- 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.
- Best Data Science, Machine Learning Courses from Udemy (only $12 until Oct 31) - Oct 27, 2017.
Fall sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $12 until Oct 31, 2017.
- Density Based Spatial Clustering of Applications with Noise (DBSCAN) - Oct 26, 2017.
DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.
- KDnuggets™ News 17:n41, Oct 25: Learning git not enough to become data scientist; Peak Data Scientist Demand? Top Machine Learning w. R videos - Oct 25, 2017.
Becoming a data scientist after a science PhD; New Poll: When will demand for Data Scientists/Machine Learning experts peak? It Only Takes One Line of Code to Run Regression.
- Top 10 Machine Learning with R Videos - Oct 24, 2017.
A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.
- How Can Machine Learning Affect Your Organizational Data Strategy? - Oct 24, 2017.
The rise of high information advances, for example, Big Data, Machine Learning (ML), and the Internet of Things (IoT) in the Data Management scene has now started another enthusiasm for Data Governance.
- Rethinking 3 Laws of Machine Learning - Oct 23, 2017.
We rethink Asimov’s 3 law of robotics to help companies moving to unsupervised machine learning and realize 100% automated predictive information governance (PIG).
- Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
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- H2O World 2017: The best of data science, AI, and business transformation, Dec 4-5, Mountain View - Oct 18, 2017.
The flagship H2O World is back to bring together the best of data science, AI, Machine Learning, and business transformation. Spaces are limited, so get a spot at 50% off w. code KDNUGGETS by Oct 21, 2017.
- Random Forests®, Explained - Oct 17, 2017.
Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
- How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool - Oct 16, 2017.
In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.
- Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018 - Oct 12, 2017.
Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.
- Best practices of orchestrating Python and R code in ML projects - Oct 12, 2017.
Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.
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- Top KDnuggets tweets, Oct 04-10: Using #MachineLearning to Predict, Explain Attrition; Tidyverse, an opinionated #DataScience Toolbox in R - Oct 11, 2017.
Also #MachineLearning: Understanding Decision Tree Learning; #PyTorch tutorial distilled - Moving from #TensorFlow to PyTorch.
- Learn Generalized Linear Models (GLM) using R - Oct 11, 2017.
In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.
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- KDnuggets™ News 17:n39, Oct 11: Machine Learning to Predict, Explain Attrition; Deep Learning for Object Detection - Oct 11, 2017.
Also How to Choose a Data Science Job; Tidyverse, an opinionated Data Science Toolbox in R; A Quick Guide to Fake News Detection.
- A Quick Guide to Fake News Detection on Social Media - Oct 10, 2017.
Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.
- IAPA National Conference on “Advancing Analytics,” October 18, Melbourne - Oct 9, 2017.
Only three weeks until the IAPA National Conference "Advancing Analytics", October 18, Melbourne - don't miss this one-day to get up-to-date, meet with peers and hear from global leaders. KDNuggets readers receive a further 10% off full priced tickets, simply use the code ‘AAKDNUGGETS10’ at checkout.
- Using Machine Learning to Predict and Explain Employee Attrition - Oct 4, 2017.
Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.
- Neural Networks: Innumerable Architectures, One Fundamental Idea - Oct 4, 2017.
At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
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- U. of Miami: Assistant Professor, with emphasis on Data Science and Machine Learning - Oct 4, 2017.
Candidates must possess a Ph.D. in Computer Science or a closely related discipline. The position requires teaching and research expertise in Machine Learning, with emphasis on applications in Data Science.
- KDnuggets™ News 17:n38, Oct 4: What Blockchains Mean to Big Data; Keras Deep Learning Cheat Sheet; Machine Learning in Finance - Oct 4, 2017.
Also: XGBoost, a Top Machine Learning Method on Kaggle, Explained; How to win Kaggle competition based on NLP task, if you are not an NLP expert; Fundamental Breakthrough in 2 Decade Old Algorithm Redefines Big Data Benchmarks
- XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
- Understanding Machine Learning Algorithms - Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
- Data Science, AI & Deep Learning Conference – 16 November 2017, London - Oct 2, 2017.
This conference brings together a range of expert practitioners to explore and discuss the new era of AI, Machine Learning and Deep Learning. Participants gain real insights on how to exploit these technological advances for themselves and their organisations in an increasingly ‘data-driven world’.
- Top 10 Videos on Machine Learning in Finance - Sep 29, 2017.
Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!
- Learn How to Make Machine Learning Work (webinars every Tue in October, Live or on-demand) - Sep 28, 2017.
To fully use machine learning, we first need to understand both the potential benefits and the techniques to create data-driven models. In this webinar series, we will show you how to easily and automatically apply complex algorithms to data in real world applications.
- IAPA National Conference Advancing Analytics, Melbourne, October 18 - Sep 27, 2017.
Only three weeks until the IAPA National Conference "Advancing Analytics", October 18, Melbourne - don't miss this one-day to get up-to-date, meet with peers and hear from global leaders.
- KDnuggets™ News 17:n37, Sep 27: Essential Data Science & Machine Learning Cheat Sheets; 5 Machine Learning Projects to Check Out Now! - Sep 27, 2017.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; 5 Machine Learning Projects You Can No Longer Overlook - Episode VI; Putting Machine Learning in Production; 5 Ways to Get Started with Reinforcement Learning; Ensemble Learning to Improve Machine Learning Results
- 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.
- Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated - Sep 26, 2017.
Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.
- Machine Learning Reveals 9 Elements of Deal-Closing Sales - Sep 26, 2017.
The data science team at Gong.io analyzed over 67,000 sales calls/demos to understand the patterns that close deals. Here is what we found.
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- 10 Things Everyone Should Know About Machine Learning - Sep 26, 2017.
The author often finds himself explaining machine learning to non-experts. These are 10 things that he believes everyone should know, which he offers as a public service announcement.
- The Search for the Fastest Keras Deep Learning Backend - Sep 26, 2017.
This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.
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- Visualizing High Dimensional Data In Augmented Reality - Sep 25, 2017.
When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.
- Putting Machine Learning in Production - Sep 22, 2017.
In machine learning, going from research to production environment requires a well designed architecture. This blog shows how to transfer a trained model to a prediction server.
- Ensemble Learning to Improve Machine Learning Results - Sep 22, 2017.
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
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- 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
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- Tensorflow Tutorial: Part 1 – Introduction - Sep 21, 2017.
Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.
- How to Get Ready for AI and Machine Learning - Sep 20, 2017.
AI and machine learning have hit the mainstream, spanning consumer devices to enterprise initiatives. Experts say change is coming fast - if you don't have a plan, are you behind the curve?
- 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
- 5 Machine Learning Projects You Can No Longer Overlook – Episode VI - Sep 20, 2017.
Deep learning, data preparation, data visualization, oh my! Check out the latest installation of '5 Machine Learning Projects You Can No Longer Overlook' for insight on... well, what machine learning projects you can no longer overlook.
- KDnuggets™ News 17:n36, Sep 20: Data Science and the Imposter Syndrome; How To Become a 10x Data Scientist - Sep 20, 2017.
Also: New-Age Machine Learning Algorithms in Retail Lending; Cartoon: What Else Can AI Guess From Your Face? Machine Learning Translation and the Google Translate Algorithm
- Cool Vendor status for CrowdFlower means SF best ice cream for you - Sep 19, 2017.
Schedule a time to see a demo of the CrowdFlower platform and see how we empower data scientists to train, test, and tune machine learning for a human world. We’ll hook you up with some of San Francisco's best ice cream!
- Using Apache SystemML(tm) with Hortonworks Data Platform - Sep 18, 2017.
Learn how to add Apache SystemML to an existing Hortonworks Data Platform (HDP) 2.6.1 cluster for Apache Spark. Users interested in Python, Scala, Spark, or Zeppelin can run Apache SystemML as described here.
- Best Data Science, Machine Learning Courses from Udemy (only $12 until Sep 20) - Sep 14, 2017.
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $12 until Sep 20, 2017.
- Machine Learning Translation and the Google Translate Algorithm - Sep 14, 2017.
Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.
- New-Age Machine Learning Algorithms in Retail Lending - Sep 13, 2017.
We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.
- KDnuggets™ News 17:n35, Sep 13: Putting the “Science” Back in Data Science; Python vs. R: And the leader is… - Sep 13, 2017.
Putting the "Science" Back in Data Science; Python vs R - Who Is Really Ahead in Data Science, Machine Learning; I built a chatbot in 2 hours and this is what I learned; Are Data Lakes Fake News?; Python Overtaking R?
- K-Nearest Neighbors – the Laziest Machine Learning Technique - Sep 12, 2017.
K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.
- Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
- Top 10 Machine Learning Use Cases: Part 2 - Sep 11, 2017.
This post is the second in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.
- Python vs R for Artificial Intelligence, Machine Learning, and Data Science - Sep 11, 2017.
This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
- Top KDnuggets tweets, Aug 30 – Sep 5: Python overtakes R, becomes the leader in #DataScience; Humble Book Bundle: #DataScience - Sep 6, 2017.
Also: Pandas tips and tricks #Python #DataScience; How I replicated an $86 million project in 57 lines of code; Future #MachineLearning Class.
- Putting the “Science” Back in Data Science - Sep 6, 2017.
The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.
- KDnuggets™ News 17:n34, Sep 6: 277 Data Science Key Terms, Explained; Top 10 Machine Learning Use Cases; Future Machine Learning Class - Sep 6, 2017.
Also Top 10 Machine Learning Use Cases; Search Millions of Documents for Thousands of Keywords in a flash; Cartoon: Future Machine Learning Class; Data Science: (not) the preferred nomenclature.