This video demonstrates how to use Google search query suggestions/autocomplete API using Python. More awesome topics covered here: Introduction to Numpy: http://bit.ly/2RZMxvO Introduction to Matplotlib: http://bit.ly/2UzwfqH Introduction to Pandas: http://bit.ly/2GkDvma Intermediate Python: http://bit.ly/2sdlEFs Functional Programming in Python: http://bit.ly/2FaEFB7 Python Package Publishing: http://bit.ly/2SCLkaj Multithreading in Python: http://bit.ly/2RzB1GD Multiprocessing in Python: http://bit.ly/2Fc9Xrp Parallel Programming in Python: http://bit.ly/2C4U81k Concurrent Programming in Python: http://bit.ly/2BYiREw Dataclasses in Python: http://bit.ly/2SDYQub Exploring YouTube Data API: http://bit.ly/2AvToSW Jupyter Notebook (Tips, Tricks and Hacks): http://bit.ly/2At7x3h Decorators in Python: http://bit.ly/2sdloX0 Inside Python: http://bit.ly/2Qr9gLG Exploring datetime: http://bit.ly/2VyGZGN Computer Vision for noobs: http://bit.ly/2RadooB Python for web: http://bit.ly/2SEZFmo Awesome Linux Terminal: http://bit.ly/2VwdTYH Tips, tricks, hacks and APIs: http://bit.ly/2Rajllx Optical Character Recognition: http://bit.ly/2LZ8IfL Facebook Messenger Bot Tutorial: http://bit.ly/2BYjON6 #python #google-search #autocomplete
Views: 1588 Indian Pythonista
What is WEB SEARCH QUERY? What does WEB SEARCH QUERY mean? WEB SEARCH QUERY meaning - WEB SEARCH QUERY definition - WEB SEARCH QUERY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A web search query is a query that a user enters into a web search engine to satisfy his or her information needs. Web search queries are distinctive in that they are often plain text or hypertext with optional search-directives (such as "and"/"or" with "-" to exclude). They vary greatly from standard query languages, which are governed by strict syntax rules as command languages with keyword or positional parameters. There are three broad categories that cover most web search queries: informational, navigational, and transactional. These are also called "do, know, go." Although this model of searching was not theoretically derived, the classification has been empirically validated with actual search engine queries. Informational queries – Queries that cover a broad topic (e.g., colorado or trucks) for which there may be thousands of relevant results. Navigational queries – Queries that seek a single website or web page of a single entity (e.g., youtube or delta air lines). Transactional queries – Queries that reflect the intent of the user to perform a particular action, like purchasing a car or downloading a screen saver. Search engines often support a fourth type of query that is used far less frequently: Connectivity queries – Queries that report on the connectivity of the indexed web graph (e.g., Which links point to this URL?, and How many pages are indexed from this domain name?). Most commercial web search engines do not disclose their search logs, so information about what users are searching for on the Web is difficult to come by. Nevertheless, research studies appeared in 1998. Later, a study in 2001 analyzed the queries from the Excite search engine showed some interesting characteristics of web search: The average length of a search query was 2.4 terms. About half of the users entered a single query while a little less than a third of users entered three or more unique queries. Close to half of the users examined only the first one or two pages of results (10 results per page). Less than 5% of users used advanced search features (e.g., boolean operators like AND, OR, and NOT). The top four most frequently used terms were, (empty search), and, of, and sex. A study of the same Excite query logs revealed that 19% of the queries contained a geographic term (e.g., place names, zip codes, geographic features, etc.). Studies also show that, in addition to short queries (i.e., queries with few terms), there are also predictable patterns to how users change their queries. A 2005 study of Yahoo's query logs revealed 33% of the queries from the same user were repeat queries and that 87% of the time the user would click on the same result. This suggests that many users use repeat queries to revisit or re-find information. This analysis is confirmed by a Bing search engine blog post telling about 30% queries are navigational queries In addition, much research has shown that query term frequency distributions conform to the power law, or long tail distribution curves. That is, a small portion of the terms observed in a large query log (e.g. 100 million queries) are used most often, while the remaining terms are used less often individually. This example of the Pareto principle (or 80–20 rule) allows search engines to employ optimization techniques such as index or database partitioning, caching and pre-fetching. In addition, studies have been conducted on discovering linguistically-oriented attributes that can recognize if a web query is navigational, informational or transactional. But in a recent study in 2011 it was found that the average length of queries has grown steadily over time and average length of non-English languages queries had increased more than English queries. Google has implemented the hummingbird update in August 2013 to handle longer search queries since more searches are conversational (i.e. "where is the nearest coffee shop?"). For longer queries, Natural language processing helps, since parse trees of queries can be matched with that of answers and their snippets. For multi-sentence queries where keywords statistics and Tf–idf is not very helpful, Parse thicket technique comes into play to structurally represent complex questions and answers.
Views: 422 The Audiopedia
PyData Berlin 2016 This talk discusses how machine learning/data mining techniques can be applied to classify search terms that people use in search engines like Google, Bing, Yahoo etc. The talk focuses on machine learning techniques such as LSTM (long short term memory) rather than traditional ways like analysing user-behaviour with the help of their search logs. Traditionally, search queries are classified into different categories by analysing user-behavior with the help of search logs. Instead of focussing on the search logs and by analysing queries with the help of machine learning models such as LSTM, it is possible to get a very decent model to classify the search queries. This talk focuses majorly on LSTMs and their usefulness when it comes to search query classification. We also discuss how we can accurately classify search queries in hundreds of categories using open source data available online and how this can be combined with LSTMs to provide a much stable and better result.
Views: 1546 PyData
Energy-Efficient Query Processing in Web Search Engines IEEE PROJECTS 2017-2018 Call Us: +91-7806844441,9994232214 Mail Us: [email protected] Website: http://www.ieeeproject.net : http://www.projectsieee.com : http://www.ieee-projects-chennai.com : http://www.24chennai.com WhatsApp : +91-7806844441 Chat Online: https://goo.gl/p42cQt Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support
Views: 250 IEEE PROJECTS CHENNAI
There is a trend to advance the functionality of search engines to a more expressive semantic level. This is enabled by employing large-scale information extraction of entities and relationships from semistructured as well as natural-language Web sources. In addition, harnessing Semantic-Web-style ontologies and reaching into Deep-Web sources can contribute towards a grand vision of turning the Web into a comprehensive knowledge base that can be efficiently searched with high precision. This talk presents ongoing research at the Max-Planck Institute for Informatics towards this objective, centered around the YAGO knowledge base and the NAGA search engine. YAGO is a large collection of entities and relational facts that are harvested from Wikipedia and WordNet with high accuracy and reconciled into a consistent RDF-style semantic graph. NAGA provides graph-template-based search over this data, with powerful ranking capabilities based on a statistical language model for graphs. Advanced queries and the need for ranking approximate matches pose efficiency and scalability challenges that are addressed by algorithmic and indexing techniques. This is joint work with Georgiana Ifrim, Gjergji Kasneci, Maya Ramanath, and Fabian Suchanek.
Views: 205 Microsoft Research
DOTNET | Mining Web Graphs for Recommendations TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of various types of graphs. In this paper, aiming at providing a general framework on mining Web graphs for recommendations, 1) we first propose a novel diffusion method which propagates similarities between different nodes and generates recommendations; 2) then we illustrate how to generalize different recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, tag recommendations, expert finding, image recommendations, image annotations, etc. The experimental analysis on large data sets shows the promising future of our work.
Views: 91 jpinfotechprojects
Are you want to search image base on google search? Create Your Api Key and Google Custom Search Engine. If you want to scrap or grab search result from google search this best choice for you with google API ... :D In Free Version limit is 100 query/day
Views: 6404 Autoblogscript
Google Tech Talks December, 19 2007 ABSTRACT We present a distributed architecture for a Web search engine, based on the concept of collection selection. We introduce a novel approach to partition the collection of documents, able to greatly improve the effectiveness of standard collection selection techniques (CORI), and a new selection function outperforming the state of the art. Our technique is based on the novel query-vector (QV) document model, built from the analysis of query logs, and on our strategy of co-clustering queries and documents at the same time. By suitably partitioning the documents in the collection, our system is able to select the subset of servers containing the most relevant documents for each query. Instead of broadcasting the query to every server in the computing platform, only the most relevant will be polled, this way reducing the average computing cost to solve a query. We introduce a novel strategy to use the instant load at each server to drive the query routing. Also, we describe a new approach to caching, able to incrementally improve the quality of the stored results. Our caching strategy is effectively both in reducing computing load and in improving result quality. The proposed architecture, overall, presents a trade-off between computing cost and result quality, and we show how to guarantee very precise results in face of a dramatic reduction to computing load. This means that, with the same computing infrastructure, our system can serve more users, more queries and more documents. Speaker: Diego Puppin
Views: 10715 GoogleTechTalks
Title: Mining Web Graph For Recommendation is developed by Mirror Technologies Pvt Ltd -- Vadapalani, Chennai. Domain: Data Mining. Algorithm Used: Query Suggestion Algorithm Key Features: 1. It is a general method, which can be utilized to many recommendation tasks on the Web. 2. It can provide latent semantically relevant results to the original information need. 3. This model provides a natural treatment for personalized recommendations. 4. The designed recommendation algorithm is scalable to very large datasets. Visit http://www.lbenchindia.com/ For more details contact: Mirror Technologies Pvt Ltd #73 & 79, South Sivan kovil Street, Vadapalani, Chennai, Tamil Nadu. Telephone: +91-44-42048874. Phone: 9381948474, 9381958575. E-Mail: [email protected], [email protected]
Views: 774 Learnbench India
Views: 3445 Amarindaz
This technical demo will be presented at ACM Multimedia 2013 in Barcelona (Spain). This work focuses on visual objects (like logos or buildings) mining in web-images. All discovered instances are linked together in a visual matching graph, which is then clustered to be used in a dedicated GUI.
Views: 312 Pierre Letessier
This is a tutorial which goes over how to create a PHP search which filters out results from a database table. Sorry for the mistakes made in this video, the video following this goes over how to take this and make it instant with jQuery Tutor Facebook: http://www.facebook.com/JoeTheTutor Dibbble: www.dribbble.com/sleekode www.helpingdevelop.com
Views: 455016 Joseph Smith
If you go to Amazon.com or the Apple Itunes store, your ability to search for new music will largely be limited by the `query-by-metadata' paradigm: search by song, artist or album name. However, when we talk or write about music, we use a rich vocabulary of semantic concepts to convey our listening experience. If we can model a relationship between these concepts and the audio content, then we can produce a more flexible music search engine based on a 'query-by-semantic- description' paradigm. In this talk, I will present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-base query. I consider the related tasks of content- based audio annotation and retrieval as one supervised multi-class, multi-label problem in which we model the joint probability of acoustic features and words. For each word in a vocabulary, we use an annotated corpus of songs to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies Expectation Maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our `query-by-semantic-description' system can retrieve appropriate songs for a large number of musically relevant words. I also show that our audition system is general by learning a model that can annotate and retrieve sound effects. Lastly, I will discuss three techniques for collecting the semantic annotations of music that are needed to train such a computer audition system. They include text-mining web documents, conducting surveys, and deploying human computation games.
Views: 179 Microsoft Research
100 free coins for signing up with this ICO: https://goo.gl/jgMp9P In this video I show you how to add Presearch as your default address bar search engine in Google Chrome. Click here for an invite to the Presearch beta and earn PRE tokens. https://goo.gl/RiYxoA Support this channel by checking out the links below: Buy Bitcoin with your credit card here: https://goo.gl/m1pXxJ Secure your Bitcoin with the Ledger Nano S Hardware Wallet: https://goo.gl/o5RTbf Get Free Cyrpto Currencies Here (faucet): https://goo.gl/gjegLN Buy Sell and Trade Crypto Currencies at Cryptopia (Buy and Sell Electroneum): https://goo.gl/rfT1GE CoinExchange.io: https://goo.gl/EUjAgs COSS.IO: https://goo.gl/jesY1s ICO's I Am Participating In BitDegree: Decentralizing education: https://goo.gl/rTrx3c Crypterium: First crypto bank - https://goo.gl/Q4HXx4 CrypoXchanger: Decentralized trading platform: https://goo.gl/tKNGTH ********************************************************** Enter the following as your Search Engine URL in Google Chrome Settings https://www.presearch.org/search?term=%s
Views: 3839 Crypto Explorer
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: 125110 Udacity
S/W: PHP, MySQL Automatically Mining Facets for Queries from Their Search Results
Views: 1159 ChennaiSunday Sivakumar
Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query. Perhaps surprisingly, despite QAC being widely used, users' interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants completed web search tasks, we recorded their interactions using eye-tracking and client-side logging. This allows us to provide a first look at how users interact with QAC. We specifically focus on the effects of QAC ranking, by controlling the quality of the ranking in a within-subject design. We identify a strong position bias, that is consistent across ranking conditions. Due to this strong position bias, ranking quality affects QAC usage. We also find an effect on task completion, in particular on the number of result pages visited. We show how these effects can be explained by a combination of searchers' behavior patterns, namely monitoring or ignoring QAC, and searching for spelling support or complete queries to express a search intent. We conclude the paper with a discussion of the important implications of our findings for QAC evaluation.
Views: 609 Компьютерные науки
Presentation slides available here: http://www.lucenerevolution.org/past_events Information Retrieval is becoming the principal mean of access to Information. It is now common for web applications to provide interface for free text search. In this talk we start by describing the scientific underpinning of information retrieval. We review the main models on which are based the main search tools, i.e. the Boolean model and the Vector Space Model. We illustrate our talk with a web application based on Lucene. We show that Lucene combines both the Boolean and vector space models. The presentation will give an overview of what Lucene is, where and how it can be used. We will cover the basic Lucene concepts (index, directory, document, field, term), text analysis (tokenizing, token filtering, sotp words), indexing (how to create an index, how to index documents), and seaching (how to run keyword, phrase, Boolean and other queries). We'll inspect Lucene indices with Luke. After this talk, the attendee will get the fundamentals of IR as well as how to apply them to build a search application with Lucene.
Views: 12110 LuceneSolrRevolution
LaSEWeb: automating search strategies over semi-structured web data KDD 2014 Presentation Oleksandr Polozov Sumit Gulwani We show how to programmatically model processes that humans use when extracting answers to queries (e.g., "Who invented typewriter?", "List of Washington national parks") from semi-structured Web pages returned by a search engine. This modeling enables various applications including automating repetitive search tasks, and helping search engine developers design micro-segments of factoid questions. We describe the design and implementation of a domain-specific language that enables extracting data from a webpage based on its structure, visual layout, and linguistic patterns. We also describe an algorithm to rank multiple answers extracted from multiple webpages. On 100,000+ queries (across 7 micro-segments) obtained from Bing logs, our system LaSEWeb answered queries with an average recall of 71%. Also, the desired answer(s) were present in top-3 suggestions for 95%+ cases.
Views: 4 Research in Science and Technology
More here: http://www.gaintap.com/seo/scrape-google-search-links-for-free *UPDATE 6/30/2015 - Set your search results to 100 results instead of the default 20. This makes it WAY FASTER. Big thanks to "The 5 Dollar Website" for letting me know. In this video I cover how to scrape Google search results in a totally safe, non-black hat way. This uses the Google chrome extension called Linkclump and it's totally free. Scraping Google search results doesn't work well with automated web crawlers. If you're not using a proxy to mask your IP, you'll get yourself banned from Google pretty quickly. For that reason I don't mess around trying to scrape Google that way. This method of scraping Google pulls page titles and links. From there you can go on to process that data in interesting ways. I was able to pull 1,000 links in about 5 minutes sitting on my couch, watching TV. That's about 40 links every 5 seconds.
Views: 21916 GainTap
Watch this short video and learn why you shouldn't settle for Google's free Custom Search Engine (CSE). With free, you get what you pay for, and forced ads aren't the only limitation to Google's Custom Search Engine. Learn more at www.swiftype.com/GSS
Views: 1099 Swiftype Search
Search technologies have significantly transformed the way people seek information and acquire knowledge from the internet. To further improve the search accuracy and usability of the current-generation search engines, one of the most important research challenges is to understand a user's intent or information need underlying the query. However, understanding a query in the form of plain text is a non-trivial task. In this talk I will first introduce a framework in which a query is interpreted and represented in multiple levels. Then I will briefly overview our efforts on addressing key research questions from query string, query syntactic, to query semantic understanding. In the rest of the talk I will present our recent work on dynamic query understanding in the query auto-completion process, in which we aim at predicting query representation given only a short prefix.
Views: 249 Microsoft Research
Bing Entity Search API will identify the most relevant entity based on your search term, spanning multiple entity types such as famous people, places, movies, TV shows, video games, books, and even local businesses near you. Enrich your application by infusing contextual information and keep users engaged with your applications.
Views: 271 Microsoft Developer
Hey guys, Here i have shows that how you can get css and api key for an make custom search engine. Also how to use that into your python app. Code of python : from googleapiclient.discovery import build import pprint my_api_key = "Your_api_key" my_cse_id = "Your_cse_id" def google_search(search_term, api_key, cse_id, **kwargs): service = build("customsearch", "v1", developerKey=api_key) res = service.cse().list(q=search_term, cx=cse_id, **kwargs).execute() return res['items'] results = google_search('what is python?', my_api_key, my_cse_id, num=2) for result in results: '''pprint.pprint(result)''' title = result['title'] link = result['formattedUrl'] dis = result['snippet'] print (title) print (link) print (dis) ''' pip install google-api-python-client ''' Thanks, To watching this video. Please like, share this video and if you have any queries and questions please comment me in below section. And don't forget to subscribe the channel. Facebook : https://www.facebook.com/KatharotiyaRajnish/ Instagram : https://www.instagram.com/tutorial_spot/ Twitter : https://twitter.com/tutorial_spot Youtube: https://www.youtube.com/channel/UCvNjso_gPQIPacA6EraoZmg
Views: 2375 TutorialSpot
Author: Dawei Yin, Yahoo! Inc. Abstract: Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search engine has gone far beyond text matching, and now involves tremendous challenges. The semantic gap between queries and URLs is the main barrier for improving base relevance. Clicks help provide hints to improve relevance, but unfortunately for most tail queries, the click information is too sparse, noisy, or missing entirely. For comprehensive relevance, the recency and location sensitivity of results is also critical. In this paper, we give an overview of the solutions for relevance in the Yahoo search engine. We introduce three key techniques for base relevance – ranking functions, semantic matching features and query rewriting. We also describe solutions for recency sensitive relevance and location sensitive relevance. This work builds upon 20 years of existing efforts on Yahoo search, summarizes the most recent advances and provides a series of practical relevance solutions. The reported performance is based on Yahoo’s commercial search engine, where tens of billions of URLs are indexed and served by the ranking system. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 916 KDD2016 video
To Get any Project for CSE,IT ECE,EEE Contact Me @9493059954 or mail us @ [email protected]m Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.
Views: 1515 IEEE2012PROJECTS
To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Efficient Prediction of Difficult Keyword Queries over Databases Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. Further, we present a suite of optimizations to minimize the incurred time overhead.
Views: 1077 jpinfotechprojects
This video covers how to search Google in Python the "easy way." Along with that, you are also introduced to the JSON module in Python. The reason this is the "easy" way is because you are only returned the top 4 results. It may be the case that this is good enough for you. If you are wanting to get more search results, then you will have to do it the harder way. Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 37762 sentdex
On May 27th, David Amerland and David Kutcher will join Denver Prophit Jr. for the event: https://goo.gl/e2Aoxa Related Blog: https://goo.gl/NgWenY We will be discussing: User Search Queries and Semantic Data for the best strategies to optimize your e-commerce website. We will be using PrestaShop in our examples.
Views: 107 StrikeHawk eCommerce
#1: SEMrush Keyword Research: https://link.upcontests.com/semrush (BONUS: Open FREE SEMrush Pro trial account for 14-Days Here: https://link.upcontests.com/semrushtrial ) #2: Keyword Shitter: https://link.upcontests.com/keywordshitter #3: AdWord & SEO Keyword Permutation Generator: https://link.upcontests.com/danzambonini #4: Answer the Public: https://link.upcontests.com/answerthepublic #5: Google Correlate: https://link.upcontests.com/googlecorrelate #6: Keywords Everywhere: https://link.upcontests.com/keywordseverywhere #7: Ubersuggest: https://link.upcontests.com/ubersuggest ------------------------------------------------------------------------------ ** 7 Best Free Keyword Research Tools ** Find Right Keywords That Drive Right People to Your Site In Matter of 60 Seconds or Less! .. Do you want to find keywords for SEO (or next blog post)? And do you want to find best free keywords research tools out there that you can use today to make your content not only keywords rich, but also user-friendly too? Then, after watchig this video, you will find top free SEO keywords research tools and software that crush Google keyword planner. Yes, these free keywords research tools are best free alternatives to Google Keywords Planner (or the Google Keyword Research Tool that was known a few years ago) We are using these awesome tools for keywords research to find broad search terms to long tail keywords that are low competitive and high search volume. Check out these free keywords finder tools to see which one is best for your requirements. #1: SEMrush Keyword Research One of the BEST SEO tools and a must-have marketing toolkit by any Internet marketer. SEMrush will represent the keyword overview data report. Use the Phrase Match Report to Find Long Tail Keywords Use Filter options to get low competitive, medium traffic keywords. (Perfect match for any website that wants to rank higher on SERPs quickly) Reference the Related Keywords Report to Find More Topics Use tools like Keyword Magic to generate targeted keywords in minutes. Include and Exclude keywords to make your research more successful You can use SEMrush tools for free. However, if you want to unlock other SEO tools and increase limits, create a SEMrush Pro Trial account for FREE by clicking the link in description below. #2: Keyword Shitter Enter a “seed” keyword (or many) and hit “Shit keywords!” Keywords Shitter works by mining Google Autocomplete. It doesn’t show search volumes or trends data, nor does it group keywords in any way (as Google Keyword Planner does). But it does have one other notable feature: positive and negative filters. #3: AdWord & SEO Keyword Permutation Generator This tool combines multiple lists of keywords into every possible permutation. This is useful if you want to add transactional or informational modifiers (e.g., “best,” “cheapest,” “buy,” etc.) to a list of topics. It could also be used for local SEO purposes. #4: Answer the Public Answer the Public finds questions, prepositions, comparisons, alphabeticals, and related searches. By default, you’ll see a visualization, but you can switch to a regular ol’ list if you prefer. Alphabeticals are Google Autocomplete suggestions. #5: Google Correlate A search trend tool from Google. In Google’s own words, Google Correlate finds search patterns which correspond with real‐world trends. I.e., trend correlations. Google kicks back ten search queries with trends that correlate with “protein powder.” You’ll notice that not all of these queries contain the “seed” phrase. That’s because this is correlation data—they’re keywords where the search trend correlates with that of your seed keyword. #6: Keywords Everywhere Keywords Everywhere is a free addon for Chrome (or Firefox) that adds search volume, CPC & competition data to all your favorite websites. Download Keywords Everywhere Google Chrome Extension Here: https://chrome.google.com/webstore/detail/keywords-everywhere-keywo/hbapdpeemoojbophdfndmlgdhppljgmp?hl=en These websites include: Google, eBay, Amazon, Answer the Public, Keyword Shitter, and more. #7: Ubersuggest Ubersuggest was acquired by Neil Patel (a famous digital marketer specialized in SEO). Find the keyword competitiveness and the chances of ranking in SERPs. .. So, these are the best free keyword research tools for search engine optimization. If you want to know more about SEO and how to use these free SEO keyword research tools to grow your blog organic traffic, then check out these previous SEO guides and tools. Paid and free backlink checker tools: https://www.youtube.com/watch?v=E1mHpYBWJLA How to find low competition keywords: https://www.youtube.com/watch?v=qXn6qJ102Io What best keyword research tools do you currently use? Is SEMrush one of them or do you use any other free or paid keywords research tools? So, What is your best free keywords research tool? Share your thoughts in the comments below.
Views: 35 Online Marketing
Keyword Research Tools are incredibly important. Not only do they help you identify the right keywords for your website, which will help your SEO, but they also help generate new content ideas! Check our blog post out for more information - http://blog.domainmonster.com/3-free-keyword-research-tools/ This video is based on a Slideshare presentation which you can see here: http://www.slideshare.net/Monster_Sam/3-free-keyword-research-tools Domainmonster.com is the only registrar you'll ever need! We offer a huge range of domain names, web hosting and email services. We provide all our customers with exceptional value and supreme support. Check us out at http://www.domainmonster.com/ Twitter: http://www.twitter.com/domainmonster/ Facebook: http://www.facebook.com/domainmonster/ Google+: http://plus.google.com/+domainmonster/ ----- Slide Text ----- 3 Free Keyword Research Tools To Help Make Your Keyword Research More Effective! What Are Keywords? • Words or phrases that describe a web page. • Help Search Engines identify content. • Help users understand the page intention. • Form the basis of your SEO strategy. • Should be used in your Domain Names Why Is Keyword Research Important? • The more specific your keywords, the easier they are to rank for. • Specific keywords are called 'Long Tail'. • Generic keywords are called 'Short Tail'. • Keyword research tools help you identify which keywords to use on your website. Which Keyword Research Tools Should I Use? 1 -- Google Keyword Tool • See what keywords are being searched for. • Data from the biggest search engine on the planet. • Exact Match, Phrase Match and Broad Match Queries. • Suggests Related Keywords. 2 -- Wordtracker Keyword Questions Tool • See what questions users are asking. • Generate Content Ideas. • Queries for Long Tail Traffic. 3 -- Soovle.com • Automatically generates lists of related keywords • Lets you target different keywords in different search engines • Allows YouTube keyword suggestion
Views: 72 Domainmonster.com
Aleksandar Velkoski http://www.pyvideo.org/video/3545/realtor-search-elasticsearch-and-python-practice Part of our Master Member Profile project, the REALTOR search is a Web2py-based application, leveraging Elasticsearch, that aims to provide users (staff and members) with a means to query comprehensive member profiles. With relevant data gathered and presented via an easy-to-use centralized platform, staff can leverage information to enhance services provided to members and members to enhance productivity.
Views: 5073 Next Day Video
Nearest Keyword Set Search in Multi-Dimensional Datasets To get this project in Online or through training sessions Contact: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landline: (0413) - 4300535 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, we consider objects that are tagged with keywords and are embedded in a vector space. For these datasets, we study queries that ask for the tightest groups of points satisfying a given set of keywords. We propose a novel method called ProMiSH (Projection and Multi Scale Hashing) that uses random projection and hash-based index structures, and achieves high scalability and speedup. We present an exact and an approximate version of the algorithm. Our experimental results on real and synthetic datasets show that ProMiSH has up to 60 times of speedup over state-of-the-art tree-based techniques.
Views: 1034 jpinfotechprojects
In this video, I’ll show you how we can implement Wikipedia API in Python to fetch information from a Wikipedia article. Let’s see how to do it- First we have to install Wikipedia. To install it, open your command prompt or terminal and type this command- "pip install wikipedia" That’s all we have to do. Now we can fetch the data from Wikipedia very easily – To get the summary of an article using Wikipedia API- import wikipedia print(wikipedia.summary("google")) output- it will fetch the summary of google from wikipedia and print it on the screen. To get a given number of sentences from the summary of an article using Wikipedia API- import wikipedia print(wikipedia.summary("google", sentences=1)) output - Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, search engine, cloud computing, software, and hardware. same way you can pass any number as a parameter to get the number of sentences you want. To change the language of the article using Wikipedia API - import wikipedia wikipedia.set_lang("fr") print(wikipedia.summary("google", sentences=1)) output – Google (prononcé [ˈguːgəl]) est une entreprise américaine de services technologiques fondée en 1998 dans la Silicon Valley, en Californie, par Larry Page et Sergueï Brin, créateurs du moteur de recherche Google. Here fr stands for French. You can use any other code instead of fr to get the information in other language. But make sure that the Wikipedia should have that article in the language you want. To see the code of other languages open this link - https://www.loc.gov/standards/iso639-2/php/code_list.php To search to get the Titles of the artcles- import wikipedia print(wikipedia.search("google")) output - ['Google', 'Google+', 'Google Maps', 'Google Search', 'Google Translate', 'Google Chrome', '.google', 'Google Earth', 'Gmail', 'Google Scholar'] the method search() will return a list which consist of all the article’s titles that we can open. To get the URL of the article- import wikipedia page = wikipedia.page("google") print(page.url) output- https://en.wikipedia.org/wiki/Google first wikipedia.page() will store all the relevant information in variable page. Then we can use the url property to get the link of the page. To get the Title of the article- import wikipedia page = wikipedia.page("google") print(page.title) output- Google To get complete article- import wikipedia page = wikipedia.page("google") print(page.content) output- complete article from starting to end will be printed on the screen To get the images included in article- import wikipedia page = wikipedia.page("google") print(page.images) Output - https://upload.wikimedia.org/wikipedia/commons/1/1d/20_colleges_with_the_most_alumni_at_Google.png So it will return us the URL of the particular image present at index 0. To fetch another image use 1, 2, 3 . . . . . according to images present in the article. But if you want image to be downloaded into your local directory instead of printing the result then we can use urllib. Here’s the program which will help you to download an image from the link. import urllib.request import wikipedia page = wikipedia.page("Google") image_link = page.images urllib.request.urlretrieve(image_link , "local-filename.jpg") output – the image present at index 0 will be saved as local-filename.jpg into the same directory where your program is saved. The above program will work for python 3.x , if you’re using Python 2.x then please see the program below – import urllib import wikipedia page = wikipedia.page("Google") image_link = page.images urllib.urlretrieve(image_link , "local-filename.jpg") That’s all for this article. For more information please visit- https://pypi.org/project/wikipedia/ If you’ve any problem or suggestion related with this article then please comment below.
Views: 1462 Tech-Gram Academy
Search engine indexing collects, parses, and stores data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process in the context of search engines designed to find web pages on the Internet is web indexing. Popular engines focus on the full-text indexing of online, natural language documents. Media types such as video and audio and graphics are also searchable. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1491 Audiopedia
High Performance Computing, HPC, is a research group at the ISTI institute in Pisa. One of the main activity of HPC-Lab consists in studying applications of query log mining to search. In the last years several results have been proposed by members of the lab. In this talk we will present three recent results: i) A novel effective and efficient query recommendation method based on the concept of Search Shortcuts; ii) A novel recommendation paradigm based on the concept of user task instead of the well-known concept of user query, and iii) A very efficient result diversification algorithm that is based on results from i) and ii).
Views: 101 Компьютерные науки
DevDay (http://devday.pl), 20th of September 2013, Kraków Itamar Syn Hershko - "Full-text search with Lucene and neat things you can do with it" Description: "Apache Lucene is a high-performance, full-featured text search engine library. But it can do an awful lot more than just enable searching on documents or DB records. In this hands-on session we will get to know Lucene and how to use it, and all the great features it provides for applications requiring full-text search capabilities. After understanding how it works under the hood we will learn how to make use of it to do other cool stuff that would amaze our users, get the most out of your applications, and maximize profit. Delivered by Apache Lucene.NET committer Itamar Syn-Hershko, this talk is aimed at providing you tools for starting to work with Lucene, and also to show you how it can be used to provide better UI and UX and to leverage data in use by your application to provide better experience to the user."
Views: 3749 Dev Day
To Get any Project for CSE,IT ECE,EEE Contact Me @9966032699,8519950799 or mail us - [email protected]m-Visit Our WebSite www.liotechprojects.com,www.iotech.in Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.
Views: 109 LT LIOTechprojects
Custom search console uses Google's Webmaster and site verification API to carry out operations. Complete Series will be available soon. Stay Tuned. Series Playlist ============ https://www.youtube.com/watch?v=U-voWnfkzNQ&list=PLC-R40l2hJfdAnJ8KvNPlB2hCysuqV4Da Console.php File ============== https://goo.gl/GeV497 Download Whole Series ==================== https://www.myphpnotes.com/post/custom-google-webmaster-tools-with-site-verification-api Follow us ======== Twitter: https://www.twitter.com/myphpnotes Instagram: https://www.instagram.com/myphpnotes Facebook: https://www.facebook.com/LearningWithExperts/ Request Tutorial =================== https://www.myphpnotes.com/RequestTutorial Paid Projects ================== https://www.myphpnotes.com/RequestTutorial Learn more about Composer: ======================== https://www.youtube.com/watch?v=darYWb_Oml0 Learn more about Virtualhosts: ========================= https://www.youtube.com/watch?v=iBjirLD5X7Q Author ====== Adnan Hussain Turki Facebook: https://www.facebook.com/myPHPnotes Twitter: https://www.twitter.com/AdnanTurki Email: [email protected] Brought to you by: www.myphpnotes.com
Views: 800 myPHPnotes
Applying Geospatial Analytics at a Massive Scale using Kafka, Spark and Elasticsearch on DC/OS - Adam Mollenkopf, Esri This session will explore how DC/OS and Mesos are being used at Esri to establish a foundational operating environment to enable the consumption of high velocity IoT data using Apache Kafka, streaming analytics using Apache Spark, high-volume storage and querying of spatiotemporal data using Elasticsearch, and recurring batch analytics using Apache Spark & Metronome. Additionally, Esri will share their experience in making their application for DC/OS portable so that it can easily be deployed amongst public cloud providers (Microsoft Azure, Amazon EC2), private cloud providers and on-premise environments. Demonstrations will be performed throughout the presentation to cement these concepts for the attendees. About Adam Mollenkopf Esri Real-Time & Big Data GIS Capability Lead Redlands, CA Twitter Tweet Websiteesri.com Adam Mollenkopf is responsible for the strategic direction Esri takes towards enabling real-time and big data capabilities in the ArcGIS platform. This includes having the ability to ingest real-time data streams from a wide variety of sources, performing continuous and recurring spatiotemporal analytics on data as it is received & disseminating analytic results to communities of interest. He leads a team of experienced individuals in the area of stream processing and big data analytics.
Views: 1409 The Linux Foundation