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Search results “List of web mining algorithms for beginners”
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 229047 Well Academy
PageRank Algorithm - Example
 
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Full Numerical Methods Course: http://bit.ly/numerical-methods-java FREE Beginner Java Course: http://bit.ly/2rMkyxN
Views: 72758 Balazs Holczer
PageRank Algorithm - Matrix Representation
 
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Numerical Methods Bootcamp: http://bit.ly/numerical-methods-java FREE Beginner Java Course: http://bit.ly/2rMkyxN
Views: 20353 Balazs Holczer
Web Mining: Methods and Tools, Elad Segev
 
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Web Mining: Methods and Tools, a lecture by Elad Segev. The lecture was given during the Scholarly use of Web archives: Studying Israeli Politics on the Web,The Fifth Annual Conference of the Israeli Forum for Internet and Technology Researchers held at BIU in May 2013. For All Videos: http://www.youtube.com/playlist?list=PLXF_IJaFk-9DheU5AKzYO5fgCQFFLbAp9 Bar-Ilan University: http://www1.biu.ac.il/en
Views: 4055 barilanuniversity
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Last Minute Tutorials | Apriori algorithm | Association Rule Mining
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 85791 Last Minute Tutorials
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1758 Leo Isikdogan
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 179991 Well Academy
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 5141 PhD Assistance
Intro to Web Scraping with Python and Beautiful Soup
 
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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 Find more tutorials here: https://tutorials.datasciencedojo.com/ -- 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 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6wzS0 See what our past attendees are saying here: https://hubs.ly/H0f6wzY0 -- 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
Views: 513234 Data Science Dojo
Machine learning algorithms, choosing the correct algorithm for your problem - Joakim Lehn
 
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When I started out exploring machine learning I often faced the problem of choosing the most appropriate algorithm for my specific problem. In this presentation I will try to explain basic concepts and give you an intuititive approach to using different kinds of machine learning algorithms for different tasks. By the end of this presentation you’ll know what category of algorithms is most suited for your problem. NDC Conferences https://ndcoslo.com https://ndcconferences.com
Views: 2080 NDC Conferences
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 129889 Udacity
data mining tutorial for beginners
 
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data mining tutorial for beginners Join Free course here: https://peakget.com/offer/list-building-course/ Affiliate Marketing Google Strategy: https://www.udemy.com/affiliate-marketing-with-google-editor/?couponCode=YOUTUBE Welcome to my YouTube channel! My name is Juri and I am the owner of http://www.tips-digital.com I am professional in social media and in audience building, also I am doing affiliate marketing and SEO. You can hire me if you want to build a website and grow your sales or maybe you need professional advice or consultation?! It does not matter what business you have: online shop or small cafe... What you need to understand is: if you want to increase your sales - you need to be social and use right strategies! I will teach you step by step how to attract visitors from social media. If you do not have any business you can still receive profit by promoting affiliate links. Please check my blog, subscribe to my YouTube channel. For any business inquires you can contact me.
Views: 3622 Juri Fab
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 385009 Last moment tuitions
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 75570 Last moment tuitions
Data Mining For Automated Personality Classification
 
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
Views: 5307 Nevon Projects
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 110340 StudyKorner
Data Mining Full Bangla Tutorial || Data Entry Lesson- 4||
 
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Data Entry And Web Research Bangla Tutorial (2017) this tutorial i am going to teach you about data entry and web research.this is a tutorial for begginers data entry and web research have a great place in freelancing marketplaces.so after learning you guys can easily start doing this type of jobs ## Outsourcing Working Tips 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSJqRnUVx7CSi1Cdc2KdD-F0 ## Data Entry Job A to Z Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSJNMpoVIxEcQLZscDRwkI3l ## Advance Internet Tricks https://www.youtube.com/playlist?list=PLCTj6SR5wKSJkKunzQUi8xTls2X5M9yZH ## Basic SEO Full Bangla Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSLjfUstoM8MDpc_MEvU24Ow ## SEO Full Bangla Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSKYTnjGvxvry98WhlyzecJb ## Basic To Advance Computer Operating And Internet Browsing Full Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSKbFCaphVP_Bij9JyOKz8-- In This Playlist 1. Simple Data Entry Job Full Bangla Tutorial 2017 || Data Entry Lesson- 1 https://youtu.be/9lhzyYRBJVg 2. Data Collection Full Bangla Tutorial (2017) || Data Entry Lesson- 2 https://youtu.be/eI2ktApDYew 3. BPO Data Entry Full Bangla Tutorial 2017 || Data Entry Lesson- 3 https://youtu.be/1nbtMVJpi38 4. Data Mining Full Bangla Tutorial 2017 || Data Entry Lesson- 4 https://youtu.be/P-WvkOMiLGA 5. Data Scraping Full Bangla Tutorial 2017 || Data Entry Lesson- 5 https://youtu.be/LpZ9XU1RogQ 6. Data Processing Full Bangla Tutorial 2017 || Data Entry Lesson- 6 7. Data Research Full Bangla Tutorial 2017 || Data Entry Lesson- 7 https://youtu.be/xCB4STWNTdg 8. Data Entry For Ecommerce Site Full Bangla Tutorial 2017 || Data Entry Lesson- 8 https://youtu.be/f8qb43o2wFA 9. Magento data entry Full Bangla Tutorial 2017 || Data Entry Lesson- 9 https://youtu.be/IWIB_GYqFYc 10. ERP Software Full Bangla Tutorial 2017 || Data Entry Lesson- 10 https://youtu.be/Wn3RpvElMFU 11. Data Convert Full Bangla Tutorial 2017 || Data Entry Lesson- 11 https://youtu.be/tYVjdwTOwGk #web research bangla tutorial #data entry bangla tutrorial #how to start doing data entry bangla tutorial #how to do data entry job bangla tutorial #Data Entry And Web Research Bangla Tutorial (2017) Data Entry And Web Research Bangla Tutorial ডাটা এন্টি জব, ডাটা এন্টি শিখবো, ডাটা এন্ট্রি বাংলা টিউটোরিয়াল,
Views: 2918 Gazi Tech School
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 113237 LearnEveryone
How to Create a Custom Recipe - Rows
 
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Learn how to create custom recipes. In this video we'll focus on the Rows section. This section is only used for List pages or search results pages. The row defines the containers where the data is located. They also define the final rows in your CSV. To learn more about Data Miner and download the tool, please visit the following link: https://chrome.google.com/webstore/detail/data-scraper-easy-web-scr/nndknepjnldbdbepjfgmncbggmopgden
Views: 42518 Data Miner
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 138097 Brandon Weinberg
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4944 Microsoft Research
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 21035 Nevon Projects
Machine Learning Tutorial 4  - Generalization (Algorithms)
 
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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 Generalization (Algorithms) is 4th in this machine learning course. This video explains an algorithm's ability to generalize beyond data that we have available. This allows the algorithm to choose the best model even if we are lacking historical data to fully represent reality. Consider also generalization as a measurement of how well an algorithm is able to predict an entity's target feature value even though we do not have historical data to match such entity. 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: 5199 Caleb Curry
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 33525 GRIETCSEPROJECTS
Ranking Web Pages - CS101 - Udacity
 
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Other units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232 Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE Q&A: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E To gain access to interactive quizzes, homework, programming assignments and a helpful community, join the class at http://www.udacity.com
Views: 1476 Udacity
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 273753 Siraj Raval
Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn
 
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This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis. This Data Science with Python tutorial will cover the following topics: 1. What is Data Science? 2. Basics of Python for data analysis - Why learn Python? - How to install Python? 3. Python libraries for data analysis 4. Exploratory analysis using Pandas - Introduction to series and dataframe - Loan prediction problem 5. Data wrangling using Pandas 6. Building a predictive model using Scikit-learn - Logistic regression 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/ifQRpS 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. 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 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=Data-Science-With-Python-mkv5mxYu0Wk&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn 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: 83979 Simplilearn
L1: Data Warehousing and Data Mining |Introduction to Warehousing| What is Mining| Tutorial in Hindi
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L1: Data Warehousing and Data Mining | What is Warehousing| What is Mining| Tutorial in Hindi Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 1310 University Academy
Automatic Recommendation for Online Users Using Web Usage Mining | ieee data mining projects
 
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Automatic Recommendation for Online Users Using Web Usage Mining 2012 ieee data mining project in java (mtech,btech,mca). Read more http://ieee-projects10.com/automatic-recommendation-for-online-users-using-web-usage-mining/
Views: 2514 satya narayana
How to Do Sentiment Analysis - Intro to Deep Learning #3
 
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In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. Please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 145931 Siraj Raval
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3231 Galit Shmueli
Machine Learning Tutorial 5 - Big Data, Data Warehouse, Hadoop, Federation
 
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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 Big Data, Hadoop, Federation is 5th video in this machine learning course. This video explains some of the machine learning platforms and technologies that are used. Keep in mind that these are the foundations, so we go into the types of infrastructures rather than specific products or vendors. The topics covered are big data, Hadoop, and federation. These are all terms that are very useful in predictive analytics. 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: 5607 Caleb Curry
Orchestrating the Intelligent Web with Apache Mahout
 
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Presenter(s): Aneesha Bakharia URL: http://2011.linux.conf.au/programme/schedule/view_talk/213 Presenters: Aneesha Bakharia ([email protected]) and Aaron Tan ([email protected]) It is becoming increasingly important to incorporate “collective intelligence” within web, mobile and business intelligence applications. Traditionally the implementation of algorithms capable of adding intelligence to an application either required a highly specialised knowledge of machine learning or was extremely costly. Apache Mahout is one of the first open source and scalable machine learning libraries that seeks to mainstream the use of machine learning. This presentation will focus on providing the audience with a practical understanding of the algorithms included in Apache Mahout and how they can be used to provide insight into the patterns that exist in large amounts of data? Text clustering with the Latent Dirichlet Algorithm will also be covered. The Apache Mahout library consists of scalable machine learning algorithms for data mining tasks that encompass classification (Naïve Bayes and Support Vector Machines), clustering (k­means, Expectation Maximization, Mean Shift, Latent Dirichlet Allocation and Hierarchical Clustering), recommendation (collaborative filtering) and frequent pattern mining (parallel fp-growth). As of the 0.3 release, an impressive total of 25 machine learning algorithms have been implemented. Apache Mahout achieves scalability by leveraging Apache Hadoop which implements the MapReduce parallel processing paradigm that was first made popular by Google. Latent Dirichlet Allocation is a relatively new algorithm first introduced in 2003 with a suggested use in Topic Modeling (text clustering). Unlike generic clustering algorithms such as k-means, Latent Dirichlet Allocation is able to model document overlap. Latent Dirichlet Allocation is not a hard clustering algorithm and is able to map documents and words to multiple clusters. This feature is a natural fit for documents, which usually discuss multiple topics. The Latent Dirichlet Allocation algorithm simultaneously groups both documents and words into clusters. This is a useful feature as the main words belonging to a cluster and the prominent documents within a cluster are both output by the algorithm. Twitter recently released a feature called Lists that allows you to group people you follow and view the timeline of Tweets for users in a List separately. We will use the Latent Dirichlet Allocation algorithm to cluster people you follow and suggest Lists for Twitter. This will serve as a practical overview of using Apache Mahout for clustering. The following topics of interest are: - What is machine learning? - What is Apache Mahout? - Who is using Apache Mahout? - The MapReduce paradigm - Machine learning with Apache Mahout - Clustering with Apache Mahout - Classification with Apache Mahout - Collaborative Filtering with Apache Mahout - Frequent Pattern Mining with Apache Mahout - Processing Large Datasets with Multiple Cluster Nodes - Building a Twitter List recommendation application with the Latent Dirichlet Allocation algorithm http://2011.linux.conf.au/ - http://www.linux.org.au CC BY-SA - http://creativecommons.org/licenses/by-sa/4.0/legalcode.txt
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 77 Data Analytics
Learn Data Science in 3 Months
 
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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 270635 Siraj Raval
Building a Blockchain in Under 15 Minutes - Programmer explains
 
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I wanted to demonstrate that the concept of a blockchain that powers almost all of the modern cryptocurrencies is very simple at its core. Bitcoin, Ethereum, Litecoin etc all are based on this blockchain technology. Many people think that the blockchain is a complicated thing while at it's core its just a clever use case of hashes. Enjoy guys! CODE: https://github.com/ivan-liljeqvist/SimpleBlockchain JOIN MY EXCLUSIVE MAILING LIST FOR EVEN MORE BLOCKCHAIN KNOWLEDGE: http://eepurl.com/c0hyc9 ESSENTIAL CRYPTO RESOURCES Hardware wallets: ♥ TREZOR https://trezor.io ♥ LEDGER NANO S https://www.ledgerwallet.com/r/4607 To buy cryptocurrencies: ♥ Coinbase https://www.coinbase.com/join/529bab0ab08ded7080000019 JOIN SLACK COMMUNITY http://slack-invite-ivan-on-tech.herokuapp.com https://steemit.com/@ivanli
Views: 416255 Ivan on Tech
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 29372 edureka!
Machine Learning Tutorial 21 - Decision Trees
 
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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 Today we are talking about decision trees and why they are important! Watch to learn more. :) 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: 1338 Caleb Curry
Page Rank - Intro to Computer Science
 
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This video is part of an online course, Intro to Computer Science. Check out the course here: https://www.udacity.com/course/cs101.
Views: 15816 Udacity
Indexing 2: inverted index
 
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A search engine represents documents as vectors over the vocabulary. If we arrange all documents as row vectors in a matrix, then the column vectors are inverted lists: for each keyword in our vocabulary they give a list of occurrences of this keyword in all documents.
Views: 26127 Victor Lavrenko
40 Data Analysis New Tools - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 90 Data Analytics
After Python Basics, What Next?
 
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After Python Basics, What Next? ► 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 ► Other Essential Programming skills in Python: Deeper understanding of Python data structures - string, list, tuple, dict, numpy array Advanced Data Structures - trees, graphs, queues, stacks, heaps, linked lists Analysis of Algorithms - Big O analysis, and learn some common tree, graph and sorting algos databases - SQL Object Oriented Programming - Classes, Inheritance, Design Patterns ► Problem Solving and Coding Skills in Python: ProjectEuler CodeWars TopCoder HackerRank ► Three Popular Tracks for Python Careers: Web Development - Django, Flask frameworks related languages: Javascript Data Analysis and Visualization - Numpy, Pandas, Matplotlib Related languages: R, Julia, Machine Learning - Numpy, Pandas, Tensorflow, Keras, SciKitLearn, Theano ► Other Good Projects to Work On: web data mining recommendation systems sentiment analysis for reviews or tweets search engines Kaggle data science competitions contribute to open source projects RELATED VIDEOS ► Numpy Intro: https://youtu.be/8Mpc9ukltVA ► Numpy Intro Jupyter nb: https://youtu.be/AAS8yoKuK7M ► Pandas Intro: https://youtu.be/e60ItwlZTKM ► Pandas and MPL for Data Analysis: https://youtu.be/ALX88JzeQnk ► Matplotlib Intro: https://youtu.be/MbKrSmoMads
Views: 627 Joe James
3 - ETL Tutorial | Extract Transform and Load
 
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This video aims to provide an overview of #ETL (Extract Load Transformation ) process and covers: #extraction Process and its Strategies Transformation and various tasks performed Loading Process and its Strategies ETL tools and its features. ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle ETL Tools Features: Source and Target Data System Connectivity Scalability and Performance Easy Transformation connectors Data Profiling Data Cleaning and Quality Easy integration with Web services Logging and Exception Handling Robust Administration features Efficient Batch and Real time processing For more details visit: http://www.vikramtakkar.com/2015/10/what-is-etl-extract-transformation-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 112829 Vikram Takkar
Introduction to Data Structure and Algorithms | data structures tutorial | free online course
 
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This Day-0 video tutorial is introductory and helps you to understand what data structure is. A brief description of data structure, its types, characteristics and a real-time example to understand data structure more easily. If you enjoyed this tutorial please Like, share and subscribe. If you want to stay updated with our upcoming videos, press the bell icon.
Views: 264 Nursery to Varsity
Generating Association Rules from Frequent Itemsets
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 71295 Noureddin Sadawi

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