- 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.
- Learn from experts at Netflix, Facebook, Tesla, DeepMind & more - Sep 5, 2017.
January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!
- Visualizing Cross-validation Code - Sep 5, 2017.
Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.
- Cartoon: Future Machine Learning Class - Sep 2, 2017.
New KDnuggets Cartoon looks at an unusual but possible future Machine Learning Class.
- Top 10 Machine Learning Use Cases: Part 1 - Aug 31, 2017.
This post is the first 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.
- Learning Machine Learning… with Flashcards - Aug 31, 2017.
Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
- Are physicians worried about computers machine learning their jobs? - Aug 30, 2017.
We review JAMA article on “Unintended Consequences of Machine Learning in Medicine” and argue that a number of alarming opinions in this pieces are not supported by evidence.
- KDnuggets™ News 17:n33, Aug 30: Python Overtakes R in Machine Learning; Data Science in 42 Steps; Deep Learning not AI’s Future - Aug 30, 2017.
Also: KDnuggets part-time, paid internship in Data Science/Machine Learning Journalism; How To Write Better SQL Queries: The Definitive Guide; Understanding overfitting: an inaccurate meme in Machine Learning; How to Become a Data Scientist: The Definitive Guide
- Vital Statistics You Never Learned… Because They’re Never Taught - Aug 29, 2017.
Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.
- Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples - Aug 28, 2017.
In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.
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- 42 Steps to Mastering Data Science - Aug 25, 2017.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
- Understanding overfitting: an inaccurate meme in Machine Learning - Aug 23, 2017.
Applying cross-validation prevents overfitting is a popular meme, but is not actually true – it more of an urban legend. We examine what is true and how overfitting is different from overtraining.
- Machine Learning vs. Statistics: The Texas Death Match of Data Science - Aug 23, 2017.
Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.
- What is the most important step in a machine learning project? - Aug 18, 2017.
In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.
- Lessons Learned From Benchmarking Fast Machine Learning Algorithms - Aug 16, 2017.
Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.
- A Guide to Understanding AI Toolkits - Aug 16, 2017.
This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
- KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest - Aug 16, 2017.
Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines
- 4 Industries Being Transformed by Machine Learning and Robotics - Aug 15, 2017.
When used in combination with big data and machine learning, both AI and robotics can actively improve over time as they collect more information. You don’t have to look far to see how these technologies have revolutionized the world, and continue to do so.
- 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!
- Transforming from Autonomous to Smart: Reinforcement Learning Basics - Aug 11, 2017.
This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
- What Artificial Intelligence and Machine Learning Can Do—And What It Can’t - Aug 10, 2017.
I have seen situations where AI (or at least machine learning) had an incredible impact on a business—I also have seen situations where this was not the case. So, what was the difference?
- The Machine Learning Abstracts: Support Vector Machines - Aug 10, 2017.
While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).
- Top /r/MachineLearning Posts, July: Friendly Suggestions re: Coding Practices; Racist AI How-To Without Really Trying - Aug 10, 2017.
Why can't you guys comment your f*cking code?; Train Chrome's Trex character to play independently; How to make a racist AI without really trying; Is training a NN to mimic a closed-source library legal?; 37 Reasons why your NN is not working
- Top KDnuggets tweets, Aug 2-8: PyTorch: concise overview of the framework and its tensor implementation - Aug 9, 2017.
Also: What is the most important step in a #MachineLearning project? #MachineLearning Algorithms: a concise technical overview; McKinsey state of #MachineLearning and #AI.
- KDnuggets™ News 17:n30, Aug 9: Machine Learning Algorithms: Concise Overview; Train your Deep Learning model faster and sharper - Aug 9, 2017.
Also: A unified deep learning framework for time-series mobile sensing data processing; EDISON Data Science Framework Release 2; Data mining Airbnb.
- Strata Data Conference, the reunion of data brain trust – KDnuggets Offer - Aug 8, 2017.
Strata Data Conference, the annual reunion of data brain trust, is Sept 25-28 in New York. Early price ends Aug 11 - save more with code KDNU.
- Going deeper with recurrent networks: Sequence to Bag of Words Model - Aug 8, 2017.
Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.
- Best Data Science, Machine Learning Courses from Udemy (only $10 or $12 till Aug 10) - Aug 6, 2017.
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 or $12 until Aug 10, 2017.
- Machine Learning Algorithms: A Concise Technical Overview – Part 1 - Aug 4, 2017.
These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.
- Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
- The Machine Learning Abstracts: Decision Trees - Aug 3, 2017.
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
- Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python - Aug 2, 2017.
Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
- Train your Deep Learning model faster and sharper: Snapshot Ensembling — M models for the cost of 1 - Aug 2, 2017.
We explain a novel Snapshot Ensembling method for increasing accuracy of Deep Learning models while also reducing training time.
- KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail - Aug 2, 2017.
Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data
- How to squeeze the most from your training data - Jul 27, 2017.
In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.
- The Machine Learning Abstracts: Classification - Jul 27, 2017.
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
- Machine Learning and Misinformation - Jul 27, 2017.
The creative aspects of machine learning are overshadowed by visions of an autonomous future, but machine learning is a powerful tool for communication. Most machine learning in today’s products is related to understanding.
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- Machine Learning Exercises in Python: An Introductory Tutorial Series - Jul 26, 2017.
This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.
- ExxonMobil: Machine Learning Position - Jul 21, 2017.
Seeking a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section.
- AI and Deep Learning, Explained Simply - Jul 21, 2017.
AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
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- Deep Learning, AI Assistant Summits London feature DeepMind and much more, Sep 21-22 – KDnuggets Offer - Jul 20, 2017.
The Deep Learning Summit London and the AI Assistant Summit London will be continuing the RE•WORK Global Summit Series this September 21 & 22. Early Bird discount is ending on July 28th. Register now to guarantee a spot at the Summit and use the discount code KDNUGGETS to save 20% on all tickets.
- Hacking in silico protein engineering with Machine Learning and AI, explained - Jul 19, 2017.
Proteins are building blocks of all living matter. Although tremendous progress has been made, protein engineering remains laborious, expensive and truly complicated. Here is how Machine Learning can help.
- Road Lane Line Detection using Computer Vision models - Jul 19, 2017.
A tutorial on how to implement a computer vision data pipeline for road lane detection used by self-driving cars.
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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
- Are Most Machine Learning Experts Turning to Deep Learning? - Jul 18, 2017.
Read a short opinion on what the impact of machine learning researchers focusing on deep learning will be.
- Optimizing Web sites: Advances thanks to Machine Learning - Jul 17, 2017.
Machine learning has revitalized a nearly dormant method, leading to a powerful approach for optimizing Web pages, finding the best of thousands of alternatives.
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- Machine Learning Applied to Big Data, Explained - Jul 17, 2017.
Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.
- 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.
- Stylight: Machine Learning Engineer - Jul 13, 2017.
We are looking for an experienced Machine Learning Engineer who wants to liberate us from manual classification. We use English as our main language.
- 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.
- Top KDnuggets tweets, Jul 05-11: 10 Free Must-Read Books for #MachineLearning and #DataScience; Why AI and Machine Learning? - Jul 12, 2017.
Also great overview: Unintuitive properties of #NeuralNetworks; #Apache #Flink vs #Spark: The Strange Loop in #DeepLearning - the coolest idea in #MachineLearning in 20 yrs;
- The Guerrilla Guide to Machine Learning with Julia - Jul 12, 2017.
This post is a lean look at learning machine learning with Julia. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
- Why Every Company Needs a Digital Brain - Jul 11, 2017.
As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.
- What Are Artificial Intelligence, Machine Learning, and Deep Learning? - Jul 10, 2017.
AI and Machine Learning have become mainstream, and people know shockingly little about it. Here is an explainer and useful references.
- 5 Free Resources for Getting Started with Self-driving Vehicles - Jul 10, 2017.
This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.
- Fidelity Investments: Vice President (AI Lead) – Artificial Intelligence, Machine Learning & Big Data - Jul 7, 2017.
Seeking an outstanding hands-on AI leader who can partner with business stakeholders and identify/prioritize top AI opportunities, create business/technical requirements, transform large volumes of data into AI-driven solutions using creative, and lead ML strategy and road map planning.
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3 - Jul 4, 2017.
In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)
- Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!? - Jul 3, 2017.
NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE
- Optimization in Machine Learning: Robust or global minimum? - Jun 30, 2017.
Here we discuss how convex problems are solved and optimised in machine learning/deep learning.
- Why Artificial Intelligence and Machine Learning? - Jun 30, 2017.
With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
- Interesting Things Learned as a Student of Machine Learning - Jun 29, 2017.
Did you ever learn something you didn't really want to? The path to machine learning mastery is paved with such collateral knowledge. Here are a few examples of such information I have gleaned while trekking away.
- KDnuggets™ News 17:n25, Jun 28: Emerging Data Science Software Ecosystem; 3 Key Data Science 2017 Hiring Trends - Jun 28, 2017.
Emerging Data Science Software Ecosystem; 3 Key Trends Shaping the 2017 Data Science Hiring Market; Top 10 Quora Machine Learning Writers and Their Best Advice; The world's first protein database for Machine Learning and AI; Making Sense of Machine Learning
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 2 - Jun 27, 2017.
In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.
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- Top 10 Quora Machine Learning Writers and Their Best Advice, Updated - Jun 26, 2017.
Gain some insight on a variety of topics with select answers from Quora's current top machine learning writers. Advice on research, interviews, hot topics in the field, how to best progress in your learning, and more are all covered herein.
- The world’s first protein database for Machine Learning and AI - Jun 22, 2017.
dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp
- Top KDnuggets tweets, Jun 14-20: 5 EBooks to Read Before Getting into A Data Science or Big Data Career - Jun 21, 2017.
Also 10 Free Must-Read Books for #MachineLearning and #DataScience; #Keras implementation of a simple Neural Net module for relational reasoning; Applying #deeplearning to real-world problems
- Making Sense of Machine Learning - Jun 21, 2017.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
- Does Machine Learning Have a Future Role in Cyber Security? - Jun 20, 2017.
In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.
- Best Data Science Courses from Udemy (only $10 till June 21) - Jun 19, 2017.
Here are some of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until June 21, 2017.
- The Machine Learning Algorithms Used in Self-Driving Cars - Jun 19, 2017.
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
- Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
- Top KDnuggets tweets, Jun 07-13: Is Regression Analysis Really Machine Learning? - Jun 14, 2017.
Machine Learning in Real Life: Tales from the Trenches; Is Regression Analysis Really Machine Learning?; Implementing Your Own k-Nearest Neighbour Algorithm Using Python; Building Simple Neural Networks - TensorFlow for Hackers.
- Open Innovation and Crowdsourcing in Machine Learning – Getting premium value out of data - Jun 14, 2017.
Recently, PSL Research University launched a one-week course combining theoretical lectures and practical sessions. 115 students from various backgrounds and skill levels were enrolled; something quite spectacular happened during the week: Students have achieved an astounding level of score improvement - in just three afternoons.
- KDnuggets™ News 17:n23, Jun 14: The Practice of Machine Learning, Data Science Implementation, and Feature Selection - Jun 14, 2017.
A Practical Guide to Machine Learning; Your Checklist to Get Data Science Implemented in Production; The Practical Importance of Feature Selection; Machine Learning in Real Life: Tales from the Trenches.
- 7 Ways to Get High-Quality Labeled Training Data at Low Cost - Jun 13, 2017.
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
- Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.
Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
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