N8 Follow us on Twitter! https://twitter.com/DataOptimal **Video Resources** Full article: https://www.dataoptimal.com/data-cleaning-with-python-2018/ Dataset: https://github.com/dataoptimal/videos/tree/master/cleaning%20messy%20data%20with%20pandas Pandas link: http://pandas.pydata.org/pandas-docs/version/0.21/indexing.html#indexing-label Error handling in Python: https://docs.python.org/3/tutorial/errors.html Matt Brems material on missing values: https://github.com/matthewbrems/ODSC-missing-data-may-18/blob/master/Analysis%20with%20Missing%20Data.pdf It's the start of a new project and you're excited to apply some machine learning models. You take a look at the data and quickly realize it's an absolute mess. According to IBM Data Analytics you can expect to spend up to 80% of your time on a project cleaning data. There's all different types of messy data, but today we're going to focus on one of the most common, missing values. We'll take a look at standard types that Pandas recognizes out of the box. Next we'll take a look at some non-standard types. These are inputs that Pandas won't automatically recognize as missing values. After that we'll take a look at unexpected types. Let's say you have a column of names that contains a 12, technically that's a missing value. After we've finished detecting missing values we'll learn how to summarize and do simple replacements.
Views: 14245 DataOptimal

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Views: 51696 LucidProgramming

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Views: 53392 edureka!

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Views: 11924 Tukang Leding

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In order to interact with the Twitter APIs, we need a Python client that implements the different calls to the APIs itself. We will use Tweepy in these tutorials and see how to build our application in multiple parts to collects data from our own Twitter timeline and other users timeline. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 10658 Sukhvinder Singh

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Views: 190925 edureka!

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Data Cleaning In Python with Pandas In this tutorial we will see some practical issues we have when working with data,how to diagnose them and how to solve them. ==Tutorial and Data Set here== Github: https://goo.gl/erg89C Blog: https://goo.gl/6PJsdo Reference ====Common Data Cleaning Issues==== Reading File Inconsistent Column Names Missing Data Duplicates Inconsistent Data Types Outliers Noisy Data etc.
Views: 17223 J-Secur1ty

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Views: 4595 Anurag P

09:28:18
Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions. Section 1: Introduction Lecture 1 About the course 08:42 Lecture 2 About V2 Maestros 01:39 Lecture 3 Resource Bundle Article Section 2: Overview Lecture 4 Hadoop Overview 10:06 Lecture 5 HDFS Architecture 14:46 Lecture 6 Map Reduce - How it works 17:24 Lecture 7 Map Reduce - Example 16:46 Lecture 8 Hadoop Stack 06:27 Lecture 9 What is Spark? 14:03 Lecture 10 Spark Architecture - Part 1 13:23 Lecture 11 Spark Architecture - Part 2 13:25 Lecture 12 Installing Spark and Setting up for Python 12:05 Quiz 1 Hadoop and Spark Architecture 5 questions Section 3: Programming with Spark Lecture 13 Spark Transformations 11:33 Lecture 14 Spark Actions 15:04 Lecture 15 Advanced Spark Programming 10:10 Lecture 16 Python - Spark Programming examples 1 16:11 Lecture 17 Python - Spark Programming Examples 2 17:18 Quiz 2 Data Engineering with Spark 5 questions Lecture 18 PRACTICE Exercise : Spark Operations Article Section 4: Spark SQL Lecture 19 Spark SQL Overview 10:03 Lecture 20 Python - Spark SQL Examples 16:16 Quiz 3 Spark SQL 2 questions Lecture 21 PRACTICE Exercise : Spark SQL Article Section 5: Spark Streaming Lecture 22 Streaming with Apache Spark 15:53 Lecture 23 Python - Spark Streaming examples 17:47 Quiz 4 Spark Streaming 3 questions Section 6: Real time Data Science Lecture 24 Basic Elements of Data Science 11:51 Lecture 25 The Dataset 10:44 Lecture 26 Learning from relationships 12:55 Lecture 27 Modeling and Prediction 09:31 Lecture 28 Data Science Use Cases 07:47 Lecture 29 Types of Analytics 12:08 Lecture 30 Types of Learning 17:16 Lecture 31 Doing Data Science in real time with Spark 07:39 Quiz 5 Spark Data Science 5 questions Section 7: Machine Learning with Spark Lecture 32 Spark Machine Learning 12:18 Lecture 33 Analyzing Results and Errors 13:46 Lecture 34 Linear Regression 19:00 Lecture 35 Spark Use Case : Linear Regression 18:33 Lecture 36 Decision Trees 10:42 Lecture 37 Spark Use Case : Decision Trees Classification 14:58 Lecture 38 Principal Component Analysis 07:28 Lecture 39 Random Forests Classification 10:31 Lecture 40 Python Use Case : Random Forests & PCA 13:16 Lecture 41 Text Preprocessing with TF-IDF 14:53 Lecture 42 Naive Bayes Classification 19:21 Lecture 43 Spark Use Case : Naive Bayes & TF-IDF 07:26 Lecture 44 K-Means Clustering 11:53 Lecture 45 Spark Use Case : K-Means 14:26 Lecture 46 Recommendation Engines 11:55 Lecture 47 Spark Use Case : Collaborative Filtering 06:34 Lecture 48 Real Time Twitter Data Sentiment Analysis 10:11 Quiz 6 Spark Machine Learning Algorithms 4 questions Lecture 49 PRACTICE Exercise : Spark Clustering Article Lecture 50 PRACTICE Exercise : Spark Classification Article Section 8: Conclusion Lecture 51 Closing Remarks 01:56 Lecture 52 BONUS Lecture : Other courses you should check out Article

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Views: 335248 Siraj Raval

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In this Statistics Using Python Tutorial, Learn cleaning Data in Python Using Pandas. learn basic data cleaning steps in excel before importing data in python. We use Pandas Functions to clean data perform exploratory data analysis on our Data set. 🔷🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Practice Files: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷🔷 Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI 🔷🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 9144 TheEngineeringWorld

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Python untuk Data Mining | Python for Data Mining Tutorial 2 Data Mining menggunakan bahasa pemrograman Python. Dari Mulai: 1. Alasan Kenapa menggunakan Python 2. Machine Learning Library di Python 3. Komponen bahasa pemrograman Python 4. Virtual environment di Python 5. Dasar-dasar pemrograman Python 6. Numpy, scipy, pandas, matplotlib 7. Data Mining dengan Python 8. Analisis data menggunakan Python 9. Dan sebagainya #Python #DataMining #MachineLearning #DataAnalysis #scikit-learn
Views: 1407 Rischan Mafrur

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Learn web scraping using the Python Beautiful Soup library. Get HTML web pages using Requests library, and scrape data using BS4. ► Get Jupyter notebook: https://github.com/joeyajames/Python/tree/master/Web%20Data%20Mining ► Subscribe to my Channel https://www.youtube.com/channel/UC4Xt-DUAapAtkfaWWkv4OAw?view_as=subscriber?sub_confirmation=1 ► Thank me on Patreon: https://www.patreon.com/joeyajames
Views: 1239 Joe James

19:52
** Python for Data Science: https://www.edureka.co/python ** This Edureka video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist for more videos: http://bit.ly/2taym8X Subscribe to our channel to get video updates. Hit the subscribe button above. #KNNAlgorithm #MachineLearningUsingPython #MachineLearningTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 64174 edureka!

01:34:32
Session du 19.01.2018 La session portere sur la Place de R et Python dans les formations en Data Science, présentée par Ricco Rakotomalala du Master SISE – Université Lyon 2. La science des données n’échappe pas à la vague des logiciels libres. Depuis plusieurs années, les deux outils les plus populaires auprès des data scientists sont R et Python selon le sondage annuel du site KDnuggets (Mai 2017). Certes, les licences présentent des subtilités un peu difficiles à suivre parfois, mais elles respectent deux caractéristiques fondamentales de mon point de vue : nous avons accès au code source, nous garantissant un certain contrôle sur les calculs et opérations réellement effectuées ; ils sont accessibles et exploitables gratuitement, quels que soient les contextes d’utilisation. De fait, l’adoption de R et Python dans les formations en data science semble évidente. Pourtant, il faut être prudent, ne serait-ce que par principe. Dans mon exposé, je m’appuierai sur ma propre expérience d’enseignant d’une part, de créateur de logiciels de data mining gratuits à vocation pédagogique (SIPINA, TANAGRA) d’autre part, pour essayer de cerner les attentes que l’on peut avoir vis-à-vis des outils dans les cours de statistique et de data science. L’élaboration de TANAGRA (2004) en particulier aura été l’occasion de mener une réflexion approfondie sur les caractéristiques clés que doivent présenter les logiciels pour l’enseignement. Je reviendrai rapidement dessus pour mieux rebondir sur la définition d’un cahier des charges moderne où les compétences en programmation et les accès aux API tiennent une place importante. Dans ce contexte, que l’on pourrait qualifier de Big Data, R et Python se démarquent réellement et justifient pleinement l’investissement que l’on pourrait leur consacrer au sein des formations. Je m’appuierais sur une étude récente réalisée par un groupe d’étudiants du Master SISE pour essayer de cerner les mots clés importants qui caractérisent les annonces dans nos domaines en France. Python y occupe un espace assez singulier.
Views: 6585 Lyon Data Science

20:15
Views: 878 Amit Sharma

09:51
Views: 2687 Afiz

14:43
Data Mining Preprocessing in Jupyter Notebook with Python using Pandas, Numpy and a Baseball dataset.
Views: 562 D Thomas

02:30:43
Lecture 12: Business Data Mining (Loan Prediction with Python)