Tag: Machine Learning (1242)

• 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.

  Pages: 1 2

  Tags: Data Mining, Data Science, Deep Learning, Machine Learning, Natural Language Processing, Python, Visualization

• The Practical Importance of Feature Selection - Jun 12, 2017.
  
  Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.

  Tags: Feature Selection, Machine Learning, Rubens Zimbres

• Autonomous Vehicles Need Superhuman Perception for Success - Jun 12, 2017.
  
  Michael Milford, Associate Professor at Queensland University of Technology (QUT), is a leading robotics researcher working to improve perception and more in autonomous vehicles, conducting his research at the intersection of robotics, neuroscience and computer vision.

  Tags: Machine Learning, RE.WORK, Self-Driving Car

• Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN - Jun 9, 2017.
  
  Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?

  Tags: Deep Learning, Google, Machine Learning, Neural Networks, Reddit

• A Practical Guide to Machine Learning: Understand, Differentiate, and Apply - Jun 9, 2017.
  
  So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.

  Tags: IBM, Machine Learning, Production

• New Speakers Announced for the European Machine Intelligence Summit & Machine Intelligence in Autonomous Vehicles Summit in Amsterdam, 28-29 June - Jun 8, 2017.
  
  Explore the cutting-edge technology leading the way in Machine Intelligence and Autonomous Vehicles and it’s applications in industry at the Amsterdam Summits on June 28th & 29th. Use the discount code KDNUGGETS to save 20% on all tickets.

  Tags: AI, Amsterdam, Machine Intelligence, Machine Learning, Netherlands, RE.WORK, Self-Driving Car

• The Unintended Consequences of Machine Learning - Jun 8, 2017.
  
  But with great power comes great responsibility. Let me tell you a story about the unintended consequences of well-meaning machine learning research.

  Tags: AI, Amazon, Machine Learning, Personalization

• How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I - Jun 8, 2017.
  
  As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!

  Tags: Apache Spark, Feature Engineering, Jupyter, Kaggle, Machine Learning, Python

• Machine Learning in Real Life: Tales from the Trenches to the Cloud – Part 1 - Jun 8, 2017.
  
  We live in a world where everyone knows enough about the Buzzwords “Deep Learning” and “Big Data”... we also live in a world where if you’re a developer you can, while knowing nothing about machine learning, go from zero to training a OCR model in the space of an hour.

  Tags: Cloud, Machine Learning, Production

• Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering - Jun 7, 2017.
  
  The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.

  Tags: Clustering, K-means, Machine Learning, Python, Workflow

• KDnuggets™ News 17:n22, Jun 7: 7 Steps to Mastering Data Preparation with Python; Why Does Deep Learning Not Have a Local Minimum? - Jun 7, 2017.
  
  7 Steps to Mastering Data Preparation with Python; Why Does Deep Learning Not Have a Local Minimum?; 7 Techniques to Handle Imbalanced Data; Which Machine Learning Algorithm Should I Use?; Is Regression Analysis Really Machine Learning?

  Tags: Data Preparation, Deep Learning, Machine Learning, Python, Regression, Unbalanced

• DataRobot Webinar on June 27, 2017: Automated Machine Learning in Action - Jun 6, 2017.
  
  In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.

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

• TPOT Automated Machine Learning Competition: Can AutoML beat humans on Kaggle? - Jun 5, 2017.
  
  Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.

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

• Is Regression Analysis Really Machine Learning? - Jun 5, 2017.
  
  What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?

  Tags: Applied Statistics, Linear Regression, Machine Learning, Regression, Statistics

• 7 Steps to Mastering Data Preparation with Python - Jun 2, 2017.
  
  Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  Pages: 1 2

  Tags: 7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python

• Which Machine Learning Algorithm Should I Use? - Jun 1, 2017.
  
  A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.

  Tags: Algorithms, Cheat Sheet, Machine Learning, Reinforcement Learning, Supervised Learning, Unsupervised Learning

• Data Science for Newbies: An Introductory Tutorial Series for Software Engineers - May 31, 2017.
  
  This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.

  Tags: Apache Spark, Data Science, Jupyter, Machine Learning, Pandas, Python, Reddit, Scala, SQL

• KDnuggets™ News 17:n21, May 31: Python Machine Learning Workflows from Scratch; Machine Learning Crash Course - May 31, 2017.
  
  Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Machine Learning Crash Course: Part 1; An Introduction to the MXNet Python API; How A Data Scientist Can Improve Productivity; Data science platforms are on the rise and IBM is leading the way

  Tags: Data Preparation, Data Science, Data Science Platform, Data Scientist, Machine Learning, Python

• Challenges in Machine Learning for Trust - May 29, 2017.
  
  With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.

  Tags: Machine Learning, Risk Modeling, Trust

• Machine Learning Workflows in Python from Scratch Part 1: Data Preparation - May 29, 2017.
  
  This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation.

  Tags: Data Preparation, Machine Learning, Python, Workflow

• Machine Learning Anomaly Detection: The Ultimate Design Guide - May 25, 2017.
  
  Considering building a machine learning anomaly detection system for your high velocity business? Learn how with Anodot ultimate three-part guide.

  Tags: Anodot, Anomaly Detection, Machine Learning, Real-time

• ExxonMobil: Machine Learning - May 24, 2017.
  
  Seeking a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section.

  Tags: Clinton, ExxonMobil, Machine Learning, NJ

• Machine Learning Crash Course: Part 1 - May 24, 2017.
  
  This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.

  Tags: Classification, Cost Function, Gradient Descent, Machine Learning, Regression

• The Path To Learning Artificial Intelligence - May 19, 2017.
  
  Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.

  Tags: AI, Artificial Intelligence, Deep Learning, Machine Learning, Online Education, Python

• Webinar: A New Era of Data Science – Unlocking Big Data Insights with Machine Learning and Spark, May 31 - May 19, 2017.
  
  Learn about Big Data technologies and trends, Democratizing Big Data analytics, Big Data and the Cloud, and more in this webcast with top experts Dean Abbott and Mamdouh Refaat.

  Tags: Angoss, Apache Spark, Dean Abbott, Machine Learning

• Data Preparation Strategies for Successful Machine Learning - May 18, 2017.
  
  This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.

  Tags: Data Preparation, DataRobot, Machine Learning, Strategy

• Top KDnuggets tweets, May 10-16: Which Machine Learning algorithm should I use? #cheatsheet - May 17, 2017.
  
  Also HDFS vs. HBase: All you need to know #BigData mini-tutorial; #MachineLearning overtaking #BigData?

  Tags: Algorithms, Machine Learning, Top tweets

• Best Data Science Courses from Udemy (only $10 till May 27) - May 17, 2017.
  
  Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until May 27, 2017.

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

• KDnuggets™ News 17:n19, May 17: Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can’t Overlook - May 17, 2017.
  
  The Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can No Longer Overlook, May; The Two Phases of Gradient Descent in Deep Learning; HDFS vs. HBase: All you need to know; Must-Know: What are common data quality issues for Big Data and how to handle them?

  Tags: Deep Learning, Gradient Descent, HBase, HDFS, Machine Learning, R

• Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn - May 12, 2017.
  
  We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.

  Tags: Dask, Distributed, Distributed Systems, Machine Learning, Optimization, scikit-learn

• Data Version Control: iterative machine learning - May 11, 2017.
  
  ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.

  Tags: CRISP-DM, DVC, GitHub, Machine Learning, Open Source, Reproducibility

• The Guerrilla Guide to Machine Learning with R - May 11, 2017.
  
  This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.

  Tags: Data Analysis, Machine Learning, R

• Top 10 Recent AI videos on YouTube - May 10, 2017.
  
  Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.

  Tags: AI, Google, Machine Learning, MIT, Neural Networks, NVIDIA, Robots, Youtube

• 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
  
  In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.

  Tags: Automated Machine Learning, Data Exploration, Deep Learning, Distributed Systems, Machine Learning, Overlook, Pandas, Reinforcement Learning

• KDnuggets™ News 17:n18, May 10: KDnuggets Poll: Software used for Analytics, Data Science? Top Machine Learning videos - May 10, 2017.
  
  Also Top 10 Machine Learning Videos on YouTube, updated; Deep Learning in Minutes with this ...; Machine Learning overtaking Big Data?

  Tags: Big Data, Machine Learning, Poll, Youtube

• MLTrain: transitioning academic theory to practice - May 9, 2017.
  
  Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.

  Tags: CNTK, Deep Learning, Johannesburg, Machine Learning, MLTrain, New York City, TensorFlow, Training

• Sales forecasting using Machine Learning - May 8, 2017.
  
  SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.

  Tags: Forecasting, Machine Learning, Sales, SpringML

• Data Science & Machine Learning Platforms for the Enterprise - May 8, 2017.
  
  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  Tags: Algorithmia, Data Science Platform, Enterprise, Machine Learning

• Deep Learning in Minutes with this Pre-configured Python VM Image - May 5, 2017.
  
  Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.

  Tags: Deep Learning, Machine Learning, Python

• Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow - May 5, 2017.
  
  Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI

  Tags: AI, Distill, Generative Adversarial Network, Go, Gradient Descent, Machine Learning, Reddit, Self-Driving Car, TensorFlow

• Machine Learning overtaking Big Data? - May 4, 2017.
  
  Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.

  Tags: Big Data, Big Data Hype, Gartner, Google Trends, Machine Learning

• Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
  
  The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

  Tags: Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Machine Learning, Neural Networks, Robots, Video Games, Yaser Abu-Mostafa, Youtube

• KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified - May 3, 2017.
  
  How to Learn Machine Learning in 10 Days; Keep it simple! How to understand Gradient Descent algorithm; The Guerrilla Guide to Machine Learning with Python; What Data You Analyzed - KDnuggets Poll Results and Trends; Cartoon: Machine Learning - What They Think I Do

  Tags: Gradient Descent, Machine Learning, Neural Networks, Poll, Python, TensorFlow

• How to Learn Machine Learning in 10 Days - May 1, 2017.
  
  10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.

  Tags: Machine Learning, Sebastian Raschka

• The Guerrilla Guide to Machine Learning with Python - May 1, 2017.
  
  Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.

  Tags: Deep Learning, Machine Learning, Pandas, Python, scikit-learn, Sebastian Raschka

• Cartoon: Machine Learning – What They Think I Do - Apr 29, 2017.
  
  Different views of Machine Learning: What society, my friends, my parents, other programmers think I do, and what I really do.

  Tags: Cartoon, Machine Learning

• Resource-aware Machine Learning – International Summer School, Sep 25-28, TU Dortmund - Apr 27, 2017.
  
  How to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? Learn at TU Dortmund International Summer School. Apply by July 15.

  Tags: Dortmund, Germany, Machine Learning, Resource-aware, Summer School

• Top KDnuggets tweets, Apr 19-25: 10 Free Must-Read Books for Machine Learning and Data Science - Apr 26, 2017.
  
  Also Practical #DeepLearning For Coders-18 hours of free lessons; Different views of #Machinelearning #cartoon #humor; Scikit-learn #MachineLearning classification algorithms.

  Tags: Deep Learning, Free ebook, Machine Learning, scikit-learn, Top tweets

• AI & Machine Learning Black Boxes: The Need for Transparency and Accountability - Apr 25, 2017.
  
  When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.

  Tags: AI, Machine Learning, Transparency

• Best Data Science Courses from Udemy (only $10 till Apr 29) - Apr 24, 2017.
  
  Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until April 29, 2017.

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

• Data Science for the Layman (No Math Added) - Apr 20, 2017.
  
  Written for the layman, this book is a practical yet gentle introduction to data science. Discover key concepts behind more than 10 classic algorithms, explained with real-world examples and intuitive visuals.

  Tags: Book, Data Science, Machine Learning, Tutorial

• KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook - Apr 19, 2017.
  
  Also Top mistakes data scientists make when dealing with business people; New Online Data Science Tracks for 2017; Cartoon: Why AI needs help with taxes.

  Tags: Data Science Platform, Forrester, Gartner, Machine Learning, Mistakes

• Sift Science: Sr. Software Engineer, Machine Learning - Apr 17, 2017.
  
  Seeking an experienced engineer who has built scalable machine learning data pipelines & systems, and feels equally comfortable running small experiments on their laptop using R as they do running Spark or Map Reduce jobs on remote clusters.

  Tags: CA, Machine Learning, San Francisco, Seattle, Sift Science, Software Engineer, WA

• Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions - Apr 14, 2017.
  
  Who leads in Data Science, Machine Learning, and Predictive Analytics? We compare the latest Forrester and Gartner reports for this industry for 2017 Q1, identify gainers and losers, and strong leaders vs contenders.

  Tags: Data Science Platform, Forrester, Gartner, IBM, Knime, Machine Learning, Mike Gualtieri, Predictive Analytics, RapidMiner, SAS

• 5 Machine Learning Projects You Can No Longer Overlook, April - Apr 13, 2017.
  
  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. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.

  Tags: Data Exploration, Deep Learning, Java, Machine Learning, Neural Networks, Overlook, Python, Scala, scikit-learn, Topic Modeling

• Machine Learning Finds “Fake News” with 88% Accuracy - Apr 12, 2017.
  
  In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.

  Tags: Data Science, Fake News, Machine Learning, Naive Bayes, Politics, Text Analytics

• KDnuggets™ News 17:n14, Apr 12: Free Machine Learning & Data Science Books; Top Machine Learning & Deep Learning Papers - Apr 12, 2017.
  
  10 Free Must-Read Books for Machine Learning and Data Science; Top 20 Recent Research Papers on Machine Learning and Deep Learning; New Poll: What data types you analyzed?; The 42 V's of Big Data and Data Science; A Brief History of Artificial Intelligence

  Tags: Books, Data Science, Deep Learning, Free ebook, Machine Learning, Poll, Research

• Must-Know: How to evaluate a binary classifier - Apr 11, 2017.
  
  Binary classification is a basic concept which involves classifying the data into two groups. Read on for some additional insight and approaches.

  Tags: Classifier, Interview questions, Machine Learning

• 10 Free Must-Read Books for Machine Learning and Data Science - Apr 10, 2017.
  
  Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.

  Tags: Books, Data Science, ebook, Free ebook, Machine Learning

• ODSC East 2017: Accelerate your Deep Learning & Machine Learning Knowledge and Career Opportunities, Boston, May 3-5 - Apr 7, 2017.
  
  With change comes opportunity! The pace of data science innovation continues to quicken. New tools and techniques are constantly emerging especially around deep learning and machine learning. In 2017 many more companies are embarking on data science projects.

  Tags: Boston, Data Science, Deep Learning, MA, Machine Learning, ODSC

• Top 20 Recent Research Papers on Machine Learning and Deep Learning - Apr 6, 2017.
  
  Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".

  Tags: Deep Learning, Machine Learning, Research, Top list, Yoshua Bengio

• KDnuggets™ News 17:n13, Apr 5: What makes a great data scientist? Best R Packages for Machine Learning - Apr 5, 2017.
  
  Also Best R Packages for Machine Learning; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Learning; A Short Guide to Navigating the Jupyter Ecosystem.

  Tags: Data Science Skills, Jupyter, Machine Learning, Pandas, R

• Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!? - Apr 4, 2017.
  
  A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research

  Tags: Advice, Andrew Ng, Distill, Google, Kaggle, Machine Learning, Reddit, Salesforce

• Must-Know: Why it may be better to have fewer predictors in Machine Learning models? - Apr 4, 2017.
  
  There are a few reasons why it might be a better idea to have fewer predictor variables rather than having many of them. Read on to find out more.

  Tags: Feature Selection, Interview questions, Machine Learning, Modeling

• The Best R Packages for Machine Learning - Mar 30, 2017.
  
  There is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages.

  Tags: Data Incubator, Machine Learning, R, R Packages

• From Big Data Platforms to Platform-less Machine Learning - Mar 27, 2017.
  
  The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.

  Tags: Big Data Services, Machine Learning, MLaaS, SaaS

• What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
  
  Check this out: A topic wise collection of 100+ data science interview questions from top companies.

  Tags: Algorithms, Data Science, Google, Hadoop, Interview questions, Machine Learning, Microsoft, Statistics, Uber

• Email Spam Filtering: An Implementation with Python and Scikit-learn - Mar 17, 2017.
  
  This post is an overview of a spam filtering implementation using Python and Scikit-learn. The results of 2 classifiers are contrasted and compared: multinomial Naive Bayes and support vector machines.

  Tags: Machine Learning, Python, scikit-learn

• Applying Machine Learning To March Madness - Mar 16, 2017.
  
  March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.

  Tags: Basketball, Machine Learning, March Madness

• 50 Companies Leading The AI Revolution, Detailed - Mar 16, 2017.
  
  We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas.

  Tags: AI, Business Analytics, Cybersecurity, Data Science, Healthcare, IoT, Machine Learning

• Data Science Game, Machine learning competition for students - Mar 14, 2017.
  
  Improve your skills and have fun with other talented students from all around the world. Reg by April 9, online qualification ends May 31, and final phase in Paris, Fall 2017.

  Tags: Competition, Data Science, France, Machine Learning, Paris, Student Competition

• Best Data Science Courses from Udemy (only $19 till Mar 31) - Mar 10, 2017.
  
  Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $19 until March 31, 2017.

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

• The HPI Future SOC Lab offers researchers free access to a powerful Big Data infrastructure - Mar 10, 2017.
  
  The HPI Future SOC (Service-Oriented Computing) Lab is a cooperation of the Hasso Plattner Institute (HPI) and industrial partners, providing free access to a powerful Big Data & Computing infrastructure. It is now accepting project proposals for 2017.

  Tags: Cloud Computing, Data Incubator, In-Memory Computing, Machine Learning, NVIDIA, Research

• Free Online Books Explaining Big Data, Machine Learning, Blockchain and More - Mar 10, 2017.
  
  It has been a challenge to keep up-to-date with new concepts from NoSQL, to machine learning, to Internet of things and blockchain, but Little Bee Books is here with free solutions to helping you do so.

  Tags: Big Data, Blockchain, Books, Free ebook, IBM, Machine Learning

• KDnuggets™ News 17:n09, Mar 8: 7 More Steps to Mastering Machine Learning w. Python; Every Intro to Data Science Course, Ranked - Mar 8, 2017.
  
  Also The Data Science Project Playbook; Hadoop Is Falling - Why? Bokeh Cheat Sheet: Data Visualization in Python

  Tags: Bokeh, Data Science Education, Data Visualization, Hadoop, Machine Learning, Python

• Software Engineering vs Machine Learning Concepts - Mar 6, 2017.
  
  Not all core concepts from software engineering translate into the machine learning universe. Here are some differences I've noticed.

  Tags: Machine Learning, Software Engineering

• Top /r/MachineLearning Posts, February: Oxford Deep NLP Course; Data Visualization for Scikit-learn Results - Mar 6, 2017.
  
  Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release

  Tags: Data Visualization, Deep Learning, Google, Machine Learning, Natural Language Processing, NLP, Oxford, Reddit, scikit-learn, TensorFlow

• 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
  
  This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
   
   

  Pages: 1 2

  Tags: 7 Steps, Classification, Clustering, Deep Learning, Ensemble Methods, Gradient Boosting, Machine Learning, Python, scikit-learn, Sebastian Raschka

• Cooperative Trust Among Neural Networks Drives Deeper Learning - Feb 28, 2017.
  
  Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.

  Tags: IBM, Machine Learning, Neural Networks, Trust

• What I Learned Implementing a Classifier from Scratch in Python - Feb 28, 2017.
  
  In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.

  Tags: Classification, Machine Learning, Perceptron, Python, Sebastian Raschka

• The 6 Best Data Science Courses from Udemy (only $10 till Feb 28) - Feb 25, 2017.
  
  Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Feb 28, 2017.

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

• Machine 4.0: Making your Factory, Production and Maintenance Data Work - Feb 24, 2017.
  
  To leverage the potential of Big Data the manufacturing firms should intelligently integrate and connect their data sources on a unified platform and use machine learning to extract insights, analyze them, and derive results.

  Tags: DataRPM, IoT, Machine Learning, Manufacturing

• Machine Learning-driven Firewall - Feb 23, 2017.
  
  Cyber Security is always a hot topic in IT industry and machine learning is making security systems more stronger. Here, a particular use case of machine learning in cyber security is explained in detail.

  Tags: Firewall, Fsecurify, GitHub, Machine Learning, Security

• The Gentlest Introduction to Tensorflow – Part 4 - Feb 22, 2017.
  
  This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.

  Tags: Beginners, Deep Learning, Machine Learning, Neural Networks, TensorFlow

• The Gentlest Introduction to Tensorflow – Part 3 - Feb 21, 2017.
  
  This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.

  Tags: Beginners, Deep Learning, Machine Learning, Neural Networks, Regression, TensorFlow

• Stacking Models for Improved Predictions - Feb 21, 2017.
  
  This post presents an example of regression model stacking, and proceeds by using XGBoost, Neural Networks, and Support Vector Regression to predict house prices.

  Tags: Ensemble Methods, Machine Learning, XGBoost

• AI & Machine Learning World, London, 13-15 June 2017 – KDnuggets Offer - Feb 21, 2017.
  
  AI & Machine Learning World, part of London Tech Week, brings together global thought leaders who have driven the adoption of machine learning within global enterprises. Use code TEC6245KD to save.

  Tags: AI, London, Machine Learning, UK

• Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory - Feb 16, 2017.
  
  Apache Parquet and Apache Arrow both focus on improving performance and efficiency of data analytics. These two projects optimize performance for on disk and in-memory processing

  Tags: Apache, Apache Arrow, Apache Spark, Data Science, Dremio, In-Memory Computing, Machine Learning, Python

• KDnuggets™ News 17:n06, Feb 15: So What is Big Data? 52 Useful Machine Learning APIs; Data Science finds Perfect Valentines Dates - Feb 15, 2017.
  
  Also Making Python Speak SQL with pandasql; 52 Useful Machine Learning & Prediction APIs, updated; New Poll: Do you support Trump Immigration Ban?

  Tags: API, Big Data, Clustering, Data Science Platform, Machine Learning, Python, SQL

• FeatureX: Software Engineer - Feb 10, 2017.
  
  Seeking a software engineer, responsible for the design and development of machine learning and computer vision platforms and data systems.

  Tags: Cambridge, Computer Vision, FeatureX, MA, Machine Learning, Software Engineer

• FeatureX: Machine Learning Research Scientist - Feb 10, 2017.
  
  As a machine learning research scientist, you will be developing machine learning techniques for a wide variety of data sources, ranging from financial time series data to features extracted from satellite imagery.

  Tags: Cambridge, FeatureX, MA, Machine Learning, Research Scientist

• 50+ Useful Machine Learning & Prediction APIs, updated - Feb 8, 2017.
  
  Very useful, updated list of 50+ APIs in machine learning, prediction, text analytics & classification, face recognition, language translation, and more.

  Tags: API, Data Science, Face Recognition, IBM Watson, Image Recognition, Machine Learning, NLP, Sentiment Analysis

• KDnuggets™ News 17:n05, Feb 8: Identifying Better Predictors; 5 Career Paths in Big Data, Data Science Explained - Feb 8, 2017.
  
  Identifying Variables That Might Be Better Predictors; 5 Career Paths in Big Data and Data Science, Explained; 5 Free Courses for Getting Started in Artificial Intelligence; 3 practical thoughts on why deep learning performs so well

  Tags: AI, Big Data, Career, Data Science, Deep Learning, Machine Learning, Predictive Analytics, R Packages

• Top /r/MachineLearning Posts, January: TensorFlow Updates; AlphaGo in the Wild; Self-Driving Mario Kart - Feb 7, 2017.
  
  TensorFlow 1.0.0-alpha; Unknown bot repeatedly beats top Go players online - so far it's undefeated; TensorKart: self-driving MarioKart with TensorFlow; GTA V integration into Universe is now open-source; Keras will be added to core TensorFlow at Google

  Tags: AlphaGo, Machine Learning, Reddit, Self-Driving Car, TensorFlow

• 5 Career Paths in Big Data and Data Science, Explained - Feb 6, 2017.
  
  Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.

  Tags: Big Data, Career, Data Analyst, Data Engineering, Data Infrastructure, Data Science, Explained, Machine Learning

• Top R Packages for Machine Learning - Feb 3, 2017.
  
  What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity.

  Tags: Machine Learning, R, R Packages

• Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
  
  What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!

  Tags: Gradient Descent, Machine Learning, NIPS, Optimization

• Top KDnuggets tweets, Jan 25-31: Python implementations of Andrew Ng #MachineLearning MOOC exercises - Feb 1, 2017.
  
  #Python implementations of Andrew Ng #MachineLearning MOOC exercises; This repository contains the entire #Python #DataScience Handbook; What are the best #visualizations of #MachineLearning algorithms? Learn #TensorFlow and #DeepLearning, without a PhD.

  Tags: Algorithms, Andrew Ng, Data Visualization, Machine Learning, Python, TensorFlow, Top tweets

• Is Deep Learning the Silver Bullet? - Feb 1, 2017.
  
  With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?

  Tags: AI, Deep Learning, Machine Learning

• KDnuggets™ News 17:n04, Feb 1: Data Science and Python Wrangling: Pandas Cheat Sheet; Great Collection of Machine Learning Algorithms - Feb 1, 2017.
  
  Also Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Bad Data + Good Models = Bad Results; Data Scientist - best job in America, again.

  Tags: AI, Algorithms, Chatbot, Cheat Sheet, Machine Learning, Pandas

• 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
  
  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  Tags: AI, Deep Neural Network, Generative Adversarial Network, Machine Learning, Reinforcement Learning

• Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
  
  Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.

  Tags: Algorithms, Machine Learning, Programming, Python

• KDnuggets™ News 17:n03, Jan 25: Automated Machine Learning Overview; Data Science Puzzle; Chatbots on Steroids - Jan 25, 2017.
  
  The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them

  Tags: Automated Machine Learning, Chatbot, CRISP-DM, Data Science, Machine Learning, Sales

• The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
  
  Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.

  Tags: Bias, Machine Learning, Model Performance, Predictive Analytics, Regularization, Statistics

• Chatbots on Steroids: 10 Key Machine Learning Capabilities to Fuel Your Chatbot - Jan 23, 2017.
  
  As chatbots become a common practice, the need for smarter bots arises. Empowering your bot with machine learning capabilities can really differentiate it from the rest. Check out these 10 capabilities to help fuel your chatbot.

  Tags: AI, Chatbot, Machine Learning, Sentiment Analysis

• The Data Science Puzzle, Revisited - Jan 20, 2017.
  
  The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.

  Tags: AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning

• Data Science of Sales Calls: 3 Actionable Findings - Jan 19, 2017.
  
  How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.

  Tags: AI, Gong.io, Machine Learning, Sales, Speech Recognition

• The Current State of Automated Machine Learning - Jan 18, 2017.
  
  What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.

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

• KDnuggets™ News 17:n02, Jan 18: Most Popular Language For Machine Learning; Analytics & Data Science Make Business Smarter - Jan 18, 2017.
  
  The Most Popular Language For Machine Learning and Data Science; Analytics & Data Science Make Business Smarter; Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science; 90 Active Blogs on Analytics, Big Data, Data Mining, and Data Science

  Tags: Analytics, Blogs, Data Science, Deep Learning, Machine Learning, Programming Languages

• More Data or Better Algorithms: The Sweet Spot - Jan 17, 2017.
  
  We examine the sweet spot for data-driven Machine Learning companies, where is not too easy and not too hard to collect the needed data.

  Tags: Algorithms, Big Data, Data, Datasets, Machine Learning

• Discover the new Modern Data Science Academy - Jan 17, 2017.
  
  The Modern Data Science Academy provides state-of-the-art workshops taught by San Diego Supercomputer Center experts. Coming workshops on Machine Learning (Feb 8-9) and NoSQL Databases (Mar 1-2).

  Tags: CA, Certificate, Data Science Education, Machine Learning, NoSQL, San Diego, UCSD

• 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
  
  Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Pages: 1 2

  Tags: Big Data, Blogs, Data Mining, Data Science, Machine Learning

• The Most Popular Language For Machine Learning and Data Science Is … - Jan 11, 2017.
  
  When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.

  Tags: Data Science, Machine Learning, Programming Languages, Python, R, Scala

• Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment - Jan 11, 2017.
  
  Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment; Huggable Image Classifier; xkcd: Linear Regression; AlphaGO WINS!; TensorFlow Fizzbuzz

  Tags: AlphaGo, Deep Learning, DeepMind, Google, Image Recognition, Machine Learning, Reddit, TensorFlow, Yann LeCun

• Deep Learning in Healthcare Summit in London, 28 February – 1 March (KDnuggets Offer) - Jan 11, 2017.
  
  Discover advances in deep learning tools and techniques from the world's leading innovators across industry, academia and the healthcare sector at the Deep Learning in Healthcare Summit in London, 28 February – 1 March. Use discount code KDNUGGETS to save 20%.

  Tags: AI, Deep Learning, Healthcare, London, Machine Learning, RE.WORK

• KDnuggets™ News 17:n01, Jan 11: 5 Machine Learning Projects You Can’t Overlook; Future of Deep Learning; Self-Driving Car Surprises - Jan 11, 2017.
  
  Also Game Theory Reveals the Future of Deep Learning; Generative Adversarial Networks - Hot Topic in ML; Cartoon: When Self-Driving Cars take you too far

  Tags: 2017 Predictions, Deep Learning, Machine Learning, Self-Driving Car

• AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017 - Jan 10, 2017.
  
  2017 is here. Check out an encore installation in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

  Tags: 2017 Predictions, AI, Data Science, Hilary Mason, Ian Goodfellow, Machine Learning, Michael O'Connell, Women

• Machine Learning Meets Humans – Insights from HUML 2016 - Jan 6, 2017.
  
  Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.

  Pages: 1 2

  Tags: AI, Bias, Decision Making, Humans, Machine Learning, Privacy, Safety, Transparency, Trust

• Top /r/MachineLearning Posts, December: OpenAI Universe; Deep Learning MOOC For Coders; Musk: Tesla Gets Awesome-er - Jan 5, 2017.
  
  OpenAI Universe; Deep Learning For Coders—18 hours of lessons for free; Elon Musk on Twitter: Tesla Autopilot vision neural net now working well; Apple to Start Publishing AI Research; Duolingo's "half-life regression" method for modeling human memory

  Tags: Apple, Deep Learning, Elon Musk, Machine Learning, OpenAI, Reddit, Regression, Tesla

• Top KDnuggets tweets, Dec 21 – Jan 03: R vs Python: A Comparison and Free Books to Learn; Popular Deep Learning Tools – a review - Jan 4, 2017.
  
  R vs Python: A Comparison and Free Books to Learn; The Five Capability Levels of Deep Learning - Yann Lecun view; The Future Of Machine Learning, McKinsey 2016 Analytics Study; #BigData: Main Developments in 2016 and Key Trends in 2017

  Tags: Deep Learning, IoT, Machine Learning, Python vs R, Top tweets

• How To Stay Competitive In Machine Learning Business - Jan 4, 2017.
  
  To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.

  Tags: Business, Business Analytics, Data Management, Machine Learning, Research

• Fundamentals of Machine Learning for Predictive Data Analytics, Dublin, 21-23 March, 2017 - Jan 4, 2017.
  
  Based on the trainers book, this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

  Tags: Applications, Dublin, Ireland, Machine Learning, Predictive Analytics, Training

• Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
  
  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  Tags: Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS

• 3 methods to deal with outliers - Jan 3, 2017.
  
  In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.

  Tags: Machine Learning, Outliers, Statistics

• Ten Myths About Machine Learning, by Pedro Domingos - Jan 3, 2017.
  
  Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.

  Tags: Machine Learning, Myths, Pedro Domingos

• Machine Learning and Cyber Security Resources - Jan 2, 2017.
  
  An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
   
   

  Tags: Cybersecurity, Machine Learning, Security

• 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
  
  There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
   
   

  Tags: Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn

• KDnuggets™ News 16:n46, Dec 28: 4 Reasons Your Machine Learning Model is Wrong; Deep Learning for coders MOOC - Dec 28, 2016.
  
  First Deep Learning for coders MOOC launched by Jeremy Howard; 4 Reasons Your Machine Learning Model is Wrong; 5 Capability Levels of Deep Learning Intelligence; Data Science Basics: Power Laws and Distributions.

  Tags: Deep Learning, Fake News, Jeremy Howard, Machine Learning, Process Mining

• Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever - Dec 21, 2016.
  
  Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.

  Tags: 2017 Predictions, Explanation, Machine Learning, Statistics, TensorFlow, Top tweets

• KDnuggets™ News 16:n45, Dec 21: 50+ Data Science, ML Cheat Sheets; Ethics of Self-Driving Cars? Top experts on Machine Learning Main Events, Key Trends - Dec 21, 2016.
  
  Also New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? Machine Learning, AI Main Events and Key Trends; 5 Basic Types of Data Science Interview Questions.

  Tags: 2017 Predictions, Bayesian, Cheat Sheet, Interview questions, Machine Learning