Machine Learning (2021)

• QCon.ai San Francisco: Applied AI Software Conference for Developers – KDnuggets Offer - Feb 8, 2019.
  
  QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.

  AI, CA, Developers, Machine Learning, San Francisco, Software

• 10 Trending Data Science Topics at ODSC East 2019 - Feb 7, 2019.
  
  ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.

  Apache Spark, Boston, Data Science, LSTM, Machine Learning, ODSC, Python

• Neural Networks – an Intuition - Feb 7, 2019.
  
  Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.

  Explained, History, Machine Learning, Neural Networks, Perceptron

• The Essential Data Science Venn Diagram - Feb 4, 2019.
  
  A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.

  Analytics, Data Science, Machine Learning, Statistics, Venn Diagram

• Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
  
  Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

  AI, Eric Siegel, Machine Learning, Safety

• KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning - Jan 30, 2019.
  
  Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups

  AI, Hype, Logistic Regression, Machine Learning, Modeling, Python, Skills, Workflow

• The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
  
  We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”

  Algorithms, Bias, Machine Learning

• 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
  
  With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!

  7 Steps, Classification, Clustering, Jupyter, Machine Learning, Python, Regression

• Machine Learning Security - Jan 25, 2019.
  
  We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.

  Adversarial, Alexa, Machine Learning, Security

• How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy - Jan 23, 2019.
  
  We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.

  Cross-validation, Forecasting, Machine Learning, Overfitting, Time Series

• Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
  
  Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.

  Logistic Regression, Machine Learning

• What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
  
  2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.

  2019 Predictions, AI, AlphaZero, BERT, Deep Learning, Machine Learning, NLP, Trends

• How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
  
  We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.

  Anomaly Detection, Deployment, Machine Learning, MapR, Monitoring, Real-time

• Automated Machine Learning in Python - Jan 18, 2019.
  
  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn

• Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
  
  Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.

  Machine Learning, Model Performance, P-value, Statistical Modeling, Statistical Significance

• The Hundred-Page Machine Learning Book - Jan 17, 2019.
  
  This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.

  Andriy Burkov, Book, Machine Learning, Peter Norvig

• Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples - Jan 17, 2019.
  
  We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.

  Cold Start, Data Scientist, Kirk D. Borne, Machine Learning

• How to build an API for a machine learning model in 5 minutes using Flask - Jan 17, 2019.
  
  Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.

  API, Flask, Machine Learning, Python

• KDnuggets™ News 19:n03, Jan 16: Top 10 Books on NLP and Text Analysis; End To End Guide For Machine Learning Projects - Jan 16, 2019.
  
  Also: Why Vegetarians Miss Fewer Flights - Five Bizarre Insights from Data; 4 Myths of Big Data and 4 Ways to Improve with Deep Data; The Role of the Data Engineer is Changing; How to solve 90% of NLP problems: a step-by-step guide

  Big Data, Data Engineer, Data Science, Insights, Machine Learning, Myths, NLP, Text Analysis

• The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
  
  Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.

  Automated Machine Learning, Facebook, fast.ai, Google, Keras, Machine Learning, Object Detection, Python, Reinforcement Learning, Word Embeddings

• Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
  
  Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  AI, Analytics, Big Data, Blogs, Data Mining, Data Science, Data Visualization, Machine Learning

• End To End Guide For Machine Learning Projects - Jan 14, 2019.
  
  Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

  Machine Learning, Workflow

• Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data - Jan 12, 2019.
  
  A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.

  Credit Risk, Eric Siegel, Healthcare, Machine Learning, Overfitting, Uber

• The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup - Jan 11, 2019.
  
  A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.

  2019 Predictions, AI, Hype, Interpretability, Machine Learning, Xavier Amatriain

• Top KDnuggets tweets, Jan 02-08: 10 Free Must-Read Books for Machine Learning and Data Science - Jan 9, 2019.
  
  Also: Papers with Code: A Fantastic GitHub Resource; Most Recommended #DataScience and #MachineLearning Books by Top MS programs;10 More Free Must-Read Books for ML and DS

  Books, Free ebook, Keras, Machine Learning

• [Webinar] Accelerating Machine Learning on Databricks - Jan 9, 2019.
  
  In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.

  Databricks, Deep Learning, Deployment, Machine Learning

• 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
  
  There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.

  Big Data, Data Lakes, Data Warehouse, Hype, Machine Learning, Sampling

• Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver - Jan 9, 2019.
  
  Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.

  AI, Data Science, Machine Learning, Product

• KDnuggets™ News 19:n02, Jan 9: The cold start problem: how to build your machine learning portfolio; 5 Best Data Visualization Libraries - Jan 9, 2019.
  
  Learn how to bootstrap your Machine Learning portfolio, which data visualization libraries to use, main approaches to ensemble learning, how to do text summarization, and check our special offers for leading analytics, AI, and Data Science events below.

  Data Visualization, Ensemble Methods, Machine Learning

• Math for Machine Learning - Jan 4, 2019.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  Book, ebook, Machine Learning, Mathematics, Richard Han

• The cold start problem: how to build your machine learning portfolio - Jan 4, 2019.
  
  This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.

  Beginners, Cold Start, Machine Learning

• What to do when your training and testing data come from different distributions - Jan 4, 2019.
  
  However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!

  Distribution, Machine Learning, Training Data

• Improve your AI and Machine Learning skills at AI NEXTCon in Seattle, Jan 23-27 - Jan 3, 2019.
  
  If you are a developer looking to hone your skills, a tech lead and manager to learn latest AI tech that apply to your engineering teams to innovate products and services, or someone who just wants to learn more about the AI industry that's re-shaping the tech world, the AI NEXTCon is right for you.

  AI, Machine Learning, Seattle, WA

• Ensemble Learning: 5 Main Approaches - Jan 3, 2019.
  
  We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.

  Bagging, Boosting, Ensemble Methods, Machine Learning

• KDnuggets™ News 19:n01, Jan 3: The Essence of Machine Learning; A Guide to Decision Trees for Machine Learning and Data Science - Jan 3, 2019.
  
  Also: 10 More Must-See Free Courses for Machine Learning and Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Feature engineering, Explained; Papers with Code: A Fantastic GitHub Resource for Machine Learning; BERT: State of the Art NLP Model, Explained

  Courses, Data Science, Decision Trees, Machine Learning

• 3 More Google Colab Environment Management Tips - Jan 2, 2019.
  
  This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.

  Google, Google Colab, Jupyter, Machine Learning, Python

• Papers with Code: A Fantastic GitHub Resource for Machine Learning - Dec 31, 2018.
  
  Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.

  GitHub, Machine Learning, Research

• Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
  
  An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?

  Decision Trees, Deep Learning, Linear Regression, Logistic Regression, Machine Learning, Neural Networks, SVM

• The Essence of Machine Learning - Dec 28, 2018.
  
  And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.

  Aaron Courville, Classification, Ian Goodfellow, Machine Learning, Tom Mitchell, Yoshua Bengio

• A Case For Explainable AI & Machine Learning - Dec 27, 2018.
  
  In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.

  Bias, Explainable AI, Explanation, Interpretability, Machine Learning

• Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
  
  A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.

  Pages: 1 2

  Classification, Clustering, Datasets, Machine Learning, Python, Synthetic Data

• A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
  
  What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

  Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn

• Six Steps to Master Machine Learning with Data Preparation - Dec 21, 2018.
  
  To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.

  Data Preparation, Machine Learning

• Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI - Dec 20, 2018.
  
  We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.

  AI, Explainable AI, Explanation, Interpretability, Machine Learning

• 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
  
  Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.

  AI, Algorithms, Big Data, Data Science, Deep Learning, Machine Learning, MIT, NLP, Reinforcement Learning, U. of Washington, UC Berkeley, Yandex

• Improve ML transparency without sacrificing accuracy - Dec 19, 2018.
  
  O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!

  Dataiku, ebook, Machine Learning, O'Reilly

• Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
  
  Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.

  Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow

• KDnuggets™ News 18:n48, Dec 19: Why You Shouldn’t be a Data Science Generalist; Industry Data Science & Machine Learning 2019 Predictions - Dec 19, 2018.
  
  Also: Top Stories of 2018; NLP Breakthrough Imagenet Moment has arrived; Four Approaches to Explaining AI and Machine Learning; Solve any Image Classification Problem Quickly and Easily

  2019 Predictions, AI, Analytics, Career Advice, Data Science, Machine Learning, NLP

• Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019 - Dec 18, 2018.
  
  This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!

  2019 Predictions, AI, Analytics, Data Science, Domino, dotData, Figure Eight, Industry, Knime, Machine Learning, MapR, MathWorks, OpenText, ParallelM, Salesforce, Splice Machine, Splunk

• 2018 Year-in-Review: Machine Learning Open Source Projects & Frameworks - Dec 17, 2018.
  
  This post is a look at the top open source projects and major developments in machine learning frameworks over the past 12 months.

  Machine Learning, Open Source, Review

• Four Real-Life Machine Learning Use Cases - Dec 13, 2018.
  
  This ebook will walk you through four use cases for Machine Learning on Databricks, covering loan risk, advertising analytics and predictive use case, market basket analysis, suspicious behaviour identification in video use, and more.

  Databricks, ebook, Machine Learning, Use Cases

• Four Approaches to Explaining AI and Machine Learning - Dec 12, 2018.
  
  We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).

  AI, Explainable AI, Interpretability, LIME, Machine Learning

• KDnuggets™ News 18:n47, Dec 12: Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors - Dec 12, 2018.
  
  Common mistakes when carrying out machine learning and data science; Most popular Python IDEs/Editors; Machine Learning / AI Main Developments in 2018 and Key Trends for 2019; Machine Learning Project checklist.

  Data Science, Machine Learning, Mistakes, Python, Trends

• A Machine Learning Deep Dive [Webinar, Dec 13] - Dec 11, 2018.
  
  Learn how ShopRunner uses Databricks on AWS and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.

  AWS, Databricks, Deployment, Machine Learning, Personalization

• Machine Learning & AI Main Developments in 2018 and Key Trends for 2019 - Dec 11, 2018.
  
  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 2018 and their 2019 key trend predictions.

  2019 Predictions, AI, Ajit Jaokar, Andriy Burkov, Anima Anandkumar, Brandon Rohrer, Daniel Tunkelang, Machine Learning, Pedro Domingos, Rachel Thomas, Zachary Lipton

• P&G: Data Scientist – Machine Learning/NLP [Cincinnati, OH] - Dec 11, 2018.
  
  P&G is seeking a Data Scientist - Machine Learning/NLP in Cincinnati, OH. In this role you will have multiple projects on which you will leverage machine learning tools to solve these types of problems.

  Cincinnati, Data Scientist, Machine Learning, NLP, OH, Procter and Gamble

• Learning Machine Learning vs Learning Data Science - Dec 11, 2018.
  
  We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.

  Career, Data Science, Education, Machine Learning

• Math for Machine Learning - Dec 10, 2018.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  Book, ebook, Machine Learning, Mathematics, Richard Han

• Should you become a data scientist? - Dec 10, 2018.
  
  An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.

  Career, Data Science, Data Scientist, History, Machine Learning, Tips, Trends

• How Different are Conventional Programming and Machine Learning? - Dec 10, 2018.
  
  When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.

  Machine Learning, Programming

• Dr. Data Show Video: Five Reasons Computers Predict When You’ll Die - Dec 8, 2018.
  
  Death prediction is a very morbid topic indeed — but a very practical one. So how does it work? What technological sorcery is at play? And how accurate is it? And, gee whiz, what's it used for?

  Data Science, Eric Siegel, Machine Learning, Predictive Analytics, Predictive Analytics World

• XGBoost on GPUs: Unlocking Machine Learning Performance and Productivity - Dec 7, 2018.
  
  On Dec 18, 11:00 AM PT, join NVIDIA for a technical deep dive into GPU-accelerated machine learning, to exploring the benefits of XGBoost on GPUs and much more.

  GPU, Machine Learning, NVIDIA, XGBoost

• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
  
  A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.

  Cheat Sheet, Data Science Education, Deep Learning, Machine Learning, Mathematics, Open Source, Reinforcement Learning, Resources, Statistics

• The Machine Learning Project Checklist - Dec 7, 2018.
  
  In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."

  Checklist, Machine Learning, Process, Workflow

• DATAx Presents: AI AND MACHINE LEARNING TRENDS IN 2019 - Dec 6, 2018.
  
  This free Ebook from DATAx offers advice on using AI and machine learning to enhance customer satisfaction, how chief data officers are taking the reins on AI strategy, successful case studies from across the business, and more.

  AI, ebook, Machine Learning, Trends

• Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
  
  We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

  Data Preparation, Data Science, Data Visualization, Machine Learning, Missing Values, Mistakes, Multicollinearity

• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
  
  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.

  Pages: 1 2

  Explainable AI, Interpretability, LIME, Machine Learning, SHAP

• KDnuggets™ News 18:n46, Dec 5: AI, Data Science, Analytics 2018 Main Developments, 2019 Key Trends; Deep Learning Cheat Sheets - Dec 5, 2018.
  
  Also: Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools; How to Build a Machine Learning Team When You Are Not Google or Facebook; A Complete Guide to Choosing the Best Machine Learning Course; Handling Imbalanced Datasets in Deep Learning

  2019 Predictions, AI, Analytics, Cheat Sheet, Data Science, Deep Learning, Facebook, Google, Machine Learning

• Graph-Powered Machine Learning - Dec 3, 2018.
  
  This book from Manning Publications is a wonderful introduction to graphs for machine learning enthusiasts, as well as a great entrée into machine learning for graph experts.

  Book, Data Visualization, Machine Learning, Manning

• Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
  
  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau

• Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
  
  We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.

  AI, Explainable AI, Explanation, Interpretability, Machine Learning, Trust

• A Complete Guide to Choosing the Best Machine Learning Course - Nov 30, 2018.
  
  A collection of the best courses covering machine learning concepts and techniques, including supervised and unsupervised learning, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

  Career, Machine Learning, Online Education, Simplilearn

• Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
  
  Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  Pages: 1 2

  AI, Deep Learning, Machine Learning, Semantic Analysis

• Variational Autoencoders Explained in Detail - Nov 30, 2018.
  
  We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.

  Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow

• How to Build a Machine Learning Team When You Are Not Google or Facebook - Nov 28, 2018.
  
  If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.

  Data Science Team, Google, Lukas Biewald, Machine Learning, Team

• Making Machine Learning Accessible [Webinar Replay] - Nov 27, 2018.
  
  Learn the business "why" and technical "how" for implementing machine learning in your organization - watch now.

  ActiveState, Deployment, Machine Learning

• Bringing Machine Learning Research to Product Commercialization - Nov 27, 2018.
  
  In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.

  Academics, Machine Learning, Products, Research

• KDnuggets™ News 18:n44, Nov 21: What is the Best Python IDE for Data Science?; Anticipating the next move in data science - Nov 21, 2018.
  
  Also: Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries; Predictive Analytics in 2018: Salaries & Industry Shifts; Sorry I didn't get that! How to understand what your users want; Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU

  Cloud, Data Science, Gradient Boosting, Interpretability, Machine Learning, Predictive Analytics, Python

• Machine Learning in Action: Going Beyond Decision Support Data Science - Nov 20, 2018.
  
  In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking.

  Business Value, Data Science, Data Science Team, Decision Support, Machine Learning

• How Important is that Machine Learning Model be Understandable? We analyze poll results - Nov 19, 2018.
  
  About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.

  Asia, Europe, Explainable AI, Explanation, GDPR, Machine Learning, Poll, USA

• What I Learned About Machine Learning at ODSC West 2018 - Nov 19, 2018.
  
  Reflecting back on the ODSC West 2018 conference, with a review of some of the best talks on topics including active learning, interactive coefficient plots, time-series forecasting, and more.

  Machine Learning, ODSC, San Francisco

• How to Choose an AI Vendor - Nov 16, 2018.
  
  This report explores why it is so challenging to choose an AI vendor and what you should consider as you seek a partner in AI. Download now.

  AI, DataRobot, Machine Learning, White Paper

• Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
  
  Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.

  Boosting, Gradient Boosting, Machine Learning, Python

• [Download] Real-Life ML Examples + Notebooks - Nov 13, 2018.
  
  In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!

  Databricks, ebook, Jupyter, Machine Learning, Use Cases

• The ultimate guide to starting AI - Nov 13, 2018.
  
  A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.

  AI, Deployment, Ethics, Machine Learning, Production

• Machine Learning Toronto Summit
  Nov 20-21 – Special KDnuggets discount - Nov 12, 2018.
  
  The Toronto Machine Learning Summit takes place Nov 20-21. Register to celebrate Canada's top AI Research, and enjoy a 30% off -Special Discount with code KDNUGGETS. Register now!

  Canada, Machine Learning, Summit, Toronto

• Dr. Data Show Video: What the Hell Does “Data Science” Really Mean? - Nov 10, 2018.
  
  The latest episode of the Dr. Data Show answers the question, "What the hell is data science?"

  Data Science, Eric Siegel, Machine Learning, Predictive Analytics, Predictive Analytics World

• 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
  
  Check out a collection of free machine learning and data science courses to kick off your winter learning season.

  Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex

• 7 Best Practices for Machine Learning on a Data Lake - Nov 7, 2018.
  
  Download this report to learn about the data requirements for advanced analytics on a data lake, and best practices such analytics with a focus on machine learning.

  Best Practices, Data Lake, Machine Learning, TDWI

• Quantum Machine Learning: A look at myths, realities, and future projections - Nov 5, 2018.
  
  An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.

  Machine Learning, Python, Quantum Computing, Statistics

• Machine Learning Classification: A Dataset-based Pictorial - Nov 5, 2018.
  
  In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.

  Datasets, Machine Learning, Supervised Learning

• Learn how machine learning is transforming business, Nov 12 Webinar - Nov 2, 2018.
  
  In this webinar on Nov 12, titled How to Transform Your Business with Automated Machine Learning, learn the difference between AI, machine learning, and deep learning, the challenges of implementing traditional data science solutions, and how automated machine learning enables more employees to take part in AI initiatives.

  AI, Automated Machine Learning, DataRobot, Machine Learning

• How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
  
  The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly.

  Machine Learning, NLP, NLTK, Python, Tokenization

• In-Depth Training for the Future of Data, Orlando, Nov 11-16 – Save with code KD30 - Oct 30, 2018.
  
  Planning and implementing new data and analytics initiatives can be overwhelming. Join us in Orlando for in-depth training that will give you the needed skills and use code KD30 thru Nov 7 to save.

  Bootcamp, Dashboard, Data Integration, Machine Learning, Orlando, TDWI

• New Poll: How Important is Understanding Machine Learning Models? - Oct 30, 2018.
  
  New KDnuggets poll is asking: When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable? Please vote

  Explainable AI, Explanation, GDPR, Machine Learning, Poll

• Moody’s Analytics: Machine Learning / NLP – Research Scientist / Engineer [New York, NY] - Oct 30, 2018.
  
  Moody's Analytics is seeking a Machine Learning / NLP - Research Scientist / Engineer in New York, NY, to drive algorithmic improvements by enabling new automation capabilities, and improving efficiency and performance across multiple business lines.

  Engineer, Machine Learning, Moody's Analytics, New York, NY, Research Scientist

• Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
  
  We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.

  Pages: 1 2

  Machine Learning, Naive Bayes, Python

• Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
  
  What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.

  Automated Machine Learning, Feature Engineering, Feature Selection, Hyperparameter, Machine Learning, Open Source

• Top KDnuggets tweets, Oct 17-23: Machine Learning Cheat Sheets - Oct 24, 2018.
  
  Also 5 "Clean Code" Tips That Will Dramatically Improve Your Productivity; Data Science Cheat Sheet.

  Cheat Sheet, Machine Learning, Top tweets

• New Book: Linear Algebra – what you need for Machine Learning and Data Science now - Oct 24, 2018.
  
  From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.

  Beginners, Book, Linear Algebra, Machine Learning, Mathematics, Richard Han

• U. of Zurich: Assistant Professorship in AI and Machine Learning (Non-tenure Track) [Zurich, Switzerland] - Oct 24, 2018.
  
  Candidates should hold a Ph.D. degree in Computer Science with specialization in Machine Learning and/or Data Mining including Deep Learning, lnteractive/ Cooperative Learning, statistical learning or privacy preserving modeling, and have an excellent record of academic achievements in the relevant fields.

  AI, Faculty, Machine Learning, Switzerland, Zurich

• Building a Question-Answering System from Scratch - Oct 24, 2018.
  
  This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.

  Machine Learning, NLP, Question answering

• A Deep Look at Deep Learning: Understanding The Basics of How (and Why) it Works - Oct 23, 2018.
  
  In this illustrated guide by Dataiku you'll learn what exactly deep learning is and why its growing and why it can be more powerful than classical machine learning (ML).

  Dataiku, Deep Learning, Machine Learning

• Introduction to Active Learning - Oct 23, 2018.
  
  An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.

  Active Learning, Data Preparation, Figure Eight, Machine Learning

• Speak at Mega-PAW Vegas 2019 – on Machine Learning Deployment (Apply by Nov 15) - Oct 22, 2018.
  
  Presenting at PAW is a fulfilling way to engage with the leading members of the machine learning community, offers a chance to share how predictive analytics delivers an impact for your organization, and provides complimentary registration/access to the PAW event.

  Deep Learning, Las Vegas, Machine Learning, NV, PAW, Predictive Analytics World

• How to Define a Machine Learning Problem Like a Detective - Oct 22, 2018.
  
  The common refrain among machine learning practitioners is that it’s as much an art as a science. True enough, but in this discipline, you can only appreciate the former if you understand the latter.

  Crime, Data journalism, Machine Learning

• Dr. Data Show Video: How Can You Trust AI? - Oct 20, 2018.
  
  This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.

  AI, Eric Siegel, Machine Learning, Predictive Analytics, Predictive Analytics World, Trust

• The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
  
  Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.

  Bayesian, Distribution, Hyperparameter, Machine Learning, Optimization

• New Jobs Sure to Emerge Alongside Artificial Intelligence - Oct 18, 2018.
  
  There’s a lot of doomsaying about AI pushing humans out of jobs and destroying entire industries. Is it as bad as all that? Maybe not!

  AI, Data Science, Machine Learning

• Mindstrong Health: Sr Data Scientist / Machine Learning, Statistics, Coding [Palo Alto, CA] - Oct 17, 2018.
  
  Mindstrong Health is seeking a Sr Data Scientist in Palo Alto, CA, who is passionate about our mission, committed to excellence and excited to build a company that will address one of the greatest health challenges of our time.

  CA, Data Scientist, Machine Learning, Mindstrong Health, Palo Alto, Statistics

• The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
  
  Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.

  Machine Learning, Neural Networks, NLP, Text Classification

• GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
  
  This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools.

  Data Science, Docker, Ensemble Methods, fast.ai, GitHub, Machine Learning, NLP, Python

• Machine learning — Is the emperor wearing clothes? - Oct 12, 2018.
  
  We take a look at the core concepts of Machine Learning, including the data, algorithm and optimization needed to get you started, with links to additional resources to help enhance your knowledge.

  Algorithms, Beginners, Machine Learning

• We Sized Washington’s Edible Marijuana Market Using AI - Oct 12, 2018.
  
  As Canada legalizes marijuana, it might look to Washington to understand how pot sells. With the RAND Corp., we used machine learning to estimate how much THC- in pot is sold in Washington.

  AI, Canada, Data Science, Law, Machine Learning, Washington

• Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
  
  The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.

  Confusion Matrix, Data Science, Machine Learning, Metrics, Predictive Modeling

• a4 Media: Manager, Machine Learning Data Engineer [Long Island City, NY] - Oct 10, 2018.
  
  a4 Media is seeking a Manager, Machine Learning Data Engineer in Long Island City, NY, focused on managing the business understanding, data acquisition, and data understanding phases of an agile CRISP-data science process.

  a4 Media, Data Engineer, Long Island City, Machine Learning, Manager, NY

• KDnuggets™ News 18:n38, Oct 10: Concise Explanation of Learning Algorithms; Why I Call Myself a Data Scientist; Linear Regression in the Wild - Oct 10, 2018.
  
  This week, KDnuggets brings you a discussion of learning algorithms with a hat tip to Tom Mitchell, discusses why you might call yourself a data scientist, explores machine learning in the wild, checks out some top trends in deep learning, shows you how to learn data science if you are low on finances, and puts forth one person's opinion on the top 8 Python machine learning libraries to help get the job done.

  Algorithms, Data Science, Deep Learning, Linear Regression, Machine Learning, Python, Tom Mitchell

• Top 8 Python Machine Learning Libraries - Oct 9, 2018.
  
  Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  GitHub, Keras, Machine Learning, Python

• A Concise Explanation of Learning Algorithms with the Mitchell Paradigm - Oct 5, 2018.
  
  A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.

  Algorithms, Learning, Machine Learning, Tom Mitchell

• Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
  
  An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.

  Deep Learning, Image Recognition, Machine Learning, Object Detection, Segmentation

• KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R - Oct 3, 2018.
  
  Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning

  Computer Vision, Deep Learning, Machine Learning, Mathematics, NLP, R, Transfer Learning

• DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
  
  Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.

  ActiveState, Development, DevOps, Machine Learning, Software

• How to Create a Simple Neural Network in Python - Oct 2, 2018.
  
  The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.

  Machine Learning, Neural Networks, Python

• 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
  
  In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.

  Cross-validation, Data Science, Machine Learning

• Dr. Data Show Video: Why Machine Learning Is the Coolest Science - Oct 1, 2018.
  
  This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.

  Eric Siegel, Machine Learning, Predictive Analytics, Predictive Analytics World

• Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
  
  A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.

  Deep Learning, Explained, Flat Minima, Linear Networks, Machine Learning, Optimization, SGD

• Math for Machine Learning - Sep 28, 2018.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  Book, ebook, Machine Learning, Mathematics, Richard Han

• Learning mathematics of Machine Learning: bridging the gap - Sep 28, 2018.
  
  We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning?

  AI, Machine Learning, Mathematics

• Building Actionable Machine Learning Models using Trifacta, DataRobot, and Tableau - Sep 26, 2018.
  
  In this webinar, learn best practices and practical implementation tips for each step of an automated machine learning project, and see live, hands-on demos of Trifacta, DataRobot, and Tableau.

  Automated Machine Learning, DataRobot, Machine Learning, Tableau

• ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise - Sep 26, 2018.
  
  Key takeaways and highlights from ODSC India 2018 conference about the latest trends, breakthroughs and revolutions in the field of Data Science and Artificial Intelligence

  AI, Chatbot, Deep Learning, Machine Learning, NLP, ODSC, Recurrent Neural Networks, Reinforcement Learning, Speech Recognition