Machine Learning (1333)

• A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
  
  This article explains the Support Vector Machines (SVM) algorithm in an easy way.

  Algorithms, Explained, Machine Learning, Support Vector Machines, SVM

• Can graph machine learning identify hate speech in online social networks? - Sep 11, 2019.
  
  Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

  Graph Analytics, Machine Learning, Social Network Analysis, Twitter

• Train sklearn 100x Faster - Sep 11, 2019.
  
  As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

  Distributed Systems, Machine Learning, Python, scikit-learn, Training

• Scikit-Learn vs mlr for Machine Learning - Sep 10, 2019.
  
  How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

  Exxact, Machine Learning, R, scikit-learn

• Common Machine Learning Obstacles - Sep 9, 2019.
  
  In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

  Cross-validation, Decision Trees, Logistic Regression, Machine Learning, MathWorks, Overfitting, SVM

• OpenStreetMap Data to ML Training Labels for Object Detection - Sep 9, 2019.
  
  I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location.

  Geospatial, Machine Learning, Object Detection, Python

• Build Your First Voice Assistant - Sep 6, 2019.
  
  Hone your practical speech recognition application skills with this overview of building a voice assistant using Python.

  Machine Learning, NLP, Python, Speech Recognition

• Advice on building a machine learning career and reading research papers by Prof. Andrew Ng - Sep 5, 2019.
  
  This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.

  Andrew Ng, Career, Machine Learning, Research

• An Easy Introduction to Machine Learning Recommender Systems - Sep 4, 2019.
  
  Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.

  Beginners, Machine Learning, Python, Recommendation Engine, Recommender Systems

• Python Libraries for Interpretable Machine Learning - Sep 4, 2019.
  
  In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.

  Bias, Interpretability, LIME, Machine Learning, Python, SHAP

• 6 Tips for Building a Training Data Strategy for Machine Learning - Sep 2, 2019.
  
  Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.

  Advice, Machine Learning, Training Data

• Object-oriented programming for data scientists: Build your ML estimator - Aug 30, 2019.
  
  Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

  Data Scientist, Machine Learning, Programming, Python

• Types of Bias in Machine Learning - Aug 29, 2019.
  
  The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.)

  Bias, Data Science, Data Scientist, Machine Learning

• Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing - Aug 27, 2019.
  
  Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

  AI, Explainability, Machine Learning, Modeling

• Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference? - Aug 26, 2019.
  
  Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.

  AI, Deep Learning, Machine Learning

• How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions - Aug 22, 2019.
  
  As machine learning evolves, the need for tools and platforms that automate the lifecycle management of training and testing datasets is becoming increasingly important. Fast growing technology companies like Uber or LinkedIn have been forced to build their own in-house data lifecycle management solutions to power different groups of machine learning models.

  AirBnB, Data Management, LinkedIn, Machine Learning, Netflix, Uber

• Understanding Cancer using Machine Learning - Aug 16, 2019.
  
  Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis.

  Cancer Detection, Healthcare, Machine Learning, Medical

• Statistical Modelling vs Machine Learning - Aug 14, 2019.
  
  At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

  Advice, Data Science, Machine Learning, Statistics

• 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
  
  If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

  AI, Andrew Ng, Best Practices, Deployment, Machine Learning, Metrics, Training Data

• Knowing Your Neighbours: Machine Learning on Graphs - Aug 8, 2019.
  
  Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.

  Convolutional Neural Networks, Graph Analytics, Graph Mining, Machine Learning

• Coding Random Forests® in 100 lines of code* - Aug 7, 2019.
  
  There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.

  Algorithms, Machine Learning, Multicollinearity, R, random forests algorithm

• Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment - Aug 5, 2019.
  
  This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside.

  Machine Learning, Report, Survey

• Opening Black Boxes: How to leverage Explainable Machine Learning - Aug 1, 2019.
  
  A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model.

  Explainable AI, Feature Selection, LIME, Machine Learning, SHAP, XAI

• Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning - Jul 29, 2019.
  
  Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

  AI, Analytics, Data Science, Machine Learning, Podcast

• Decentralized and Collaborative AI: How Microsoft Research is Using Blockchains to Build More Transparent Machine Learning Models - Jul 29, 2019.
  
  Recently, AI researchers from Microsoft open sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine learning models based on blockchain technologies.

  AI, Blockchain, Machine Learning, Microsoft, Transparency

• High-Quality AI And Machine Learning Data Labeling At Scale: A Brief Research Report - Jul 25, 2019.
  
  Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?

  AI, Cloudfactory, Data Labeling, Machine Learning, Report, Research

• Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI - Jul 25, 2019.
  
  Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.

  Business Analytics, Certificate, Certification, Data Science Certificate, Education, Machine Learning, Online Education, SAS Certification

• Is Bias in Machine Learning all Bad? - Jul 23, 2019.
  
  We have been taught over our years of predictive model building that bias will harm our model. Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias.

  Bias, Data Science, Machine Learning

• Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning - Jul 19, 2019.
  
  This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning.

  Bayesian, Machine Learning, Quantum Computing

• Dealing with categorical features in machine learning - Jul 16, 2019.
  
  Many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms.

  Data Cleaning, Data Preprocessing, Feature Engineering, Machine Learning, Python

• Classifying Heart Disease Using K-Nearest Neighbors - Jul 8, 2019.
  
  I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.

  Pages: 1 2

  Healthcare, K-nearest neighbors, Machine Learning, Medical, Python

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph - Jun 25, 2019.
  
  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  Advice, Data Science, Data Scientist, Graphs, Machine Learning

• 10 New Things I Learnt from fast.ai Course V3 - Jun 24, 2019.
  
  Fastai offers some really good courses in machine learning and deep learning for programmers. I recently took their "Practical Deep Learning for Coders" course and found it really interesting. Here are my learnings from the course.

  Deep Learning, fast.ai, Jeremy Howard, Machine Learning, MOOC

• 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition - Jun 24, 2019.
  
  Interested in mastering data preparation with Python? Follow these 7 steps which cover the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

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

• The Machine Learning Puzzle, Explained - Jun 17, 2019.
  
  Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.

  Algorithms, Explained, Machine Learning, Modeling

• Why Machine Learning is vulnerable to adversarial attacks and how to fix it - Jun 13, 2019.
  
  Machine learning can process data imperceptible to humans to produce expected results. These inconceivable patterns are inherent in the data but may make models vulnerable to adversarial attacks. How can developers harness these features to not lose control of AI?

  Adversarial, Machine Learning, Safety, Security

• Overview of Different Approaches to Deploying Machine Learning Models in Production - Jun 12, 2019.
  
  Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  Deployment, Jupyter, Machine Learning, Production, Training Data

• How to Automate Hyperparameter Optimization - Jun 12, 2019.
  
  A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.

  Bayesian, Deep Learning, Hyperparameter, Machine Learning, Neural Networks, Optimization, Python, TensorFlow

• 3 Main Approaches to Machine Learning Models - Jun 11, 2019.
  
  Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.

  Decision Trees, Linear Regression, Machine Learning, Naive Bayes

• Choosing an Error Function - Jun 10, 2019.
  
  The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.

  Cost Function, Machine Learning

• Using the ‘What-If Tool’ to investigate Machine Learning models - Jun 6, 2019.
  
  The machine learning practitioner must be a detective, and this tool from teams at Google enables you to investigate and understand your models.

  Advice, Data Science Tools, Data Visualization, Machine Learning, TensorFlow

• KDnuggets™ News 19:n21, Jun 5: Transitioning your Career to Data Science; 11 top Data Science, Machine Learning platforms; 7 Steps to Mastering Intermediate ML w. Python - Jun 5, 2019.
  
  The results of KDnuggets 20th Annual Software Poll; How to transition to a Data Science career; Mastering Intermediate Machine Learning with Python ; Understanding Natural Language Processing (NLP); Backprop as applied to LSTM, and much more.

  Backpropagation, Data Science Platform, LSTM, Machine Learning, NLP, Python

• Clearing air around “Boosting” - Jun 3, 2019.
  
  We explain the reasoning behind the massive success of boosting algorithms, how it came to be and what we can expect from them in the future.

  Boosting, Gradient Boosting, Machine Learning, XGBoost

• 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
  
  This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!

  7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning

• How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks - May 30, 2019.
  
  The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?

  Deep Learning, Lottery, Machine Learning, Neural Networks, Training Data

• How to use continual learning in your ML models, June 19 Webinar - May 29, 2019.
  
  This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.

  cnvrg.io, Kubernetes, Machine Learning, Production, TensorFlow

• Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
  
  We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.

  AI, Deployment, Failure, Machine Learning, Scalability

• Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint - May 24, 2019.
  
  Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.

  Pages: 1 2

  Apache Spark, Big Data, Deep Learning, Machine Learning, NLP, Optimus, Python, Twint

• Your Guide to Natural Language Processing (NLP) - May 23, 2019.
  
  This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.

  AI, Data Science, Machine Learning, Natural Language Processing, NLP, Tokenization

• End-to-End Machine Learning: Making videos from images - May 23, 2019.
  
  Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.

  Data Preparation, Image Processing, Machine Learning

• How do you teach physics to machine learning models? - May 21, 2019.
  
  How to integrate physics-based models (these are math-based methods that explain the world around us) into machine learning models to reduce its computational complexity.

  Machine Learning, Model Performance, Physics

• The Data Fabric for Machine Learning – Part 1 - May 21, 2019.
  
  How the new advances in semantics and the data fabric can help us be better at Machine Learning

  Advice, Data Science, Data Scientist, Machine Learning

• Building a Computer Vision Model: Approaches and datasets - May 20, 2019.
  
  How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.

  AI, Computer Vision, Deep Learning, ImageNet, Machine Learning, Neural Networks

• Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision - May 17, 2019.
  
  Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.

  AI, Algorithms, Computer Vision, Deep Learning, Machine Learning

• Building Recommender systems with Azure Machine Learning service - May 15, 2019.
  
  Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.

  Azure ML, Machine Learning, Microsoft Azure, Recommender Systems

• Customer Churn Prediction Using Machine Learning: Main Approaches and Models - May 14, 2019.
  
  We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  Altexsoft, Churn, Customer Analytics, Machine Learning

• Machine Learning in Agriculture: Applications and Techniques - May 14, 2019.
  
  Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.

  Agriculture, AI, Data Science, Machine Learning, Sciforce

• How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls - May 10, 2019.
  
  We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.

  Forecasting, Machine Learning, Mistakes, Stationarity, Time Series

• Books on Graph-Powered Machine Learning, Graph Databases, Deep Learning for Search – 50% off - May 9, 2019.
  
  These 3 books will help you make the most from graph-powered databases. For a limited time, get 50% off any of them with the code kdngraph.

  Book, Deep Learning, Graph Databases, Machine Learning, Manning, Search, Search Engine

• “Please, explain.” Interpretability of machine learning models - May 9, 2019.
  
  Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.

  Bias, Explainable AI, Interpretability, LIME, Machine Learning, SHAP, XAI

• [White Paper] Unlocking the Power of Data Science & Machine Learning with Python - May 8, 2019.
  
  This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.

  ActiveState, Data Science, Machine Learning, Python, White Paper

• How to fix an Unbalanced Dataset - May 8, 2019.
  
  We explain several alternative ways to handle imbalanced datasets, including different resampling and ensembling methods with code examples.

  Balancing Classes, Data Preparation, Machine Learning, Unbalanced

• 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2019.
  
  Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.

  Big Data, Data Mining Software, Data Science, Deep Learning, Machine Learning, Poll, Programming Languages

• Naive Bayes: A Baseline Model for Machine Learning Classification Performance - May 7, 2019.
  
  We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.

  Pages: 1 2

  Algorithms, Data Science, Machine Learning, Naive Bayes, Python, scikit-learn, Statistics

• XGBoost Algorithm: Long May She Reign - May 2, 2019.
  
  In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.

  Decision Trees, Gradient Boosting, Machine Learning, XGBoost

• Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
  
  Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.

  Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression

• Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
  
  We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  Fraud Detection, Graph Analytics, Graph Databases, Machine Learning, XAI

• An introduction to explainable AI, and why we need it - Apr 15, 2019.
  
  We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.

  AI, Explainable AI, LIME, Machine Learning, XAI

• How can quantum computing be useful for Machine Learning - Apr 12, 2019.
  
  We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.

  Machine Learning, Quantum Computing, SVM

• All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
  
  We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.

  Data Preprocessing, Machine Learning, NLP, Python, Text Analysis, Text Mining

• Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
  
  Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.

  Algorithms, Data Science, Machine Learning, Poll

• Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
  
  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  AI, Data Science, Deep Learning, Keras, Machine Learning, NLP, Reinforcement Learning, TensorFlow, U. of Washington, UC Berkeley, Unsupervised Learning

• Explaining Random Forest® (with Python Implementation) - Mar 29, 2019.
  
  We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.

  Explained, Machine Learning, Python, random forests algorithm

• Interpolation in Autoencoders via an Adversarial Regularizer - Mar 29, 2019.
  
  Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.

  Adversarial, AISC, Autoencoder, Machine Learning

• Mastering Fast Gradient Boosting on Google Colaboratory with free GPU - Mar 19, 2019.
  
  CatBoost is a fast implementation of GBDT with GPU support out-of-the-box. Google Colaboratory is a very useful tool with free GPU support.

  CatBoost, Google Colab, GPU, Gradient Boosting, Machine Learning, Python, Yandex

• Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
  
  This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.

  Pages: 1 2

  AI, Algorithms, Deep Learning, Machine Learning, Neural Networks, numpy, Optimization, Python

• [eBook] Standardizing the Machine Learning Lifecycle - Mar 15, 2019.
  
  We explore what makes the machine learning lifecycle so challenging compared to regular software, and share the Databricks approach.

  Databricks, ebook, Life Cycle, Machine Learning, MLflow

• Top R Packages for Data Cleaning - Mar 15, 2019.
  
  Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.

  Data Cleaning, Data Preparation, Data Science, Machine Learning, R

• My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
  
  We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.

  AI, AlphaStar, GANs, Generative Adversarial Network, Machine Learning, Machine Translation, Reinforcement Learning, Robots

• [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
  
  The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.

  ActiveState, ebook, Machine Learning

• Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
  
  The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.

  Bayesian, Deep Learning, Machine Learning, Sciforce, Text Analysis, Text Mining, Topic Modeling

• AI: Arms Race 2.0 - Mar 12, 2019.
  
  An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.

  AI, China, Deep Learning, Europe, Investment, IoT, Machine Learning, Neural Networks, Startups, Trends, USA

• Beating the Bookies with Machine Learning - Mar 8, 2019.
  
  We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.

  Machine Learning, PyTorch, Sports, Statistics

• 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
  
  For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.

  AI, Data Science, Machine Learning, Women

• Designing Ethical Algorithms - Mar 8, 2019.
  
  Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.

  AI, Algorithms, Bias, Ethics, Machine Learning

• Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
  
  Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.

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

• GANs Need Some Attention, Too - Mar 5, 2019.
  
  Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.

  AISC, Attention, Deep Learning, GANs, Image Generation, Machine Learning

• 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
  
  Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.

  Data Science, Machine Learning, Programming, Python, Workflow

• What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
  
  Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?

  AI, Machine Learning, MOOC, Online Education

• Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
  
  This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset

  Pages: 1 2

  Deep Learning, Machine Learning, Neural Networks, numpy, Python

• State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
  
  We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.

  AI, Machine Learning, Multitask Learning, NLP, Papers with code, Recommender Systems, Semantic Segmentation, TensorFlow, Transfer Learning

• How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
  
  In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.

  Machine Learning, Programming, Python

• Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
  
  We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.

  Deep Learning, Deep Neural Network, Machine Learning, Neural Networks, Optimization, TensorFlow

• An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
  
  If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.

  Machine Learning, Python, scikit-learn

• Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
  
  We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.

  Alteryx, Data Science Platform, Dataiku, DataRobot, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft, RapidMiner, SAS, TIBCO

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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