Join us in Las Vegas, June 3-7, 2018 and be there to witness these industry heavyweights share their knowledge in can't miss keynote sessions. Register by April 27 to save hundreds with current regular rates.
Learn how make great visualizations using Dash with advanced data visualization workshops for Dash, R, Shiny and Dash R from April 14–15 in Boston, featuring Chris Parmer, the creator of Dash and co-founder of Plotly. Use code KDNUGGETS for 20% off.
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
Also: Introduction to k-Nearest Neighbors; Descriptive Statistics: The Mighty Dwarf of Data Science; 8 Common Pitfalls That Can Ruin Your Prediction; Will GDPR Make Machine Learning Illegal?; Top 20 Python AI and Machine Learning Open Source Projects
Data Science and Machine Learning are applicable very widely, so it is interesting to see how the application areas change. KDnuggets was running this question each year since 2006, so please vote and we will analyze the results and report the trends.
Many underestimate the role of humans in successful deployment of AI solutions. Alegion engine produces AI training data and enables content moderation, sentiment analysis, data enrichment, tagging, categorization, and more.
The continued growth of big data, both in terms of quality and accessibility, is disrupting a wide range of roles. The skills needed to analyse this data need to be learned by everyone - not just data scientists.
At AnacondaCON 2018 in Austin, Apr 8-11, you'll learn how data scientists are using GPUs for machine learning across a variety of applications and industries. The best part? One lucky attendee will receive a FREE NVIDIA TITAN V GPU!
We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
There are limits to what the state-of-the-art is capable of, which doesn’t mean that there aren’t tons of perfect use cases for machine learning, but does mean that you have to go into the process with your eyes open.
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. Each course has theoretical and practical classes, the latter done with R or Python.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
Also: Introduction to Markov Chains; Introduction to Optimization with Genetic Algorithm; Top 5 Best Jupyter Notebook Extensions; Top 5 Best Jupyter Notebook Extensions; 5 Things to Know Before Rushing to Start in Data Science
To help you become a Data Scientist, we put together a guide with answers to: how do you break into the profession? What skills do you need to become a data scientist? Where are best data science jobs?
The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.
Join us in San Francisco or Chicago this spring for the next chapters of the World-renowned Data & Analytics Innovation events, bringing together the top minds in Big Data and Analytics across industries.
Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.
The Data Science Learnathon is coming to the US, coast to coast. Learn how to use open source, GUI driven KNIME Analytics Platform. We’ll provide datasets, jump-start workflows, solutions, and of course data science experts. Sign-up now.
Analytics is becoming important part of maintenance, with applications to analyzing part failures, using failure distributions to simulate product life, and determining the root cause of failures. We provide an overview of predictive maintenance, its usage and key issues to be considered.
Also: 5 Things to Know About Machine Learning; 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Great Data Scientists Don't Just Think Outside the Box, They Redefine the Box; Is an AI /machine-driven world better than a human driven world?
Never fear, workforce analytics is here. We've put together a dedicated track at the 2018 Mega-PAW event in Las Vegas, Jun 3-7, covering Workforce analytics (retaining & optimizing HR with analytics), benefiting your work in HR analytics and beyond.
Model Risk Management has recently become a very hot topic in regulatory and compliance-rich industries. Join DataRobot on Mar 29, 2018 for a webinar titled "Model Risk Management with Automated Machine Learning."
2018 conference will gather 1,000 experts and thought leaders in industry, government, and academia, with over 150 sessions by leading universities and organizations. Register by March 12 to get early rates.
There are good reasons to want to use R for text processing, namely that we can do it, and that we can fit it in with the rest of our analyses. Furthermore, there is a lot of very active development going on in the R text analysis community right now.
The AI Conference returns to New York, Apr 28-May 2. No other conference delivers the depth and breadth in technical content combined with an applied industry focus. Save an extra 20% on most passes with code PCKDNG.
Mobile platforms are set to benefit from Deep Learning this year, with significant improvements in privacy, offline functionality and much more. But which Android phone should you purchase to maximise these benefits?
The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.
Also: Introduction to Functional Programming in #Python; 5 Quick and Easy #DataVisualization in #Python with Code; Predicting the 2018 #Oscar Winners with #MachineLearning; #Keras implementations of Generative Adversarial Networks
The agenda is live for Predictive Analytics World for Healthcare, Jun 3-7 in Las Vegas, and the conference schedule features five information packed days filled with insightful keynote presentations, interactive workshops, actionable seminars, and great networking opportunities.
Accelerate AI (X AI) brings business professionals, management and thought leaders together with executive speakers and AI experts to learn one of the most important skills necessary for future success, artificial intelligence. Save 60% with code KDNUGGETS.
On the positive side of AI we have a prospect of self-driving cars, and other benefits, and thru education humans can evolve and improve. The risks include loss of jobs, growing inequality, dealing with superintelligence.
Also: Top 20 Python AI and Machine Learning Open Source Projects, A Tour of The Top 10 Algorithms for Machine Learning Newbies; 8 Neural Network Architectures Machine Learning Researchers Need to Learn.
The 5th AI+Blockchain NEXTCon brings 50+ tech lead speakers from Microsoft, Google, Facebook, LinkedIn, Uber, other leading firms to share best practices and solutions in machine learning, deep learning, NLP, Data science, Blockchain and more. Save 30% by Mar 9 with code KDNUGGET100.
I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others.
Fashion industry is an extremely competitive and dynamic market. Trends and styles change with the blink of an eye. Data Science can be used here on historical data to predict the trends which will be “Hot” hence potentially saving a lot of time and money.