Machine Learning (1333)

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• When Bayes, Ockham, and Shannon come together to define machine learning - Sep 25, 2018.
  
  A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.

  Bayes Theorem, Machine Learning

• Building a Machine Learning Model through Trial and Error - Sep 24, 2018.
  
  A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.

  Deployment, Machine Learning, MathWorks

• Machine Learning: How to Build a Model From Scratch - Sep 20, 2018.
  
  Register now for upcoming webinar, Building a Machine Learning Fraud Model with Momentum Travel, on Sep 27 @ 10 AM PT.

  Machine Learning, Modeling, WhitePages

• 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
  
  Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

  Data Science, Machine Learning, Neural Networks

• Free resources to learn Natural Language Processing - Sep 18, 2018.
  
  An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.

  Beginners, Machine Learning, Machine Translation, NLP, Sentiment Analysis, Text Classification

• Machine Learning Cheat Sheets - Sep 11, 2018.
  
  Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.

  Cheat Sheet, Deep Learning, Machine Learning, Mathematics, Neural Networks, Probability, Statistics, Supervised Learning, Tips, Unsupervised Learning

• Journey to Machine Learning – 100 Days of ML Code - Sep 7, 2018.
  
  A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.

  GitHub, K-nearest neighbors, Machine Learning, Python, SVM

• AI Knowledge Map: How To Classify AI Technologies - Aug 31, 2018.
  
  What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.

  AI, Classification, Deep Learning, Machine Intelligence, Machine Learning, Neural Networks

• How to Make Your Machine Learning Models Robust to Outliers - Aug 28, 2018.
  
  In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.

  Machine Learning, Modeling, Outliers

• 9 Things You Should Know About TensorFlow - Aug 22, 2018.
  
  A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.

  Deep Learning, Google, Keras, Machine Learning, Python, TensorFlow

• Interpreting a data set, beginning to end - Aug 20, 2018.
  
  Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.

  Analytics, Big Data, Data Science, Data Visualization, Machine Learning, SAS, Statistics, t-SNE

• Why Automated Feature Engineering Will Change the Way You Do Machine Learning - Aug 20, 2018.
  
  Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.

  Automated Machine Learning, Feature Engineering, Machine Learning, Python

• Cartoon: Machine Learning takes a vacation - Aug 18, 2018.
  
  August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.

  Cartoon, Deep Learning, Humor, Machine Learning, Robots

• Reinforcement Learning: The Business Use Case, Part 2 - Aug 16, 2018.
  
  In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.

  Business, Finance, Machine Learning, Reinforcement Learning, Use Cases

• Setting up your AI Dev Environment in 5 Minutes - Aug 13, 2018.
  
  Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.

  AI, datmo, Development, Docker, Machine Learning, Python, TensorFlow

• Unsupervised Learning Demystified - Aug 13, 2018.
  
  Unsupervised learning is a pattern-finding technique for mining inspiration from your data. Let's demystify!

  Cassie Kozyrkov, Clustering, Machine Learning, Unsupervised Learning

• Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
  
  Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.

  Comet.ml, Cross-validation, Machine Learning, Modeling, scikit-learn

• Reinforcement Learning: The Business Use Case, Part 1 - Aug 9, 2018.
  
  At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.

  Business, Machine Learning, Reinforcement Learning, Use Cases

• Selecting the Best Machine Learning Algorithm for Your Regression Problem - Aug 1, 2018.
  
  This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!

  Algorithms, Machine Learning, Regression

• Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks - Jul 27, 2018.
  
  Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.

  Jupyter, Machine Learning, Microsoft, Python, R, SQL, SQL Server

• 9 Reasons why your machine learning project will fail - Jul 25, 2018.
  
  This article explains in detail some of the issues that you may face during your machine learning project.

  Deployment, Failure, Machine Learning, Project Fail

• Receiver Operating Characteristic Curves Demystified (in Python) - Jul 20, 2018.
  
  In this blog, I will reveal, step by step, how to plot an ROC curve using Python. After that, I will explain the characteristics of a basic ROC curve.

  Machine Learning, Metrics, Python, ROC-AUC

• Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence - Jul 18, 2018.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  ebook, Machine Learning, Mathematics, Richard Han

• What is Minimum Viable (Data) Product? - Jul 12, 2018.
  
  This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.

  AirBnB, Data, Machine Learning, Products

• fast.ai Machine Learning Course Notes - Jul 6, 2018.
  
  This posts is a collection of a set of fantastic notes on the fast.ai machine learning MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  fast.ai, Jeremy Howard, Machine Learning, MOOC

• Automated Machine Learning vs Automated Data Science - Jul 2, 2018.
  
  Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.

  Automated Data Science, Automated Machine Learning, Data Science, Machine Learning

• 5 Data Science Projects That Will Get You Hired in 2018 - Jun 26, 2018.
  
  A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

  Data Preparation, Data Science, Data Visualization, Hiring, Jupyter, Machine Learning

• How to Execute R and Python in SQL Server with Machine Learning Services - Jun 25, 2018.
  
  Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.

  Azure ML, Machine Learning, Microsoft, Python, R, SQL, SQL Server

• 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - Jun 25, 2018.
  
  Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.

  AI, Deep Learning, Machine Learning, NLP

• An Intuitive Introduction to Gradient Descent - Jun 21, 2018.
  
  This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.

  Gradient Descent, Machine Learning, Optimization

• The 5 Clustering Algorithms Data Scientists Need to Know - Jun 20, 2018.
  
  Today, we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!

  Clustering, Data Scientist, DBSCAN, Machine Learning

• Data Science Predicting The Future - Jun 19, 2018.
  
  In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.

  Data Science, Forecasting, Machine Learning, Programming Languages, Regression

• Step Forward Feature Selection: A Practical Example in Python - Jun 18, 2018.
  
  When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.

  Feature Selection, Machine Learning, Python

• IoT on AWS: Machine Learning Models and Dashboards from Sensor Data - Jun 15, 2018.
  
  I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  AWS, Dashboard, IoT, Machine Learning, Rubens Zimbres

• 5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
  
  Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!

  Interpretability, Keras, Machine Learning, Model Performance, NLP, Overlook, Recurrent Neural Networks, Visualization

• How (dis)similar are my train and test data? - Jun 7, 2018.
  
  This articles examines a scenario where your machine learning model can fail.

  Data Science, Datasets, Feature Selection, Machine Learning, Training Data

• The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
  
  We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

  Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow

• Three techniques to improve machine learning model performance with imbalanced datasets - Jun 5, 2018.
  
  The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.

  Balancing Classes, Machine Learning, Unbalanced

• How To Build Intelligent Dashboards Powered by Machine Learning - Jun 1, 2018.
  
  In this webinar on Jun 5, 1:00 pm ET, analytics industry expert Jen Underwood will demonstrate how to visualize machine learning results with dashboard tools.

  Business Intelligence, Dashboard, DataRobot, Machine Learning

• Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM - May 29, 2018.
  
  CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.

  CRISP-DM, Deep Learning, Descriptive Analytics, Machine Learning

• 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
  
  Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.

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

• How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
  
  The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.

  Pages: 1 2

  Altexsoft, Crowdsourcing, Data Labeling, Data Preparation, Image Recognition, Machine Learning, Training Data

• The Executive Guide to Data Science and Machine Learning - May 10, 2018.
  
  This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.

  Big Data, Business, Data Science, Machine Learning

• Deep learning scaling is predictable, empirically - May 10, 2018.
  
  This study starts with a simple question: “how can we improve the state of the art in deep learning?”

  Deep Learning, Machine Learning, Scalability

• 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning” - May 8, 2018.
  
  Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.

  Andrew Ng, Book, Data Cleaning, Data Preparation, Free ebook, Machine Learning, Metrics

• Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
  
  Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.

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

• 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - May 8, 2018.
  
  Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.

  Data Scientist, Logistic Regression, Machine Learning

• 2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2018.
  
  Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

  Data Mining Software, Data Science, Machine Learning, Poll

• KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
  
  Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial

  Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python

• 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
  
  Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.

  API, Face Recognition, Image Recognition, Machine Learning, Natural Language Processing, Sentiment Analysis, Text Analytics

• Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
  
  This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.

  AI, Business, Business Analytics, Data Analytics, Data Science, Machine Learning

• Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
  
  In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives

  DevOps, Machine Learning, Metrics, MLOps

• What should be focus areas for Machine Learning / AI in 2018? - Apr 27, 2018.
  
  This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.

  2018 Predictions, AI, Machine Learning, Production

• The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
  
  Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!

  Data Cleaning, Data Preparation, Data Science, Machine Learning

• Top 16 Open Source Deep Learning Libraries and Platforms - Apr 24, 2018.
  
  We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.

  Caffe, GitHub, Keras, Machine Learning, Open Source, TensorFlow

• 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
  
  It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.

  Book, Data Science, Ian Goodfellow, Machine Learning, Mathematics, Robert Tibshirani, Vladimir Vapnik

• Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
  
  This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.

  Algorithms, Data Science, Machine Learning, Online Education, Statistics

• Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
  
  Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?

  API, Deep Learning, Francois Chollet, Keras, Machine Learning, Neural Networks, TensorFlow

• Don’t learn Machine Learning in 24 hours - Apr 13, 2018.
  
  When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.

  Advice, Andrew Ng, Machine Learning, Peter Norvig

• Onboarding Your Machine Learning Program - Apr 12, 2018.
  
  Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.

  Advice, Applications, Cats, Industry, Machine Learning

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
  
  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  Pages: 1 2

  Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM

• Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
  
  Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.

  Deep Learning, Deep Neural Network, Free ebook, Machine Learning, Neural Networks

• Comet.ml – Machine Learning Experiment Management - Apr 9, 2018.
  
  This article presents comet.ml – a platform that allows tracking machine learning experiments with an emphasis on collaboration and knowledge sharing.

  Comet.ml, Experimentation, Machine Learning

• Machine Learning for Text - Apr 9, 2018.
  
  This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.

  Book, Charu Aggarwal, Information Retrieval, Machine Learning, Text Mining

• Supervised vs. Unsupervised Learning - Apr 4, 2018.
  
  Understanding the differences between the two main types of machine learning methods.

  Machine Learning, Supervised Learning, Unsupervised Learning

• Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
  
  Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.

  Algorithms, Deep Learning, Machine Learning, Neural Networks, TensorFlow, Text Analytics, Trends

• 5 Things You Need to Know about Reinforcement Learning - Mar 28, 2018.
  
  With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.

  Machine Learning, Markov Chains, Reinforcement Learning, Richard Sutton

• Introduction to k-Nearest Neighbors - Mar 22, 2018.
  
  What is k-Nearest-Neighbors (kNN), some useful applications, and how it works.

  K-nearest neighbors, Machine Learning

• CatBoost vs. Light GBM vs. XGBoost - Mar 22, 2018.
  
  Who is going to win this war of predictions and on what cost? Let’s explore.

  Decision Trees, Gradient Boosting, Machine Learning, XGBoost

• Multiscale Methods and Machine Learning - Mar 19, 2018.
  
  We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.

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

• Quick Feature Engineering with Dates Using fast.ai - Mar 16, 2018.
  
  The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.

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

• Will GDPR Make Machine Learning Illegal? - Mar 14, 2018.
  
  Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.

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

• How to do Machine Learning Efficiently - Mar 13, 2018.
  
  I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this.

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

• Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
  
  The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.

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

• 5 Things to Know About Machine Learning - Mar 7, 2018.
  
  This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.

  Accuracy, Data Preparation, Ensemble Methods, Google Colab, Jupyter, Machine Learning, Validation

• Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
  
  Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

  Data Science, Deep Learning, Machine Learning, Predictive Modeling, Stationarity, Time Series

• The Current Hype Cycle in Artificial Intelligence - Feb 28, 2018.
  
  Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

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

• Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 27, 2018.
  
  We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.

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

• Applying Machine Learning to DevOps - Feb 27, 2018.
  
  This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.

  DevOps, Machine Learning

• Top 20 Python AI and Machine Learning Open Source Projects - Feb 20, 2018.
  
  We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.

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

• 5 Things You Need To Know About Data Science - Feb 19, 2018.
  
  Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists

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

• Logistic Regression: A Concise Technical Overview - Feb 16, 2018.
  
  Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.

  Algorithms, Classification, Logistic Regression, Machine Learning, Regression

• Resurgence of AI During 1983-2010 - Feb 16, 2018.
  
  We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.

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

• Cartoon: Machine Learning Problems in 2118 - Feb 14, 2018.
  
  For Valentine's day, new KDnuggets cartoon looks at some problems Machine Learning can face in 2118.

  Cartoon, Machine Learning, Robots, Valentine's Day

• Which Machine Learning Algorithm be used in year 2118? - Feb 9, 2018.
  
  So what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.

  Algorithms, Machine Learning, Regression, Trends

• Top 15 Scala Libraries for Data Science in 2018 - Feb 9, 2018.
  
  For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.

  Apache Spark, Data Analysis, Data Science, Data Visualization, Machine Learning, NLP, Scala

• 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
  
  It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!

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

• Deep Feature Synthesis: How Automated Feature Engineering Works - Feb 7, 2018.
  
  Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.

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

• 5 Fantastic Practical Machine Learning Resources - Feb 6, 2018.
  
  This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.

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

• A Simple Starter Guide to Build a Neural Network - Feb 5, 2018.
  
  This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.

  Machine Learning, Neural Networks, Python, PyTorch

• The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - Jan 31, 2018.
  
  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  Pages: 1 2

  Architecture, Deep Learning, Machine Learning, Neural Networks

• Automated Text Classification Using Machine Learning - Jan 30, 2018.
  
  In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.

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

• Data Structures Related to Machine Learning Algorithms - Jan 30, 2018.
  
  If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.

  Pages: 1 2

  Machine Learning, Mathematics, Programming, Statsbot

• Error Analysis to your Rescue – Lessons from Andrew Ng, part 3 - Jan 29, 2018.
  
  The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm

  Andrew Ng, Bias, Distribution, Machine Learning, Variance

• Deep Learning in H2O using R - Jan 22, 2018.
  
  This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.

  Backpropagation, Deep Learning, Gradient Descent, H2O, Machine Learning, R

• Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI - Jan 22, 2018.
  
  A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.

  Pages: 1 2

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

• Learning Curves for Machine Learning - Jan 17, 2018.
  
  But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.

  Pages: 1 2

  Bias, Machine Learning, Metrics, Training Data, Variance