2017 May
All (113) | Courses, Education (11) | Meetings (16) | News, Features (20) | Opinions, Interviews (22) | Software (5) | Tutorials, Overviews (33) | Webcasts & Webinars (6)
- Top KDnuggets tweets, May 24-30: #DataScience for Beginners; 10 Free Must-Read Books for #MachineLearning and #DataScience - May 31, 2017.
Real-time face detection and emotion/gender classification; Top 20 #Python #MachineLearning Open Source Projects, updated; Stanford CS231n lecture slides: #DeepLearning software; #DataScience platforms are on the rise
- Data Science for Newbies: An Introductory Tutorial Series for Software Engineers - May 31, 2017.
This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.
- Get your skills recognised. Get IAPA-certified in Data Analytics - May 30, 2017.
For those analytics professionals who want to prove their worth to the business, an IAPA-Certified via Credential not only recognises your skills but also helps you be the outlier.
- Learn to turn data into revenue at Wharton - May 30, 2017.
Customer Analytics from Wharton Executive Education gives you a deeper, actionable understanding of your data by delving into specific collection methodologies and patterns for predictive behavior, empowering you to make impactful decisions that drive success throughout your company.
- Data preprocessing for deep learning with nuts-ml - May 30, 2017.
Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.
- Qualitative Research Methods for Data Science? - May 30, 2017.
Why on Earth would a data scientist need to know about qualitative research? There are plenty of reasons. Here are a few.
- Must-Know: How to determine the influence of a Twitter user? - May 30, 2017.
The influence of a Twitter user goes beyond the simple number of followers. We also want to examine how effective are tweets - how likely they are to be retweeted, favorited, or the links inside clicked upon. What exactly is an influential user depends on the definition.
- Predictive Analytics Times – This month’s content - May 29, 2017.
Attend the leading event for predictive analytics case studies, expertise, and resources. With conferences strategically scheduled around the globe, you are sure to find a PAW event that will fit your calendar and specific needs. Sign up with code PATIMES17 for 15% off two day and combo passes.
- Challenges in Machine Learning for Trust - May 29, 2017.
With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
- Top Stories, May 22-28: Analytics, Data Science, Machine Learning Software Poll Results; Machine Learning Crash Course - May 29, 2017.
New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll; Machine Learning Crash Course: Part 1; Text Mining 101: Mining Information From A Resume; Data science platforms are on the rise and IBM is leading the way; An Introduction to the MXNet Python API
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Machine Learning Workflows in Python from Scratch Part 1: Data Preparation - May 29, 2017.
This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation. - The Evolving Science of Sentiment and Emotion AI, Sentiment Analysis Symposium, June 27-28 - May 26, 2017.
News, sentiment, and emotion drive markets - consumer markets and financial markets, making text and sentiment analysis essential tools for research and insights. Using code KDNUGGETS to save - early reg by May 31.
- What is an Ontology? The simplest definition you’ll find… or your money back* - May 26, 2017.
This post takes the concept of an ontology and presents it in a clear and simple manner, devoid of the complexities that often surround such explanations.
- An Introduction to the MXNet Python API - May 26, 2017.
This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API. In-depth and descriptive, this is a great guide for anyone looking to start leveraging this powerful neural network library.
- Machine Learning Anomaly Detection: The Ultimate Design Guide - May 25, 2017.
Considering building a machine learning anomaly detection system for your high velocity business? Learn how with Anodot ultimate three-part guide.
- Data science platforms are on the rise and IBM is leading the way - May 25, 2017.
Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.
- How A Data Scientist Can Improve Productivity - May 25, 2017.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
- Unsupervised Investments (II): A Guide to AI Accelerators and Incubators - May 25, 2017.
A meticulously compiled list as extensive as possible of every accelerator, incubator or program the author has read or bumped into over the past months. It looks like there are at least 29 of them. An interesting read for a wide variety of potentially interested parties - far beyond only the investor.
- Will Data Science Eliminate Data Science? - May 25, 2017.
There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.
- Top KDnuggets tweets, May 17-23: Beginner Guide To Understanding Convolutional Neural Networks; Big Data 2017: Top Influencers and Brands - May 24, 2017.
#BigData 2017: Top Influencers and Brands; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience; Good Test for #DeepLearning #ImageRecognition? #Chihuahua or #Muffin
- Live Immersive Predictive Analytics and Data Science Experiential Training. - May 24, 2017.
Successful analytics at the organizational-level starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Live Online in June, Seattle in July and Wash-DC in October.
- DataScience.com Releases Python Package for Interpreting the Decision-Making Processes of Predictive Models - May 24, 2017.
DataScience.com new Python library, Skater, uses a combination of model interpretation algorithms to identify how models leverage data to make predictions.
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Text Mining 101: Mining Information From A Resume - May 24, 2017.
We show a framework for mining relevant entities from a text resume, and how to separation parsing logic from entity specification. - Machine Learning Crash Course: Part 1 - May 24, 2017.
This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.
- Take The Next Step in Your Data Science Career - May 23, 2017.
The Saint Mary's College Master of Science in Data Science program will prepare you to enter into the data analysis process at any stage, from the initial formulation of the question, to visualizing data, to interpreting the results and drawing conclusions.
- Natural Language Generation overview – is NLG is worth a thousand pictures ? - May 23, 2017.
NLG tools automate the analysis and enhance traditional BI platforms by explaining in plain English the significance of visualizations and findings – here is an overview of the market.
- The first ever AI survey for Insurance: Get the low-down on how AI will impact you - May 23, 2017.
Check the new “AI, Analytics and GDPR Survey 2017”, where Insurance Nexus quizzed 250 of the brightest minds in insurance, and learn the latest trends in analytics, AI and GDPR to help you adjust your strategy.
- Why Java is the Language of Choice for the Internet of Things (IoT) - May 23, 2017.
What has caused this Java revival and why is Java so useful in the Internet of Things? Better yet, what is the Internet of Things?
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New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll - May 22, 2017.
Python caught up with R and (barely) overtook it; Deep Learning usage surges to 32%; RapidMiner remains top general Data Science platform; Five languages of Data Science.
- 3 Ways to Move Your Data Science Into Production, May 24 - May 22, 2017.
In this live webinar, on May 24th at 11AM Central, learn how Anaconda empowers data scientists to encapsulate and deploy their data science projects as live applications with a single click.
- Must-Know: Key issues and problems with A/B testing - May 22, 2017.
A look at 2 topics in A/B testing: Ensuring that bucket assignment is truly random, and conducting an A/B test on an opt-in feature.
- Top Stories, May 15-21: Getting Into Data Science: What You Need to Know; The Best Python Packages for Data Science - May 22, 2017.
Getting Into Data Science: What You Need to Know; The Best Python Packages for Data Science; HDFS vs. HBase : All you need to know; What are common data quality issues for Big Data and how to handle them?; Teaching the Data Science Process
- The Path To Learning Artificial Intelligence - May 19, 2017.
Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.
- Simplifying Decision Tree Interpretability with Python & Scikit-learn - May 19, 2017.
This post will look at a few different ways of attempting to simplify decision tree representation and, ultimately, interpretability. All code is in Python, with Scikit-learn being used for the decision tree modeling.
- Webinar: A New Era of Data Science – Unlocking Big Data Insights with Machine Learning and Spark, May 31 - May 19, 2017.
Learn about Big Data technologies and trends, Democratizing Big Data analytics, Big Data and the Cloud, and more in this webcast with top experts Dean Abbott and Mamdouh Refaat.
- The Best Python Packages for Data Science - May 19, 2017.
This report is the second in a series analyzing data science related topics. This time around, specifically, we rank 15 top Python data science packages, hopefully with results of use to the data science community.
- Data Preparation Strategies for Successful Machine Learning - May 18, 2017.
This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.
- Getting Into Data Science: What You Need to Know - May 18, 2017.
Ready to embark on an exciting and in-demand career? Here’s what you need to know about what a data scientist does—and how you can become competitive in this in-demand field.
- Simplifying Data Pipelines in Hadoop: Overcoming the Growing Pains - May 18, 2017.
Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.
- Top KDnuggets tweets, May 10-16: Which Machine Learning algorithm should I use? #cheatsheet - May 17, 2017.
Also HDFS vs. HBase: All you need to know #BigData mini-tutorial; #MachineLearning overtaking #BigData?
- Best Data Science Courses from Udemy (only $10 till May 27) - May 17, 2017.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until May 27, 2017.
- Top Recent Big Data videos on YouTube - May 17, 2017.
Top viewed videos on Big Data since 2015 include Big Data use cases in psychographics, sports, politics and data monetisation.
- Teaching the Data Science Process - May 17, 2017.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
- KDnuggets Free Pass to O’Reilly AI Conference, NYC, June 26-29 - May 16, 2017.
AI is the hottest technology now. You can win KDnuggets free pass to the new AI conference in NYC in from the organizers of Strata + Hadoop World Conferences.
- Learn from Legends in Machine Learning, Open Source in 3 Days - May 16, 2017.
More than 400 of the sharpest minds in the industry will meet at Postgres Vision June 26-28 in Boston. The goal is to envision the future for enterprises striving to harvest greater strategic value and actionable insight from their data.
- Join 1 Million Others on DataCamp (50% off until May 23) - May 16, 2017.
DataCamp is celebrating 1 millions learners on its platform and is offering 50% off for unlimited access until May 23. Learn R and Python for data science interactively at your own pace.
- Data science through the lens of research design - May 16, 2017.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
- Propensity Scores: A Primer - May 16, 2017.
Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.
- Must-Know: What are common data quality issues for Big Data and how to handle them? - May 16, 2017.
Let's have a look at common quality issues facing Big Data in terms of the key characteristics of Big Data – Volume, Velocity, Variety, Veracity, and Value.
- New Book: Mining Your Own Business - May 15, 2017.
It's an easy-to-read, practical primer for C-level to mid-level executives about how to harness the power of analytics to increase organizational effectiveness.
- TDWI Anaheim – Soak Up Analytics and Fun in SoCal, August 6-11, Disneyland - May 15, 2017.
TDWI Anaheim SoCal conference at Disneyland combines business with pleasure and will be the one work event your family begs you to book. Register by June 2 and save up to 30% with code KD30.
- HDFS vs. HBase : All you need to know - May 15, 2017.
Hadoop Distributed File System (HDFS), and Hbase (Hadoop database) are key components of Big Data ecosystem. This blog explains the difference between HDFS and HBase with real-life use cases where they are best fit.
- Top Stories, May 8-14: Annual KDnuggets Data Science Software Poll; Using Deep Learning To Extract Knowledge From Job Descriptions - May 15, 2017.
New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?; Using Deep Learning To Extract Knowledge From Job Descriptions; Deep Learning Past, Present, and Future; The Guerrilla Guide to Machine Learning with R
- Big Data 2017: Top Influencers and Brands - May 15, 2017.
Onalytica's Big Data Influencer report for 2017 is here. Check out the names and brands that have made the list this year, and get up to speed on the latest happenings in Big Data.
- Cartoon: Mother Of All Data. - May 14, 2017.
We revisit KDnuggets Mother's Day Cartoon. Enjoy and don't forget the mothers in your life - Big Data predicted that 67.53% of you would remember.
- Analytic Professionals: Participate in the 2017 Data Science Survey - May 13, 2017.
Please take part in Rexer Analytics Data Science Survey, conducted since 2007. Full results will be available to download later in the year.
- Guarantee yourself a data science career - May 12, 2017.
The Data Science Career Track is the first online bootcamp to guarantee you a data science job or your money back. The application process is selective - start it know.
- Madrid UPM Advanced Statistics and Data Mining Summer School, June 26 – July 7 - May 12, 2017.
The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series, and each has theoretical as well as practical classes, done with R or Python. Early bird till June 5.
- The Two Phases of Gradient Descent in Deep Learning - May 12, 2017.
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!
- Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn - May 12, 2017.
We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.
- Stanford Online Data Mining & Data Science Courses - May 11, 2017.
Stanford Data Mining Courses and Certificates are designed to give you the skills you need to gather and analyze massive amounts of information, and to translate that information into actionable business strategies. Enroll until June 18.
- How Deep Learning Is Changing The Finance and Retail Sectors - May 11, 2017.
Explore the latest advancements in deep learning and their applications in industry at the Deep Learning in Finance Summit and Deep Learning in Retail Summit in London, 1-2 June. Use discount code KDNUGGETS to save 20% off all tickets.
- Data Version Control: iterative machine learning - May 11, 2017.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.
- The Internet of Things in the Cloud - May 11, 2017.
Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.
- The Guerrilla Guide to Machine Learning with R - May 11, 2017.
This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
- Top KDnuggets tweets, May 03-09: New Poll: What software you used for Analytics, Data Science? Approaching (Almost) Any #MachineLearning Problem - May 10, 2017.
Also Is #MachineLearning overtaking #BigData? What Do Frameworks Offer Data Scientists that #Programming Languages Lack?; Seeing Theory - A Brown University visual intro to probability and stats.
- Webinar: Predictive Analytics, Failure to Launch, May 16, June 14 - May 10, 2017.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinars are May 16 and June 14.
- Top 10 Recent AI videos on YouTube - May 10, 2017.
Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.
- The Quant Crunch: The demand for data science skills - May 10, 2017.
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
- 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.
- MLTrain: transitioning academic theory to practice - May 9, 2017.
Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.
- U. of Cincinnati Analytics Summit, May 19 - May 9, 2017.
6th annual University of Cincinnati Analytics Summit will feature three keynote speakers and five all-day analytics tracks.
- What makes Predictive Analytics World for Workforce most interesting? May 14-18, San Francisco - May 9, 2017.
Predictive Analytics World for Workforce is the Most Interesting Conference in the World if You Work in HR. Join us May 14-18 in San Francisco.
- A Data Analyst guide to A/B testing - May 9, 2017.
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.
- Using Deep Learning To Extract Knowledge From Job Descriptions - May 9, 2017.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
- Must-Know: How to determine the most useful number of clusters? - May 9, 2017.
Without knowing the ground truth of a dataset, then, how do we know what the optimal number of data clusters are? We will have a look at 2 particular popular methods for attempting to answer this question: the elbow method and the silhouette method.
- Predictive Analytics World – Learn From Top Practitioners, San Francisco, May 14-18 - May 8, 2017.
Predictive Analytics World for Business focuses on concrete examples of deployed predictive analytics. Join us May 14-18 in San Francisco to learn how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.
- Sales forecasting using Machine Learning - May 8, 2017.
SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.
- Data Insight Leaders Summit, Barcelona, 18-19 Oct 2017 - May 8, 2017.
Data Insight Leaders Summit is 2 value packed days with the most senior speaker faculty of strictly Head’s of Data Science, Advanced Analytics and Business Intelligence, 18-19 October 2017 in Barcelona.
- The Power of Data and Collaboration to Improve Traffic Safety - May 8, 2017.
Datakind, in collaboration with Microsoft, completed significant data-driven projects to improve traffic safety and help save lives in New York City, Seattle, and New Orleans.
- Top Stories, May 1-7: How to Learn Machine Learning in 10 Days; Deep Learning – Past, Present, and Future - May 8, 2017.
How to Learn Machine Learning in 10 Days; Deep Learning – Past, Present, and Future; Keep it simple! How to understand Gradient Descent algorithm; New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
- Data Science & Machine Learning Platforms for the Enterprise - May 8, 2017.
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
- Building, Training, and Improving on Existing Recurrent Neural Networks - May 8, 2017.
In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.
- Top April Stories: 10 Free Must-Read Books for Machine Learning and Data Science - May 6, 2017.
Also Forrester vs Gartner on Data Science Platforms; Top 20 Recent Research Papers on Machine Learning and Deep Learning.
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New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 5, 2017.
Vote in KDnuggets 18th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will clean, analyze, visualize, and publish the results. - Deep Learning in Minutes with this Pre-configured Python VM Image - May 5, 2017.
Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.
- AI, Deep Learning, Machine Learning–Learn It or You’ll Become a Dinosaur (O’Reilly AI, NYC, Jun 26-29) - May 5, 2017.
Learn how to implement AI in real-world projects today and explore what the future holds for intelligence engineering at O'Reilly's AI Conference, NYC, June 26-29. Save extra 20% with code PCKDNG.
- Big Data Toronto Launches New Era With Triple Threat Data, AI, IoT Conference, June 20-21 - May 5, 2017.
Taking place June 20-21 at the Metro Toronto Convention Centre, Big Data Toronto 2017 will host three co-located conferences that are free to attend for data and AI professionals.
- Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow - May 5, 2017.
Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI
- DataScience.com New Update Aims to Be Industry-Leading Enterprise Data Science Platform - May 4, 2017.
DataScience.com’s enterprise data science platform can now be deployed on-premises or in the cloud. New features include scalable infrastructure, intuitive project organization, and task automation.
- Advances in AI & Deep Learning: DeepMind, Facebook & OpenAI - May 4, 2017.
RE•WORK would like to update KDnuggets readers on their upcoming European events, as discounted tickets end next week, and share their on-demand content and expert interviews! For 20% off pass prices for all RE•WORK events, use discount code KDNUGGETS.
- Machine Learning overtaking Big Data? - May 4, 2017.
Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.
- 42 Essential Quotes by Data Science Thought Leaders - May 4, 2017.
42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.
- Do We Need Balanced Sampling? - May 4, 2017.
Resampling is a solution which is very popular in dealing with class imbalance. Our research on churn prediction shows that balanced sampling is unnecessary.
- How to Fail with Artificial Intelligence: 9 creative ways to make your AI startup fail - May 4, 2017.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
- Top KDnuggets tweets, Apr 26 – May 02: Face Recognition with Python, in under 25 lines of code - May 3, 2017.
Face Recognition with Python, in under 25 lines of code; Try #DeepLearning in #Python w. a fully pre-configured VM; Homo Bayesians #MachineLearning #humor #cartoon; The Most Popular Language For #MachineLearning, #DataScience Is ...
- Technically Speaking – Analytic solutions to real-world problems - May 3, 2017.
Are you and your data "having issues?" JMP real-world case studies help you solve them with key insights on overcoming the challenges with data collection, preparation, and analysis.
- Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- Did you know cavemen were already dealing with “Big Data” issues? - May 3, 2017.
We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.
- Pros and Pitfalls of Observational Research - May 3, 2017.
Why the connection between beer brand and region? Climate? Tradition? Or simply distribution? Some combination of the three, plus other factors?
- Upcoming Meetings in Analytics, Big Data, Data Science, Machine Learning: May and Beyond - May 2, 2017.
Coming soon: TDWI Chicago Conference, PAW San Francisco, #BAChicago, Apache: Big Data Miami, Train AI SanFran, Strata + Hadoop World London, Deep Learning Summit Boston, Spark Summit San Francisco, Postgres Vision Boston, and more.
- Learn to collect, classify, analyze, and model data - May 2, 2017.
The courses offered in the Penn State World Campus 30-credit online Master of Professional Studies in Data Analytics – Business Analytics Option could enhance your potential in this growing field.
- You Scored 200 Dollars Off Open Source Data Event in Boston - May 2, 2017.
Use code KDPV17 to save on Postgres Vision, June 26-28, 2017, at the Royal Sonesta Boston. Co-hosted by EnterpriseDB and MIT, the event sponsors include Amazon Web Services, Avnet, credativ, EnterpriseDB, IBM, Microsoft, MIT, NEC, Palisade Compliance, Quest, TechData, and The Executive Council.
- DataRobot Webinar, June 6: How Automated Machine Learning is Transforming the Predictive Analytics Landscape - May 2, 2017.
Built for speed and scalability, DataRobot radically reduces the time of data science projects - from data to deployment, enabling organizations to bring products to market and react to changing conditions faster. Learn more in June 6 webinar and live demo.
- Deep Learning – Past, Present, and Future - May 2, 2017.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
- What Do Frameworks Offer Data Scientists that Programming Languages Lack? - May 2, 2017.
While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.
- Must-Know: What is the idea behind ensemble learning? - May 2, 2017.
In ensemble methods, more diverse the models used, more robust will be the ultimate result.
- Predictive Analytics World for Business, Chicago – early bird ends May 5 - May 1, 2017.
Predictive Analytics World for Business is coming to Chicago, June 19-22, 2017. NOW is the time to save $400! Register before early bird pricing ends May 5, and learn from industry leading firms.
- Top Stories, Apr 24-30: Guerrilla Guide to Machine Learning with Python; Understand the Gradient Descent Algorithm - May 1, 2017.
The Guerrilla Guide to Machine Learning with Python; How to understand Gradient Descent algorithm; Cartoon: Machine Learning – What They Think I Do; AI & Machine Learning Black Boxes: The Need for Transparency and Accountability; How to Build a Recurrent Neural Network in TensorFlow
- The 2017 Data Scientist Report is now available - May 1, 2017.
For the third year in a row, CrowdFlower surveyed data scientists (nearly 200 this year) from all manner of organizations, which they have compiled into one free report which you can be downloaded now. This year, lots of insights into the word of AI are included.
- How Not To Program the TensorFlow Graph - May 1, 2017.
Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.
- How to Learn Machine Learning in 10 Days - May 1, 2017.
10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.
- The Guerrilla Guide to Machine Learning with Python - May 1, 2017.
Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.
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