- Comet.ml – Machine Learning Experiment Management - Apr 9, 2018.
This article presents comet.ml – a platform that allows tracking machine learning experiments with an emphasis on collaboration and knowledge sharing.
- Machine Learning for Text - Apr 9, 2018.
This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Apr 9, 2018.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Health Care and Fraud Detection are gaining. Anti-spam, Manufacturing, and Social are the fastest growing sectors in 2017, while Oil / Gas / Energy and Social Networks analysis have declined.
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- Build a Foundation that Supports AI and Machine Learning - Apr 6, 2018.
In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
- Top Data Science, Machine Learning Courses from Udemy – April 2018 - Apr 5, 2018.
Udemy April $10.99 sale is now going on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- What Does GDPR Mean for Machine Learning? - Apr 4, 2018.
This post investigates how the GDPR, which comes into force at the end of May, will effect machine learning.
- Supervised vs. Unsupervised Learning - Apr 4, 2018.
Understanding the differences between the two main types of machine learning methods.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: April and Beyond - Apr 3, 2018.
Coming soon: AnacondaCON Austin, QCon.ai SF, INFORMS Baltimore, AI Conference NYC, Data Science Salon Dallas, AI Expo Global London, ODSC Boston, and many more.
- Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
- Foot Locker: Sr Solutions Architect – Machine Learning and AI Technologies - Mar 30, 2018.
Seeking a candidate to lead the data driven transformation of Foot Locker in partnership with members of the data, CX and infrastructure teams. This role has end-to-end responsibilities for our ML/AI/Cognitive platform - from design, thru technical specification, to delivery.
- 5 Things You Need to Know about Reinforcement Learning - Mar 28, 2018.
With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.
- MLaaS: Best Practices for Machine Learning as a Service platform, Apr 5 Webinar - Mar 26, 2018.
Learn how Machine Learning as a Service initiatives bring Data Scientists, IT and Analytic Operations together to deploy and scale more models.
- New KDnuggets Poll: Where did you apply Analytics, Data Science, Machine Learning methods in 2017? - Mar 25, 2018.
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.
- Introduction to k-Nearest Neighbors - Mar 22, 2018.
What is k-Nearest-Neighbors (kNN), some useful applications, and how it works.
- CatBoost vs. Light GBM vs. XGBoost - Mar 22, 2018.
Who is going to win this war of predictions and on what cost? Let’s explore.
- Score a Nvidia Titan V GPU at AnacondaCON 2018 - Mar 21, 2018.
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!
- KDnuggets™ News 18:n12, Mar 21: Will GDPR Make Machine Learning Illegal?; 5 Things You Need to Know about Big Data - Mar 21, 2018.
Also: A Beginner's Guide to Data Engineering - Part II; Introduction to Optimization with Genetic Algorithm; Introduction to Markov Chains; Your free 70-page guide to a career in data science
- Making Machine Learning Simple - Mar 20, 2018.
Learn how to build better models with support for multiple data sources and feature extraction at scale, simplify operations with on-demand cluster management, and more.
- What Machine Learning Isn’t - Mar 20, 2018.
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.
- Multiscale Methods and Machine Learning - Mar 19, 2018.
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.
- Quick Feature Engineering with Dates Using fast.ai - Mar 16, 2018.
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.
- So, How Many Machine Learning Models You Have NOT Built? - Mar 14, 2018.
Investigating how data scientists approach machine learning and applying this to the 'ship repair man' analogy.
- Will GDPR Make Machine Learning Illegal? - Mar 14, 2018.
Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.
- KDnuggets™ News 18:n11, Mar 14: Two sides of getting a job as a Data Scientist; 5 things to know about Machine Learning - Mar 14, 2018.
Also 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Great Data Scientists Don't Just Think Outside the Box; Favorite Data Science / Machine Learning Blog; Text Processing in R.
- How to do Machine Learning Efficiently - Mar 13, 2018.
I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this.
- Top Data Science, Machine Learning Courses from Udemy – March 2018 - Mar 12, 2018.
Udemy St Patrick's Day $11.99 sale on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- 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."
- Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
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.
- 5 Things to Know About Machine Learning - Mar 7, 2018.
This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.
- The 5th AI+Blockchain NEXTCon, Santa Clara, April 10-13, 2018 - Mar 5, 2018.
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.
- Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.
- TDWI Chicago, May 6-11: Get Your Hands Dirty With Data – KDnuggets Offer - Mar 2, 2018.
Attend the Hands-on Lab series and bring practical skills back from Chicago. Save 30% through March 16 with priority code KD30.
- How data science can improve retail - Mar 1, 2018.
We’re going to take a look at a few surprising ways that data science can increase your sales, both offline and online.
- Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and #MachineLearning Open Source Projects; Intro to Reinforcement Learning Algorithms - Feb 28, 2018.
Also: #NeuralNetwork #AI is simple. So... Stop pretending; 5 Free Resources for Getting Started with #DeepLearning for Natural Language Pro; Want a Job in #Data? Learn This
- Jupyter Pop-up coming to Boston on March 21 - Feb 28, 2018.
Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.
- McKinsey Analytics Online Hackathon, 10 March, 2018 - Feb 28, 2018.
Calling all coders and data scientists to join McKinsey 24-hour hackathon on March 10, 2018. Win All-expenses paid trip to a tech conference of your choice.
- The Current Hype Cycle in Artificial Intelligence - Feb 28, 2018.
Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.
- KDnuggets™ News 18:n09, Feb 28: Gartner 2018 MQ for Data Science/ML – Gainers and Losers; Comparative Analysis of Top 6 BI/Data Viz Tools - Feb 28, 2018.
A Comparative Analysis of Top 6 BI and Data Visualization Tools; A Tour of The Top 10 Algorithms for Machine Learning Newbies; A Guide to Hiring Data Scientists.
- Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 27, 2018.
We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.
- Applying Machine Learning to DevOps - Feb 27, 2018.
This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.
- How Machine Learning is Advancing Data Centers - Feb 26, 2018.
Big Data revolution led to the explosion in Data Centers, which are consuming energy at increasingly higher rate. This blog reviews 2 standard methods for improving data center efficiency and argues that 3rd method - Machine Learning - is the best solution.
- Gartner 2018 Magic Quadrant for Data Science and Machine Learning – Read the report - Feb 23, 2018.
Read Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, courtesy of Domino, and learn which data science platform is right for your organization and why Domino was named a Visionary.
- Age of AI Conference 2018 – Day 2 Highlights - Feb 23, 2018.
Here are some of the highlights from the second day of the Age of AI Conference, February 1, at the Regency Ballroom in San Francisco.
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- Top 20 Python AI and Machine Learning Open Source Projects - Feb 20, 2018.
We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.
- 5 Things You Need To Know About Data Science - Feb 19, 2018.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
- Logistic Regression: A Concise Technical Overview - Feb 16, 2018.
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
- Resurgence of AI During 1983-2010 - Feb 16, 2018.
We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.
- Cartoon: Machine Learning Problems in 2118 - Feb 14, 2018.
For Valentine's day, new KDnuggets cartoon looks at some problems Machine Learning can face in 2118.
- KDnuggets™ News 18:n07, Feb 14: 5 Machine Learning Projects You Should Not Overlook; Intro to Python Ensembles - Feb 14, 2018.
5 Machine Learning Projects You Should Not Overlook; Introduction to Python Ensembles; Which Machine Learning Algorithm be used in year 2118?; Fast.ai Lesson 1 on Google Colab (Free GPU)
- Last chance to register to attend DataScience: Elevate in San Francisco - Feb 12, 2018.
DataScience: Elevate will be held Feb 22 in San Francisco. Register to be a part of a full day of panels and presentations from people and companies at the forefront of data science.
- 4 Things You Probably Didn’t Know Machine Learning and AI was used for - Feb 12, 2018.
AI was compared to the discovery of fire, but its impact hinges on how creative we are with the technology—just like it did for early humans employing fire. Here are four diverse examples of applied AI to get your creative juices flowing.
- Which Machine Learning Algorithm be used in year 2118? - Feb 9, 2018.
So what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.
- Introduction to Python Ensembles - Feb 9, 2018.
In this post, we'll take you through the basics of ensembles — what they are and why they work so well — and provide a hands-on tutorial for building basic ensembles.
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- Top 15 Scala Libraries for Data Science in 2018 - Feb 9, 2018.
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
- 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- AI & Machine Learning: the key skills every software engineer needs in 2018 - Feb 7, 2018.
Designed specifically by, and for, senior software engineers, architects, and technical engineering managers, QCon.ai is a dedicated conference for AI and machine learning. Use code KDnuggets by Feb 17 to save.
- 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.
- KDnuggets™ News 18:n06, Feb 7: 5 Fantastic Practical Machine Learning Resources; 8 Must-Know Neural Network Architectures - Feb 7, 2018.
5 Fantastic Practical Machine Learning Resources; The 8 Neural Network Architectures Machine Learning Researchers Need to Learn; Generalists Dominate Data Science; Avoid Overfitting with Regularization; Understanding Learning Rates and How It Improves Performance in Deep Learning
- Register for DataScience: Elevate Livestream, Feb 22 - Feb 6, 2018.
DataScience: Elevate will be held Feb 22 in San Francisco. Register now for the livestream to tune into a full day of panels and presentations from people and companies at the forefront of data science.
- 5 Fantastic Practical Machine Learning Resources - Feb 6, 2018.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
- The Voleon Group: Machine learning research, software development, & referral opportunities - Feb 6, 2018.
Seeking exceptionally talented machine learning researchers and senior software engineers. Candidates who will be available within the next 12 months will be considered.
- Future Trends in Biometrics - Feb 5, 2018.
Biometric identification is moving from the realm of high -tech movie scenes to everyday use. The science is already changing physical and cyber security.
- A Simple Starter Guide to Build a Neural Network - Feb 5, 2018.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
- Avoid Overfitting with Regularization - Feb 2, 2018.
This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.
- The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - Jan 31, 2018.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
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- Automated Text Classification Using Machine Learning - Jan 30, 2018.
In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.
- Data Structures Related to Machine Learning Algorithms - Jan 30, 2018.
If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.
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- Webinar: AI, Machine Learning and Chatbots Improving Insurance Profitability & CX, Feb 15 - Jan 29, 2018.
Join Insurance Nexus as we talk to MetLife, Chubb and Nationwide about how to prioritize investments and internal resources. Learn which innovations will have the biggest impact on customer experience and improved profitability.
- Error Analysis to your Rescue – Lessons from Andrew Ng, part 3 - Jan 29, 2018.
The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm
- KDnuggets™ News 18:n04, Jan 24: TensorFlow vs XGBoost; Machine Learning Pipelines in Python; Semi-Supervised Machine Learning - Jan 24, 2018.
Gradient Boosting in TensorFlow vs XGBoost; Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2; Using Genetic Algorithm for Optimizing Recurrent Neural Networks; The Value of Semi-Supervised Machine Learning; Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI
- Machine Learning Model Metrics - Jan 23, 2018.
In this article we explore how to calculate machine learning model metrics, using the example of fraud detection. We'll see lots of different ways that we can try to understand just how good our learned model is.
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- Deep Learning in H2O using R - Jan 22, 2018.
This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.
- Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI - Jan 22, 2018.
A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.
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- Are you monitoring your machine learning systems? - Jan 18, 2018.
How are you monitoring your Python applications? Take the short survey - the results will be published on KDnuggets and you will get all the details.
- The Value of Semi-Supervised Machine Learning - Jan 17, 2018.
This post shows you how to label hundreds of thousands of images in an afternoon. You can use the same approach whether you are labeling images or labeling traditional tabular data (e.g, identifying cyber security atacks or potential part failures).
- Learning Curves for Machine Learning - Jan 17, 2018.
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
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- The LION WAY, v. 3.0: Machine Learning plus Intelligent Optimization – Free Download - Jan 16, 2018.
This newly revised book presents two topics which are in most cases separated: machine learning (the design of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions). Free download!
- Beyond Word2Vec Usage For Only Words - Jan 11, 2018.
A good example on how to use word2vec in order to get recommendations fast and efficiently.
- Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions - Jan 11, 2018.
Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
- Top 10 TED Talks for Data Scientists and Machine Learning Engineers - Jan 10, 2018.
A comprehensive and diverse compilation of TED talks to understand the big picture of AI and Machine Learning.
- Regularization in Machine Learning - Jan 10, 2018.
Regularization is a technique that helps to avoid overfitting and also make a predictive model more understandable.
- KDnuggets™ News 18:n02, Jan 10: Quantum Machine Learning; AI & Blockchain Convergence; Building a Successful Analytics Dept - Jan 10, 2018.
Quantum Machine Learning: An Overview; How to build a Successful Advanced Analytics Department; Top Data Science, Machine Learning Courses from Udemy; Supercharging Visualization with Apache Arrow; The Convergence of AI and Blockchain: What's the deal?
- 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.
- Training Sets, Test Sets, and 10-fold Cross-validation - Jan 9, 2018.
More generally, in evaluating any data mining algorithm, if our test set is a subset of our training data the results will be optimistic and often overly optimistic. So that doesn’t seem like a great idea.
- Introductory Data Concepts: Fantastic Video Tutorials from Ronald van Loon - Jan 8, 2018.
Check out these introductory data videos from noted expert and influencer Ronald van Loon.
- Top Data Science, Machine Learning Courses from Udemy - Jan 5, 2018.
Enjoy the New Year sale on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- Quantum Machine Learning: An Overview - Jan 5, 2018.
Quantum Machine Learning (Quantum ML) is the interdisciplinary area combining Quantum Physics and Machine Learning(ML). It is a symbiotic association- leveraging the power of Quantum Computing to produce quantum versions of ML algorithms, and applying classical ML algorithms to analyze quantum systems. Read this article for an introduction to Quantum ML.
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- How AI Learns What You’re Willing to Pay - Dec 28, 2017.
Why are we all paying different prices? Is it price "personalization" or price "discrimination"? The answer isn't so simple.
- 15 Minute Guide to Choose Effective Courses for Machine Learning and Data Science - Dec 28, 2017.
Advice for young professionals in non-CS field who wants to learn and contribute to data science/machine learning. Curated from personal experience.
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- How to Improve Machine Learning Algorithms? Lessons from Andrew Ng, part 2 - Dec 21, 2017.
The second chapter of ML lessons from Ng’s experience. This one will only be talking about Human Level Performance & Avoidable Bias.
- How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? - Dec 20, 2017.
When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.
- KDnuggets™ News 17:n48, Dec 20: Machine Learning 2017 Key Trends; New Poll: When is AGI Coming?; AI Year End Roundup - Dec 20, 2017.
Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018; New Poll: When will Artificial General Intelligence (AGI) be achieved?; Xavier Amatriain's Machine Learning and Artificial Intelligence Year-end Roundup; How to Generate FiveThirtyEight Graphs in Python; Transitioning to Data Science: How to become a data scientist
- $5 Data science eBooks and videos from Packt - Dec 19, 2017.
Check Packt $5 sale on every ebook and video, including many great titles on Data Analysis, Machine Learning, Python, Deep Learning, and more - sale runs until Jan 15, 2018.
- Getting Started with TensorFlow: A Machine Learning Tutorial - Dec 19, 2017.
A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.
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- NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
- Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018 - Dec 15, 2017.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.
- Best Data Science, Machine Learning Courses from Udemy, only $10 until Dec 21 - Dec 14, 2017.
Holiday Dev & IT sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Dec 21, 2017.
- Xavier Amatriain’s Machine Learning and Artificial Intelligence Year-end Roundup - Dec 14, 2017.
So much has happened in the world of AI that it is hard to fit in a couple of paragraphs. Here is my attempt.
- How to Improve Machine Learning Performance? Lessons from Andrew Ng - Dec 13, 2017.
5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.
- KDnuggets™ News 17:n47, Dec 13: Top Data Science, Machine Learning Methods in 2017; Main Data Science Developments in 2017, Key Trends; Lunch Break with Keras - Dec 13, 2017.
Also: Managing Machine Learning Workflows with Scikit-learn Pipelines; Best Masters in Data Science and Analytics - Europe Edition; Another Day in the Life of a Data Scientist; TensorFlow for Short-Term Stocks; Creating Simple Data Visualizations as an Act of Kindness
- Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018 - Dec 12, 2017.
The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.
- Top Data Science and Machine Learning Methods Used in 2017 - Dec 11, 2017.
The most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests; Deep Learning is used by only 20% of respondents; we also analyze which methods are most "industrial" and most "academic".
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- Robust Algorithms for Machine Learning - Dec 11, 2017.
This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.
- Unlock Machine Learning for the New Speed and Scale of Business - Dec 8, 2017.
Learn how Vertica in-database machine learning supports the entire predictive analytics process with, with MPP, SQL execution, R, Python, Java and more - get the whitepaper.
- 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.
- Exclusive: Interview with Rich Sutton, the Father of Reinforcement Learning - Dec 5, 2017.
My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.
- DataRobot: Moving from BI to Machine Learning with Automation - Dec 4, 2017.
Analytics industry expert Jen Underwood shares the fast path to developing world-class predictive modeling capabilities.
- Machine Learning with Optimus on Apache Spark - Nov 30, 2017.
The way most Machine Learning models work on Spark are not straightforward, and they need lots of feature engineering to work. That’s why we created the feature engineering section inside the Optimus Data Frame Transformer.
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- Natural Language Processing Library for Apache Spark – free to use - Nov 28, 2017.
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
- How To Unit Test Machine Learning Code - Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
- Key Takeaways from Open Data Science Conference (ODSC) West 2017 - Nov 21, 2017.
This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.
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- New Poll: Which Data Science / Machine Learning methods and tools you used? - Nov 20, 2017.
Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.
- Best Data Science, Machine Learning Courses from Udemy, only $10 until Nov 28- Black Friday/Cybermonday sale - Nov 17, 2017.
Black Friday/Cybermonday sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Nov 28, 2017.
- Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library - Nov 15, 2017.
Also: What is the difference between Bagging and Boosting?; Which #Python package manager should you use?; The Practical Importance of Feature Selection.
- You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
- The 10 Statistical Techniques Data Scientists Need to Master - Nov 15, 2017.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
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- Machine Learning Algorithms: Which One to Choose for Your Problem - Nov 14, 2017.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
- Oak Ridge National Laboratory: Postdoc, Imaging, Signals and Machine Learning - Nov 10, 2017.
The Imaging, Signals, and Machine Learning (ISML) group at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate with expertise in computer vision/image processing and data analytics.
- Top KDnuggets tweets, Nov 01-07: Airbnb develops an #AI which converts design into source code - Nov 8, 2017.
Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.
- Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe - Nov 8, 2017.
Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.
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- KDnuggets™ News 17:n43, Nov 8: Peak Demand for Data Scientists/Machine Learning Experts – When? Advice For New and Junior Data Scientists - Nov 8, 2017.
Also: 3 different types of machine learning; Want to know how Deep Learning works? Here's a quick guide to Deep Learning; Blockchain Key Terms, Explained.
- When Will Demand for Data Scientists/Machine Learning Experts Peak? - Nov 7, 2017.
We analyze the results of Data Science / Machine Learning peak demand poll, examine the split between optimists and pessimists, and try to explain why predictions look so similar regardless of experience, affiliation, and region?
- Interpreting Machine Learning Models: An Overview - Nov 7, 2017.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
- What is the difference between Bagging and Boosting? - Nov 6, 2017.
Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.
- 3 different types of machine learning - Nov 1, 2017.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
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- Getting Started with Machine Learning in One Hour! - Nov 1, 2017.
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.
- 6 Books Every Data Scientist Should Keep Nearby - Oct 31, 2017.
The best way to stay in touch is to continue brushing up on your knowledge while also maintaining experience. It’s the perfect storm or combination of skills to help you succeed in the industry.
- Top 6 errors novice machine learning engineers make - Oct 30, 2017.
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
- XGBoost: A Concise Technical Overview - Oct 27, 2017.
Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.