Essentials of Machine Learning Algorithms (with Python and R Codes) Introduction Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal. – Eric Schmidt (Google Chairman) We are probably living in the most defining period of human history.
The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing. Who can benefit the most from this guide? What I am giving out today is probably the most valuable guide, I have ever created. The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. I have deliberately skipped the statistics behind these techniques, as you don’t need to understand them at the start. 1. Flow — H2O.ai (0xData) - Fast Scalable Machine Learning. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Weka is a collection of machine learning algorithms for data mining tasks.
The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. Pure Data — Pd Community Site. Which Skills Should I Learn to Become a Big Data Engineer? - Mutable Ideas. A few days ago I received an email from a student of Universidad Tecnológica Nacional asking me for advice about what kind of skills he needed acquire to be hired as Big Data Engineer, I felt it was something worth writing about and hopefully it can generate a sane debate and help more people.
Disclosure The frameworks and books I’m referring here are based on my personal experience working on Digital Marketing and Analytics industry, if you want to learn bigdata to work on DNA/medical research or stocks trading, this post probably offers an incomplete reference. You may also take this post as a reference if you want to work on Analytics companies similar to Socialmetrix. I also assume you are a Senior Developer who understand general propose programming languages as Java or Python, you can manage a Linux boxes (plural) and understand SQL databases, ETL processes, etc. Big Data Platform simplifies working with Hadoop on Windows. Expand All Road map What is the planned update cycle? Big Data, How to Detect Relationships Between Categorical Variables. The goal of the techniques described in this topic is to detect relationships or associations between specific values of categorical variables in large data sets.
This is a common task in many data mining projects as well as in the data mining subcategory text mining. These powerful exploratory techniques have a wide range of applications in many areas of business practice and also research - from the analysis of consumer preferences or human resource management, to the history of language. These techniques enable analysts and researchers to uncover hidden patterns in large data sets, such as "customers who order product A often also order product B or C" or "employees who said positive things about initiative X also frequently complain about issue Y but are happy with issue Z. " How association rules work. Hadoop Training. Databases. A Carefully Selected List of Recommended Tools on Datavisualization.ch.
When I meet with people and talk about our work, I get asked a lot what technology we use to create interactive and dynamic data visualizations.
At Interactive Things, we have a set of preferred libraries, applications and services that we use regularly in our work. We will select the most fitting tool for the job depending on the requirements of the project. Sometimes a really simple tool is all you need to create something meaningful. On other occasions, a more multifaceted repertoire is needed. But how does one choose the right thing to use? That’s why we have put together a selection of tools that we use the most and that we enjoy working with. Let me answer the most likely questions right away: No, not everything find its’ way into this list, so you might not find your personal favorite. Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook. This free ebook has been provided as a public service since 1995.
Statistics: Methods and Applications textbook offers training in the understanding and application of statistics and data mining. It covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. The Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by "modules", representing classes of analytic techniques.
A glossary of statistical terms and a list of references for further study are included. You have filtered out all documents. 7 Data Presentation Tips: Think, Focus, Simplify, Calibrate, Visualize. 8 cool tools for data analysis, visualization and presentation. Reporters wrangle all sorts of data, from analyzing property tax valuations to mapping fatal accidents -- and, here at Computerworld, for stories about IT salaries and H-1B visas.
In fact, tools used by data-crunching journalists are generally useful for a wide range of other, non-journalistic tasks -- and that includes software that's been specifically designed for newsroom use. And, given the generally thrifty culture of your average newsroom, these tools often have the added appeal of little or no cost. Unpivoting Data with Excel, Open Refine and Python. "How can I unpivot or transpose my tabular data so that there's only one record per row?
" I see this question a lot and I thought it was worth a quick Friday blog post. Data often aren’t quite in the format that you want. Web-based visualisation tools. Professor of Statistics Monash University, Australia. Your Guide to Data Science Graduate Programs. Home · neo4j-contrib/graphgist Wiki. GraphAcademy. Data Blending and Advanced Analytics Software - Alteryx Analytics. Government. Neo4j Visualization Tools. Test Data Generation. The Omega Project for Statistical Computing. R Sites. Neo4j, the World's Leading Graph Database. Neo4J - Graph DB. Test Data Generation. Data-Science Tools. Machine-Learning & NNGA. OData - The Protocol for REST APIs.
Statistical foundations of machine learning. Apache Hadoop Distribution. Enthought Scientific Computing Solutions. Transforming Curiosity into Insight. Go/r-resources.md at master · datasciencemasters/go. Learn Data Science by nborwankar. The Open Source Data Science Masters. 6 dataset lists curated by data scientists. Docs Blog 6 dataset lists curated by data scientists November 21, 2013 Scott Haylon Since we do a lot of experimenting with data, we’re always excited to find new datasets to use with Mortar.
We’re saving bookmarks and sharing datasets with our team on a nearly-daily basis. Hadoop Training. Graph DBs. Hadoop Training. Elephant Scale : Big Expertise in Big Data. Online Documents - RDataMining.com: R and Data Mining. Big Data Tools. Machine Data. Understanding Big Data. Big data is a term that has grown to such popularity in tech circles that many have already dismissed it as little more than a buzzword.
But behind all the hype there is some substance; large datasets are being used by businesses in new and innovative ways to create tangible results. Understanding the reality of big data can be a challenge; it is a field that is still growing and evolving. Here are a collection of articles that touch on some of the major themes in the domain of big data. 1. Challenges in Big Data Jose Luis Pelaez/The Image Bank/Getty Images As businesses have increased their ability to collect data, the technological challenges of working with the data have evolved. 2. Kaggle: Go from Big Data to Big Analytics. Data Science Learning Resources. The surge in popularity of the notion of big data has already made the term prone to some major backlash.
But despite many tech pundits dismissing big data as little more than a buzzword, it appears that the labor market is indicating that talented people with a facility for handling data are continuing to be highly in demand, and this demand appears to be growing. Data science is a new field, one that has risen to prominence with all the attention to big data. Tips for entering the field of data analytics. Data analytics is a hot and happening field. Here are some tips for getting your foot in the door. One of our guest speakers at this year's TechRepublic Event was Grace Simrall, a data analyst at iGlass Analytics, where she helps companies in a wide variety of verticals (healthcare, retail, finance) achieve their Business Intelligence goals.
Khan Academy. Free Online Course Materials. Intro to Artificial Intelligence Class Online. Introduction to Statistics. Big Data University. Data analytics: Tips for entering the field. Data analytics is a hot and happening field. Here are some tips from a data analyst for how to get started in the field.
Grace Simrall is a data analyst and founder of iGlass Analytics, where she helps companies in a wide variety of verticals (healthcare, retail, finance) achieve their Business Intelligence goals. I asked Grace what she would recommend someone do if he or she were interested in the field of data analysis. She said she thought it was too soon to tell the value of certification or master's degree programs in analytics and that it was probably a good idea to actually try out the work before investing in a degree. She said that it's more important to demonstrate that you're able to get your hands dirty and execute what's asked for.
In the meantime, she recommends several free online courses to help you learn what's involved in the field of analytics and whether you think you'll like it. 1. bigdatauniversity.com - Free Hadoop courses 2. 3. 4. 5.