- Introduction to Automated Machine Learning - Sep 15, 2021.
AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.
- How to Create an AutoML Pipeline Optimization Sandbox - Sep 9, 2021.
In this article, we will implement an automated machine learning pipeline optimization sandbox web app using Streamlit and TPOT.
- Fast AutoML with FLAML + Ray Tune - Sep 6, 2021.
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster.
- Be Wary of Automated Feature Selection — Chi Square Test of Independence Example - Aug 5, 2021.
When Data Scientists use chi square test for feature selection, they just merely go by the ritualistic “If your p-value is low, the null hypothesis must go”. The automated function they use behaves no differently.
- Overview of AutoNLP from Hugging Face with Example Project - Jun 21, 2021.
AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.
- Binary Classification with Automated Machine Learning - May 17, 2021.
Check out how to use the open-source MLJAR auto-ML to build accurate models faster.
- Machine Learning Pipeline Optimization with TPOT - May 12, 2021.
Let's revisit the automated machine learning project TPOT, and get back up to speed on using open source AutoML tools on our way to building a fully-automated prediction pipeline.
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021.
Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
- Automated Text Classification with EvalML - Apr 6, 2021.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
- Easy AutoML in Python - Apr 1, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- Google’s Model Search is a New Open Source Framework that Uses Neural Networks to Build Neural Networks - Mar 1, 2021.
The new framework brings state-of-the-art neural architecture search methods to TensorFlow.
- Easy, Open-Source AutoML in Python with EvalML - Feb 16, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Dec 31, 2020.
We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- Algorithms for Advanced Hyper-Parameter Optimization/Tuning - Nov 17, 2020.
In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.
- Top 38 Python Libraries for Data Science, Data Visualization & Machine Learning - Nov 2, 2020.
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
- Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
The new release makes Ludwig one of the most complete open source AutoML stacks in the market.
- Build Your Own AutoML Using PyCaret 2.0 - Aug 20, 2020.
In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.
- Autotuning for Multi-Objective Optimization on LinkedIn’s Feed Ranking - Aug 19, 2020.
In this post, the authors share their experience coming up with an automated system to tune one of the main parameters in their machine learning model that recommends content on LinkedIn’s Feed, which is just one piece of the community-focused architecture.
- GitHub is the Best AutoML You Will Ever Need - Aug 12, 2020.
This article uses PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions.
- Wrapping Machine Learning Techniques Within AI-JACK Library in R - Jul 17, 2020.
The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.
- Automated Machine Learning: The Free eBook - May 18, 2020.
There is a lot to learn about automated machine learning theory and practice. This free eBook can get you started the right way.
- Hands on Hyperparameter Tuning with Keras Tuner - Feb 28, 2020.
Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.
- New Poll: When Will AutoML Replace Data Scientists (if ever)? - Feb 27, 2020.
Take part in the latest KDnuggets poll by weighing in on when you think AutoML and Automated Data Science will replace humans — if ever.
- Practical Hyperparameter Optimization - Feb 13, 2020.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
- Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About - Jan 30, 2020.
Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.
- KDnuggets™ News 20:n04, Jan 29: AutoML: If you try it, you’ll like it more; The Data Science Interview Study Guide - Jan 29, 2020.
AutoML Poll results: if you try it, you'll like it more; The Data Science Interview Study Guide; What Do Data Scientists in Europe Do & How Much Are They Worth?; 2 Questions for a Junior Data Scientist
- AutoML Poll results: if you try it, you’ll like it more - Jan 27, 2020.
The results of latest KDnuggets Poll on AutoML are quite interesting. While most respondents were not happy with AutoML performance, the opinions of those who tried it were higher than those who did not.
- H2O Framework for Machine Learning - Jan 6, 2020.
This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.
- Automated Machine Learning: How do teams work together on an AutoML project? - Jan 2, 2020.
In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.
- KDnuggets Poll: How well do current AutoML solutions work? - Dec 14, 2019.
Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions.
- Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models - Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
- Automated Machine Learning Project Implementation Complexities - Nov 22, 2019.
To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.
- GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.
- Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search - Oct 14, 2019.
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
- Research Guide for Neural Architecture Search - Oct 4, 2019.
In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.
- Automate Hyperparameter Tuning for Your Models - Sep 20, 2019.
When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?
- Automated Machine Learning: Just How Much? - Sep 5, 2019.
This is an interview between Rosaria Silipo and data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, asking a few questions on the topic of automated machine learning from their point of view, and some interesting examples of its practical use.
- KDnuggets™ News 19:n29, Aug 7: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners - Aug 7, 2019.
This week on KDnuggets: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree; How a simple mix of object-oriented programming can sharpen your deep learning prototype; Can we trust AutoML to go on full autopilot?; Ten more random useful things in R you may not know about; 25 Tricks for Pandas; and much more!
- Can we trust AutoML to go on full autopilot? - Jul 31, 2019.
We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.
- Evolving Deep Neural Networks - Jun 18, 2019.
This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural network design.
- Automated Machine Learning 101: Is Your Company Ready? - Mar 8, 2019.
In this webinar from DataRobot, learn common automated machine learning use cases how automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive, and more!
- 3 Reasons Why AutoML Won’t Replace Data Scientists Yet - Mar 6, 2019.
We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.
- Automatic Machine Learning is broken - Feb 19, 2019.
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.
- Accelerating Time Series Analysis with Automated Machine Learning - Feb 14, 2019.
This IDC Solution Spotlight examines how automated machine learning tools can augment the analysis, modeling, and prediction of time series data to deliver easily understood and actionable insights for businesses in a simple and agile fashion. Get the report now.
- Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge - Jan 23, 2019.
This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Healthcare and Automated Machine Learning 101: How Healthcare Providers Can Adopt AI - Dec 12, 2018.
AI, machine learning, and automated machine learning are transforming the healthcare industry and helping to solve some of today’s biggest healthcare challenges. In this DataRobot webinar, Dec 17, 1 PM EST, learn how AI technologies can help healthcare providers improve operational efficiency and patient experience.
- Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
- AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019 - Dec 3, 2018.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.
- Top KDnuggets tweets, Nov 07-13: 10 Free Must-See Courses for Machine Learning and Data Science - Nov 14, 2018.
Also: Best Practices for Using Notebooks for #DataScience; Automated #MachineLearning - results of Gene Feruzza AutoML research.
- Learn how machine learning is transforming business, Nov 12 Webinar - Nov 2, 2018.
In this webinar on Nov 12, titled How to Transform Your Business with Automated Machine Learning, learn the difference between AI, machine learning, and deep learning, the challenges of implementing traditional data science solutions, and how automated machine learning enables more employees to take part in AI initiatives.
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
- Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
- Citizen Data Scientists | Why Not DIY AI? - Oct 17, 2018.
This live debate will feature AutoML advocate Gene Ferruzza (Valassis Digital), countered by Gregory Piatetsky-Shapiro (KDnuggets) who asks if you would really fly with a citizen pilot. Fasten your seatbelts and register now - seats are limited!
- Building Actionable Machine Learning Models using Trifacta, DataRobot, and Tableau - Sep 26, 2018.
In this webinar, learn best practices and practical implementation tips for each step of an automated machine learning project, and see live, hands-on demos of Trifacta, DataRobot, and Tableau.
- KDnuggets™ News 18:n36, Sep 26: Machine Learning Algorithms From Scratch; Deep Learning Framework Popularity; Data Capture, the Deep Learning Way - Sep 26, 2018.
Also: SQL Case Study: Helping a Startup CEO Manage His Data; Building a Machine Learning Model through Trial and Error; The Whys and Hows of Web Scraping; Unfolding Naive Bayes From Scratch; "Auto-What?" - A Taxonomy of Automated Machine Learning
- “Auto-What?” – A Taxonomy of Automated Machine Learning - Sep 25, 2018.
Automated machine learning is a rapidly developing segment of artificial intelligence - it’s time to define what an AutoML product is so end-users can compare product capabilities intelligently.
- Everything You Need to Know About AutoML and Neural Architecture Search - Sep 13, 2018.
So how does it work? How do you use it? What options do you have to harness that power today? Here’s everything you need to know about AutoML and NAS.
- Why Automated Feature Engineering Will Change the Way You Do Machine Learning - Aug 20, 2018.
Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.
- Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code - Aug 17, 2018.
Auto-Keras is an open source software library for automated machine learning. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.
- AutoKeras: The Killer of Google’s AutoML - Aug 15, 2018.
Auto-Keras is an open source "competitor" to Google’s AutoML, a new cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in Neural Architecture Search (NAS).
- GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows - Aug 8, 2018.
This post includes a wide spectrum of data science projects, all of which are open source and are present on GitHub repositories.
- Webinar: Realizing the benefits of Automated Machine Learning, is your organization next? - Aug 2, 2018.
In this live webinar (Aug 8, 1PM EST), discover research findings, best practices for AI adoption, use cases on the growth of machine learning, and how automated machine learning technologies make AI more accessible to organizations of all sizes.
- Google’s AutoML: Cutting Through the Hype - Jul 31, 2018.
In today’s post, I want to look specifically at Google’s AutoML, a product which has received a lot of media attention, and address "What is Google's AutoML?" and more.
- Automated Machine Learning vs Automated Data Science - Jul 2, 2018.
Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.
- Advancing Your Analytics Career With Automated Machine Learning - Apr 5, 2018.
Join DataRobot on Apr 26 at 1:00 pm EST for this webinar, in which industry expert Jen Underwood will show how you can use automated machine learning to quickly develop predictive models and advance your career beyond traditional business intelligence.
- Minimizing Model Risk with Automated Data Preparation & Machine Learning, Apr 19 - Apr 2, 2018.
Join DataRobot, Apr 19 at 2:00 pm ET/11:00 am PT, for a webinar on how to use Automated Data Preparation & Machine Learning to gain a competitive advantage, while quickly aligning your business operations to regulatory requirements.
- Model Risk Management with Automated Machine Learning, Mar 29 Webinar - Mar 9, 2018.
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."
- Deep Feature Synthesis: How Automated Feature Engineering Works - Feb 7, 2018.
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
- Enhancing Customer 360 Models with Automated Machine Learning - Feb 1, 2018.
Join DataRobot on Feb 15 to discover how Automated Machine Learning provides the ability to develop and refresh Customer 360 predictive models, the ability to deploy models with a click of a button, and more!
- Using AutoML to Generate Machine Learning Pipelines with TPOT - Jan 29, 2018.
This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.
- Kogentix Automated Machine Learning Platform - Jan 24, 2018.
Kogentix Automated Machine Learning Platform is the only solution we have seen that runs natively on Spark and includes all of the elements required to build and run a machine learning application.
- Using Genetic Algorithm for Optimizing Recurrent Neural Networks - Jan 22, 2018.
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
- Webinar: Minimizing Model Risk with Automated Machine Learning, Jan 31 - Jan 17, 2018.
See how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements.
- Driverless AI: Fast, Accurate, Interpretable AI - Jan 9, 2018.
H2O.ai recently launched Driverless AI, which speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment.
- Enhancing Anti-Money Laundering Programs with Automated Machine Learning, Jan 11 Webinar - Jan 3, 2018.
In this webinar, Jan 11, DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.
- Advances in Fraud Detection with Automated Machine Learning - Dec 5, 2017.
Join DataRobot, Dec 13, for a webinar discussion of the current state of machine learning in fraud detection and learn how you can stay one step ahead of those looking to harm your business.
- Multichannel Marketing Attribution with Automated Machine Learning, Dec 12 Webinar - Nov 28, 2017.
In this webinar, Dec 12, DataRobot outlines Multichannel Marketing Attribution with Automated Machine Learning, demonstrating how automated machine learning offers the shortest path to success. Space is limited, so sign up now!
- Automated Feature Engineering for Time Series Data - Nov 20, 2017.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
- Webinar: Data Preparation Essentials for Automated Machine Learning, Nov 29 - Nov 16, 2017.
Jen Underwood will review how to organize data in a machine learning-friendly format that accurately reflects the business process and outcomes.
- Webinar: Transform the business with automated embedded Artificial Intelligence, Nov 16 - Nov 6, 2017.
Learn how much value companies can get by adding AI to business applications and processes through AI and automation, how to architect a smart business with ubiquitous AI, and more.
- Multichannel Marketing Attribution with DataRobot – download the report - Oct 27, 2017.
This new report from DataRobot explains the importance of multichannel, multi-touch attribution to accurately measure the success of your marketing efforts — and how automated machine learning offers the shortest path to success.
- [webinar] Getting Started with Automated Analytics Powered By Machine Learning, Nov 8 - Oct 18, 2017.
Join Tellius and industry expert, Jen Underwood on Nov 8 to learn how companies today are moving beyond BI—leveraging automated analytics powered by machine learning to better understand their business.
- Moving from BI to Automated Machine Learning - Sep 26, 2017.
Vast amounts of data are already overwhelming existing BI tools and analytics processes. To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.
- Build a Path to Predictive Analytics with Big Squid & Looker, Aug 24 - Aug 17, 2017.
Big Squid offers a Predictive Analytics Platform that uses automated Machine Learning to take your Looker investment from real-time data and insights to forward-looking action and impact. Learn more on Aug 24.
- DataRobot: The Making of Data Science Superheroes, Webinar August 31 - Aug 11, 2017.
Learn how two data scientists quickly transformed from mere mortals into data science superheroes, now able to tackle more projects with better results - faster than a speeding bullet!
- DataRobot: Become a Data Science Superhero – Watch on Demand - Jul 28, 2017.
A demo of DataRobot will show you how to transform your predictive analytics team into a League of Superheroes, cranking out predictive models at the speed of thought!
- Design by Evolution: How to evolve your neural network with AutoML - Jul 20, 2017.
The gist ( tl;dr): Time to evolve! I’m gonna give a basic example (in PyTorch) of using evolutionary algorithms to tune the hyper-parameters of a DNN.
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- KDnuggets™ News 17:n27, Jul 19: The 4 Types of Data Analytics; Machine Learning Applied to Big Data, Explained - Jul 19, 2017.
The 4 Types of Data Analytics; Machine Learning Applied to Big Data, Explained; Are Most Machine Learning Experts Turning to Deep Learning?; How to Build a Data Science Pipeline; Cartoon: The First Ever Self-Driving, Deep Learning Grill
- DataRobot: Become a Data Science SuperHero, Webinar, July 25 - Jul 14, 2017.
DataRobot machine learning automation platform transforms you from mild-mannered to superhuman in your abilities to develop and deploy highly-accurate predictive models. Learn more in this webinar.
- Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity - Jul 13, 2017.
There is a growing community around creating tools that automate many machine learning tasks, as well as other tasks that are part of the machine learning workflow. The paradigm that encapsulates this idea is often referred to as automated machine learning.
- DataRobot Webinar on June 27, 2017: Automated Machine Learning in Action - Jun 6, 2017.
In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.
- TPOT Automated Machine Learning Competition: Can AutoML beat humans on Kaggle? - Jun 5, 2017.
Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.
- 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.
- 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.
- How Automated ML is Transforming the Predictive Analytics Landscape - Apr 24, 2017.
Learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors, on Tuesday, May 2nd at 1:00 pm ET.
- DataRobot Webinar, May 2: How Automated Machine Learning is Transforming the Predictive Analytics Landscape - Apr 11, 2017.
Learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.
- KDnuggets™ News 17:n03, Jan 25: Automated Machine Learning Overview; Data Science Puzzle; Chatbots on Steroids - Jan 25, 2017.
The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them
- Going to War with the Giants: Automated Machine Learning with MLJAR - Jan 19, 2017.
The performance of automated machine learning tool MLJAR on Kaggle competition data is presented in comparison with those from other predictive APIs from Amazon, Google, PredicSis and BigML.
- The Current State of Automated Machine Learning - Jan 18, 2017.
What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.
- Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer - Oct 28, 2016.
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.
- Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team - Oct 4, 2016.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
- Contest Winner: Winning the AutoML Challenge with Auto-sklearn - Aug 5, 2016.
This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. It is built around the successful scikit-learn library and won the recent AutoML challenge.
- Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising - Aug 4, 2016.
This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.
- Data Science Automation: Debunking Misconceptions - Aug 2, 2016.
This opinion piece aims to clear up some proposed misconceptions surrounding data science automation.
- TPOT: A Python Tool for Automating Data Science - May 13, 2016.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
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