Tag: Machine Learning (1223)

• 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.

  Tags: Face Detection, Machine Learning, Optical Analytics, Video recognition

• 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.

  Tags: Algorithms, Data Science, Deep Learning, Machine Learning, Statistics

• 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.

  Tags: fast.ai, Feature Engineering, Machine Learning, Pandas, Python, Time Series

• 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.

  Tags: Advice, Business, Data Scientist, Machine Learning, ROI

• 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.

  Tags: Europe, Explanation, GDPR, Law, Machine Learning, Pedro Domingos, Privacy

• 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.

  Tags: Data Scientist, Machine Learning, R, Women

• 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.

  Tags: Architecture, fast.ai, Machine Learning, Validation, Workflow

• 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.

  Tags: Azure ML, Data Science, Machine Learning, Python, Udemy

• 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."

  Tags: Automated Machine Learning, DataRobot, Machine Learning, Modeling

• 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.

  Tags: Andrew Ng, Data Science, Data Scientist, Deep Learning, Machine Learning

• 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.

  Tags: Accuracy, CoLaboratory, Data Preparation, Ensemble Methods, Jupyter, Machine Learning, Validation

• 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.

  Tags: AI, Blockchain, CA, Data Science, Deep Learning, Industry, Machine Learning, Santa Clara

• 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.

  Tags: Data Science, Deep Learning, Machine Learning, Predictive Modeling, Time Series

• 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.

  Tags: Chicago, Hadoop, IL, Machine Learning, Python, R, TDWI, Training

• 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.

  Tags: Data Science, Data Warehouse, Machine Learning, Retail

• 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

  Tags: Deep Learning, Machine Learning, NLP, Python, Reinforcement Learning, Top tweets

• 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.

  Tags: Boston, Jupyter, MA, Machine Learning, O'Reilly, Software Engineering

• 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.

  Tags: Advanced Analytics, Hackathon, Machine Learning, McKinsey, Recommender Systems

• 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.

  Tags: AGI, AI, Deep Learning, History, Hype, Jobs, Machine Learning

• 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.

  Tags: Algorithms, Data Science Platform, Gartner, Machine Learning, Magic Quadrant

• 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.

  Tags: Alteryx, Anaconda, Angoss, Data Science Platform, Domino, Gartner, H2O, IBM, Knime, Machine Learning, Magic Quadrant, RapidMiner, SAS

• 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.

  Tags: DevOps, Machine Learning

• 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.

  Tags: Data Center, DeepMind, Electricity, Machine Learning

• 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.

  Tags: Data Science Platform, Domino, Gartner, Machine Learning, Magic Quadrant

• 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.

  Pages: 1 2

  Tags: Adversarial, AI, CA, Conference, D-Wave Systems, Deep Learning, Machine Learning, Neural Networks, Quantum Computing, San Francisco, Security

• 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.

  Tags: GitHub, Machine Learning, Open Source, Python, scikit-learn, TensorFlow

• 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

  Tags: Algorithms, BI, Data Analytics, Data Mining, Data Science, Data Science Education, Data Scientist, Google Trends, Jobs, Machine Learning

• 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.

  Tags: Algorithms, Classification, Logistic Regression, Machine Learning, Regression

• 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.

  Tags: AI, Big Data, History, Machine Learning, Neural Networks, Reinforcement Learning, Trends

• 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.

  Tags: Cartoon, Machine Learning, Robots, Valentine's Day

• 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)

  Tags: Algorithms, CoLaboratory, Data Science, Ensemble Methods, fast.ai, Feature Engineering, Machine Learning, Python, Scala

• 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.

  Tags: CA, Data Science, Datascience.com, Machine Learning, San Francisco

• 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.

  Tags: Aging, AI, Applications, Deep Learning, Machine Learning

• 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.

  Tags: Algorithms, Machine Learning, Regression, Trends

• 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.

  Pages: 1 2

  Tags: Decision Trees, Ensemble Methods, Machine Learning, Python, Random Forests, scikit-learn, XGBoost

• 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.

  Tags: Apache Spark, Data Analysis, Data Science, Data Visualization, Machine Learning, NLP, 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!

  Tags: Bayesian, Gradient Boosting, Keras, Machine Learning, Overlook, PHP, Python, scikit-learn

• 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.

  Tags: AI, CA, Developers, Machine Learning, San Francisco

• 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.

  Tags: Automated Machine Learning, Automation, Data Science, Feature Engineering, Machine Learning

• 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

  Tags: Architecture, Data Science, Machine Learning, Neural Networks, Overfitting, Python, Regularization, Web Scraping

• 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.

  Tags: CA, Data Science, Datascience.com, Machine Learning, San Francisco

• 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.

  Tags: Deep Learning, fast.ai, Gluon, Machine Learning, MOOC, MXNet, Python

• 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.

  Tags: Berkeley, CA, Machine Learning, Researcher, Software Developer, The Voleon Group

• 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.

  Tags: Biometrics, Machine Learning, Security, Trends

• 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.

  Tags: Machine Learning, Neural Networks, Python, PyTorch

• 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.

  Tags: Machine Learning, Overfitting, Regularization

• 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.

  Pages: 1 2

  Tags: Architecture, Deep Learning, Machine Learning, Neural Networks

• 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.

  Tags: API, Deep Learning, Machine Learning, ParallelDots, Text Classification

• 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.

  Pages: 1 2

  Tags: Machine Learning, Mathematics, Programming, Statsbot

• 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.

  Tags: AI, Chatbot, Insurance, Insurance Nexus, Machine Learning

• 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

  Tags: Andrew Ng, Bias, Distribution, Machine Learning, Variance

• 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

  Tags: Gradient Boosting, Machine Learning, MLaaS, Pipeline, TensorFlow, Workflow, XGBoost

• 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.

  Pages: 1 2

  Tags: Logistic Regression, Machine Learning, Metrics, MLlib

• 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.

  Tags: Backpropagation, Deep Learning, Gradient Descent, H2O, Machine Learning, 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.

  Pages: 1 2

  Tags: AI, Amazon, Azure ML, Cloud, Google, Google Cloud, Machine Learning, Microsoft, MLaaS, Sagemaker

• 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.

  Tags: Machine Learning, Python, Survey

• 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).

  Tags: Data Preparation, Image Recognition, Machine Learning, SVM

• 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.

  Pages: 1 2

  Tags: Bias, Machine Learning, Metrics, Training Data, Variance

• 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!

  Tags: Free ebook, LIONbook, Machine Learning, Optimization, Roberto Battiti

• 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.

  Tags: Machine Learning, Sports, Star Wars, word2vec

• 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.

  Tags: AI, Deep Learning, Dell, EMC, Machine Learning

• 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.

  Tags: AGI, AI, Anthony Goldbloom, Machine Learning, Nick Bostrom, TED, Zeynep Tufekci

• Regularization in Machine Learning - Jan 10, 2018.
  
  Regularization is a technique that helps to avoid overfitting and also make a predictive model more understandable.

  Tags: Machine Learning, Overfitting, Regularization

• 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?

  Tags: AI, Analytics, Apache Arrow, Blockchain, Data Visualization, Machine Learning, Quantum Computing, Udemy

• 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.

  Tags: AI, Automated Machine Learning, CUDA, H2O, Machine Learning

• 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.

  Tags: Cross-validation, Data Mining, Datasets, Machine Learning

• 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.

  Tags: AI, Analytics, Beginners, Big Data, Machine Learning, 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.

  Tags: Azure ML, Data Science, Machine Learning, Python, Udemy

• 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.

  Pages: 1 2

  Tags: Algorithms, Machine Learning, Quantum Computing

• 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.

  Tags: AI, Airlines, Bias, Consumer Analytics, Machine Learning, Personalization

• 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.

  Pages: 1 2

  Tags: Andrew Ng, Coursera, Data Science, edX, Machine Learning, MOOC, Udemy

• 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.

  Tags: Algorithms, Andrew Ng, Bias, Machine Learning

• 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.

  Tags: Data Science, Engineer, Machine Learning, 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

  Tags: 2018 Predictions, AGI, AI, Data Science, Data Visualization, FiveThirtyEight, Machine Learning, Poll, Python

• $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.

  Tags: Machine Learning, Packt Publishing, Python, Sebastian Raschka

• 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.

  Pages: 1 2

  Tags: Machine Learning, Python, TensorFlow

• 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.

  Tags: Bias, Conference, Machine Learning, NIPS, Reinforcement Learning

• 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.

  Tags: 2018 Predictions, AI, Ajit Jaokar, Brandon Rohrer, Daniel Tunkelang, Hugo Larochelle, Machine Learning, Pedro Domingos, Sebastian Raschka, Xavier Amatriain

• 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.

  Tags: Apache Spark, Hadoop, Machine Learning, Online Education, Python, Tableau, Udemy

• 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.

  Tags: 2018 Predictions, AI, Machine Learning, Xavier Amatriain

• 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.

  Tags: Andrew Ng, Machine Learning

• 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

  Tags: 2018 Predictions, Data Science, Data Scientist, Keras, Machine Learning, Neural Networks, Poll

• 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.

  Tags: 2018 Predictions, Carla Gentry, Data Science, Eric Siegel, Jeff Ullman, Jen Underwood, Jill Dyche, Kirk D. Borne, Machine Learning, Predictions, Rexer Analytics, Tom Davenport, Trends

• 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".

  Pages: 1 2

  Tags: Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Random Forests, Regression, Uplift Modeling

• 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.

  Tags: ActiveState, Keras, Machine Learning, Python, Statistics

• 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.

  Tags: Big Data, Database, Machine Learning, MPP Database, SQL, Vertica, White Paper

• 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.

  Tags: Automated Data Science, Automated Machine Learning, DataRobot, Machine Learning

• 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.

  Tags: AGI, AI, AlphaGo, DeepMind, Machine Learning, Neuroscience, Reinforcement Learning, Rich Sutton, Yann LeCun

• 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.

  Tags: Automation, BI, Business Intelligence, DataRobot, Jen Underwood, Machine Learning, White Paper

• 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.

  Pages: 1 2

  Tags: Apache Spark, Data Science, Feature Engineering, Machine Learning, MLlib, Python, Workflow

• 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.

  Tags: Apache Spark, API, GitHub, John Snow Labs, Machine Learning, NLP

• 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.

  Tags: Machine Learning, Neural Networks, Python, Software Engineering, TensorFlow

• 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.

  Pages: 1 2

  Tags: AI, CA, Conference, Data Science, Deep Learning, DJ Patil, Machine Learning, Neural Networks, ODSC, San Francisco

• 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.

  Tags: Algorithms, Data Science Tools, Machine Learning, Poll

• 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.

  Tags: Apache Spark, Hadoop, Machine Learning, Online Education, Python, Tableau, Udemy

• 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.

  Tags: Capsule Networks, Kaggle, Machine Learning, NLP, Top tweets

• 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.

  Tags: Data Science, Linear Regression, Machine Learning, Statistics

• 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.

  Pages: 1 2

  Tags: Algorithms, Data Science, Data Scientist, Machine Learning, Statistical Learning, Statistics

• 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.

  Tags: Algorithms, Machine Learning, Reinforcement Learning, Statsbot, Supervised Learning, Unsupervised Learning

• 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.

  Tags: Image Recognition, Machine Learning, Oak Ridge, ORNL, Postdoc, TN

• 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.

  Tags: AI, AirBnB, Books, Machine Learning, Neural Networks, Python, Sebastian Raschka, Top tweets

• 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.

  Pages: 1 2

  Tags: Caffe, Machine Learning, Open Source, scikit-learn, TensorFlow, Theano, Torch

• 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.

  Tags: Advice, Blockchain, Data Scientist, Hiring, Machine Learning, Poll

• 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?

  Tags: Data Scientist, Hiring, Machine Learning, Poll, Trends

• 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.

  Tags: Interpretability, Machine Learning, Modeling, O'Reilly

• 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.

  Tags: Bagging, Boosting, Ensemble Methods, Machine Learning

• 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.

  Pages: 1 2

  Tags: Classification, Clustering, Machine Learning, Regression, Reinforcement Learning, Supervised Learning

• 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.

  Tags: Beginners, Machine Learning

• 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.

  Tags: Books, Data Science, Data Scientist, Machine Learning

• 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.

  Tags: Beginners, Machine Learning, Mistakes, Outliers, Regression, Regularization, Time Series

• 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.

  Tags: 7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks

• 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.

  Tags: Explained, Kaggle, Machine Learning, XGBoost

• 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.

  Tags: Apache Spark, Hadoop, Machine Learning, Online Education, Python, Tableau, Udemy

• 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.

  Tags: Algorithms, Clustering, Machine Learning

• 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.

  Tags: Apache Spark, Data Scientist, Machine Learning, R

• 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.

  Tags: Algorithms, Clustering, K-nearest neighbors, Machine Learning, PCA, R, Text Mining, Top 10, Youtube

• 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.

  Tags: Machine Learning, Strategy

• 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).

  Tags: AI, Humor, Machine Learning, Robots, Skynet

• 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.
   
   

  Pages: 1 2

  Tags: Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10

• 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.

  Tags: AI, Business, CA, H2O, Machine Learning, Mountain View

• Random Forests(r), 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.

  Tags: Algorithms, CART, Decision Trees, Ensemble Methods, Explained, Machine Learning, Random Forests, Salford Systems

• 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.

  Tags: Deepak Agarwal, LinkedIn, Machine Learning, Recommendation, Recommender Systems

• 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.

  Tags: Bootcamp, Data Science, Data Visualization, Machine Learning, NLP, Python, R, Switzerland, Zurich

• 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.

  Pages: 1 2

  Tags: DVC, Machine Learning, Python, Python vs R, R

• 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.

  Tags: Attrition, Decision Trees, Machine Learning, PyTorch, Tidyverse, Top tweets

• 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.

  Pages: 1 2

  Tags: Generalized Linear Models, Linear Regression, Logistic Regression, Machine Learning, R, Regression

• 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.

  Tags: AI, Attrition, Deep Learning, Machine Learning, Tidyverse

• 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.

  Tags: Fake News, Machine Learning, Social Media

• 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.

  Tags: Analytics, Australia, IAPA, Machine Learning, Melbourne

• 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.

  Tags: Churn, H2O, HR, Machine Learning, Predictive Analytics, Workforce Analytics