Machine Learning (1326)

• I Designed My Own Machine Learning and AI Degree - May 13, 2020.
  
  With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.

  Data Science Education, Machine Learning, Online Education

• Machine Learning in Power BI using PyCaret - May 12, 2020.
  
  Check out this step-by-step tutorial for implementing machine learning in Power BI within minutes.

  Clustering, K-means, Machine Learning, Microsoft, Power BI, PyCaret, Python

• Start Your Machine Learning Career in Quarantine - May 11, 2020.
  
  While this quarantine can last two months, make the most of it by starting your career in Machine Learning with this 60-day learning plan.

  Career, Data Science Education, Machine Learning, Online Education

• The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models - May 11, 2020.
  
  The new typed feature schema streamlined the reusability of features across thousands of machine learning models.

  Feature Engineering, Feature Selection, LinkedIn, Machine Learning

• Hyperparameter Optimization for Machine Learning Models - May 7, 2020.
  
  Check out this comprehensive guide to model optimization techniques.

  Hyperparameter, Machine Learning, Modeling, Optimization, Python

• Beginners Learning Path for Machine Learning - May 5, 2020.
  
  So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

  Beginners, Learning Path, Machine Learning

• Getting Started with Spectral Clustering - May 5, 2020.
  
  This post will unravel a practical example to illustrate and motivate the intuition behind each step of the spectral clustering algorithm.

  Clustering, Machine Learning, Python

• Optimize Response Time of your Machine Learning API In Production - May 1, 2020.
  
  This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

  API, Machine Learning, Optimization, Production, Python

• 10 Best Machine Learning Textbooks that All Data Scientists Should Read - Apr 28, 2020.
  
  Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

  Books, Data Scientist, Machine Learning

• 3 Reasons Why We Are Far From Achieving Artificial General Intelligence - Apr 23, 2020.
  
  How far we are from achieving Artificial General Intelligence? We answer this through the study of three limitations of current machine learning.

  AGI, AI, Machine Learning

• Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition - Apr 22, 2020.
  
  If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

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

• Announcing PyCaret 1.0.0 - Apr 21, 2020.
  
  An open source low-code machine learning library in Python. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.

  Machine Learning, Modeling, Open Source, PyCaret, Python

• A Key Missing Part of the Machine Learning Stack - Apr 20, 2020.
  
  With many organizations having machine learning models running in production, some are discovering that inefficiencies exists in the first step of the process: feature definition and extraction. Robust feature management is now being realized as a key missing part of the ML stack, and improving it by applying standard software development practices is gaining attention.

  Feature Engineering, Feature Extraction, Feature Store, Machine Learning

• 4 Steps to ensure your AI/Machine Learning system survives COVID-19 - Apr 17, 2020.
  
  Many AI models rely on historical data to make predictions on future behavior. So, what happens when consumer behavior across the planet makes a 180 degree flip? Companies are quickly seeing less value from some AI systems as training data is no longer relevant when user behaviors and preferences change so drastically. Those who are flexible can make it through this crisis in data, and these four techniques will help you stay in front of the competition.

  AI, Coronavirus, COVID-19, Deployment, Machine Learning

• State of the Machine Learning and AI Industry - Apr 16, 2020.
  
  Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  AI, AutoML, Data Science Platform, Industry, Machine Learning

• Better notebooks through CI: automatically testing documentation for graph machine learning - Apr 16, 2020.
  
  In this article, we’ll walk through the detailed and helpful continuous integration (CI) that supports us in keeping StellarGraph’s demos current and informative.

  Graphs, Integration, Jupyter, Machine Learning, Python, Software Engineering

• Federated Learning: An Introduction - Apr 15, 2020.
  
  Improving machine learning models and making them more secure by training on decentralized data.

  Federated Learning, Learning, Machine Learning, Privacy, Security

• Can Java Be Used for Machine Learning and Data Science? - Apr 14, 2020.
  
  While Python and R have become favorites for building these programs, many organizations are turning to Java application development to meet their needs. Read on to see how, and why.

  Data Science, Java, Machine Learning, Programming Languages

• 10 Must-read Machine Learning Articles (March 2020) - Apr 9, 2020.
  
  This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.

  AI, API, Cloud, Data Analytics, Datasets, fast.ai, Machine Learning, Neural Networks, Social Media

• How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps - Apr 8, 2020.
  
  With your machine learning model in Python just working, it's time to optimize it for performance. Follow this guide to setup automated tuning using any optimization library in three steps.

  Hyperparameter, Machine Learning, Optimization, Python

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning - Apr 8, 2020.
  
  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?

  Machine Learning, Neural Networks, random forests algorithm

• 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency - Apr 7, 2020.
  
  Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.

  Efficiency, Machine Learning, Reinforcement Learning

• Introduction to the K-nearest Neighbour Algorithm Using Examples - Apr 1, 2020.
  
  Read this concise summary of KNN, a supervised and pattern classification learning algorithm which helps us find which class the new input belongs to when k nearest neighbours are chosen and distance is calculated between them.

  Algorithms, K-nearest neighbors, Machine Learning, Python, scikit-learn

• Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs - Apr 1, 2020.
  
  From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.

  Anomaly Detection, Graph, Machine Learning

• How (not) to use Machine Learning for time series forecasting: The sequel - Mar 30, 2020.
  
  Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.

  Forecasting, Machine Learning, Mistakes, Time Series

• Introduction to Kubeflow MPI Operator and Industry Adoption - Mar 27, 2020.
  
  Kubeflow just announced its first major 1.0 release recently. This post introduces the MPI Operator, one of the core components of Kubeflow, currently in alpha, which makes it easy to run synchronized, allreduce-style distributed training on Kubernetes.

  Cloud, Kubeflow, Kubernetes, Machine Learning

• Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU? - Mar 26, 2020.
  
  Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.

  Algorithms, Deep Learning, GPU, Machine Learning

• Making sense of ensemble learning techniques - Mar 26, 2020.
  
  This article breaks down ensemble learning and how it can be used for problem solving.

  Algorithms, Data Science, Ensemble Methods, Machine Learning

• Diffusion Map for Manifold Learning, Theory and Implementation - Mar 25, 2020.
  
  This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions.

  Data Preparation, Data Science, Dimensionality Reduction, Feature Engineering, Machine Learning

• Made With ML: Discover, build, and showcase machine learning projects - Mar 23, 2020.
  
  This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.

  GitHub, Kaggle, Machine Learning, Research

• Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models - Mar 23, 2020.
  
  TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures.

  Google, Machine Learning, Quantum Computing, TensorFlow

• A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - Mar 18, 2020.
  
  Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.

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

• Building a Mature Machine Learning Team - Mar 13, 2020.
  
  After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market.

  Data Science Team, Machine Learning, Team

• The Most Useful Machine Learning Tools of 2020 - Mar 13, 2020.
  
  This articles outlines 5 sets of tools every lazy full-stack data scientist should use.

  Applications, GitHub, Machine Learning, Postgres, PyCharm, Tools

• Decision Boundary for a Series of Machine Learning Models - Mar 13, 2020.
  
  I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions.

  Decision Boundaries, Machine Learning, Modeling, R

• Few-Shot Image Classification with Meta-Learning - Mar 12, 2020.
  
  Here is how you can teach your model to learn quickly from a few examples.

  Image Classification, Learning, Machine Learning

• Software Interfaces for Machine Learning Deployment - Mar 11, 2020.
  
  While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable.

  API, Deployment, Machine Learning, MLOps, Software Engineering

• 21 Machine Learning Projects – Datasets Included - Mar 9, 2020.
  
  Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today.

  Chatbot, Datasets, Google Trends, Machine Learning, Project, Uber

• A Crash Course in Game Theory for Machine Learning: Classic and New Ideas - Mar 9, 2020.
  
  Game theory is experiencing a renaissance driven by the evolution of AI. What are some classic and new ideas that data scientists should be aware of.

  Game Theory, Machine Learning

• Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2020.
  
  For the international women's day, we feature resources to help more women enter and succeed in AI, Big Data, Data Science, and Machine Learning fields.

  AI, Data Science, Diversity, Machine Learning, Women

• Phishytics – Machine Learning for Detecting Phishing Websites - Mar 6, 2020.
  
  Since phishing is such a widespread problem in the cybersecurity domain, let us take a look at the application of machine learning for phishing website detection.

  Cybersecurity, Machine Learning, Security

• Trends in Machine Learning in 2020 - Mar 5, 2020.
  
  Many industries realize the potential of Machine Learning and are incorporating it as a core technology. Progress and new applications of these tools are moving quickly in the field, and we discuss expected upcoming trends in Machine Learning for 2020.

  Machine Learning, Security, Trends

• A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
  
  Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.

  Classification, Classifier, Interpretability, Machine Learning, Metrics, ROC-AUC

• The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images - Mar 3, 2020.
  
  Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.

  Data Science, Data Scientist, Image Classification, Machine Learning

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2) - Mar 2, 2020.
  
  We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.

  AI, Data Science, Explainability, Geospatial, GPT-2, Key Terms, Machine Learning, Natural Language Generation, Reinforcement Learning, Transformer

• Uber Unveils a New Service for Backtesting Machine Learning Models at Scale - Mar 2, 2020.
  
  The transportation giant built a new service and architecture for backtesting forecasting models.

  Machine Learning, Scalability, Uber

• Decision Tree Intuition: From Concept to Application - Feb 27, 2020.
  
  While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.

  Beginners, Decision Trees, Machine Learning

• Free Mathematics Courses for Data Science & Machine Learning - Feb 25, 2020.
  
  It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.

  Courses, Data Science, Machine Learning, Mathematics, MOOC

• Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
  
  The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.

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

• Getting Started with R Programming - Feb 19, 2020.
  
  An end to end Data Analysis using R, the second most requested programming language in Data Science.

  Data Science, Machine Learning, Programming, R

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1) - Feb 18, 2020.
  
  2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.

  AI, Data Science, Key Terms, Machine Learning

• Using AI to Identify Wildlife in Camera Trap Images from the Serengeti - Feb 17, 2020.
  
  With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.

  Africa, AI, Computer Vision, Machine Learning

• Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything - Feb 17, 2020.
  
  Meena is one of the major milestones in the history of NLU. How did Google build it?

  Chat, Chatbot, Google, Machine Learning, NLU

• What Does it Mean to Deploy a Machine Learning Model? - Feb 14, 2020.
  
  You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.

  Deployment, Machine Learning, MLOps

• Adversarial Validation Overview - Feb 13, 2020.
  
  Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.

  Adversarial, Kaggle, Machine Learning, Python, Validation

• Practical Hyperparameter Optimization - Feb 13, 2020.
  
  An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.

  Automated Machine Learning, AutoML, Deep Learning, Hyperparameter, Machine Learning, Optimization, Python, scikit-learn

• Sharing your machine learning models through a common API - Feb 12, 2020.
  
  DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.

  API, Deep Learning, Machine Learning, Open Source, Python

• AI and Machine Learning In Our Every Day Life - Feb 7, 2020.
  
  The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.

  AI, Fraud Detection, Gmail, Machine Learning, Search, Social Media, Travel

• The Data Science Puzzle — 2020 Edition - Feb 7, 2020.
  
  The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here.

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

• Intro to Machine Learning and AI based on high school knowledge - Feb 5, 2020.
  
  Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.

  AI, Beginners, Linear Regression, Machine Learning, Mathematics

• Optimal Estimation Algorithms: Kalman and Particle Filters - Feb 5, 2020.
  
  An introduction to the Kalman and Particle Filters and their applications in fields such as Robotics and Reinforcement Learning.

  Kalman Filters, Machine Learning, Probability

• Serverless Machine Learning with R on Cloud Run - Feb 4, 2020.
  
  Expedite the deployment of your machine models using serverless cloud infrastructure. In this tutorial, we explore creating and deploying a model which scraps real time Twitter data and returns interactive visualization using R.

  Cloud, Machine Learning, R, Twitter

• Why are Machine Learning Projects so Hard to Manage? - Feb 3, 2020.
  
  What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.

  Deployment, Kaggle, Lukas Biewald, Machine Learning, Project Fail, Training Data

• Data Validation for Machine Learning - Jan 31, 2020.
  
  While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model.

  Cross-validation, Data Science, Machine Learning

• Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About - Jan 30, 2020.
  
  Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.

  Automated Machine Learning, AutoML, Deep Learning, Machine Learning

• Exoplanet Hunting Using Machine Learning - Jan 28, 2020.
  
  Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.

  Cosmology, Machine Learning, Python

• Artificial Intelligence Books to Read in 2020 - Jan 21, 2020.
  
  Here are some AI-related books that I’ve read and recommend for you to add to your 2020 reading list!

  AI, Books, Deep Learning, Machine Learning

• The Future of Machine Learning - Jan 17, 2020.
  
  This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.

  2020 Predictions, Computer Vision, Machine Learning, NLP, Transformer

• Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
  
  Which algorithm works best for unbalanced data? Are there any tradeoffs?

  Algorithms, Classification, Machine Learning, R, ROC-AUC, Unbalanced

• 7 AI Use Cases Transforming Live Sports Production and Distribution - Jan 14, 2020.
  
  Here are 7 powerful AI led use cases both for linear television and for OTT apps that are transforming the live sports production landscape.

  AI, Machine Learning, Sports, Use Cases

• Graph Machine Learning Meets UX: An uncharted love affair - Jan 13, 2020.
  
  When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.

  Data Science, Data Visualization, Design, Graph Analytics, Machine Learning, UI/UX

• The Book to Start You on Machine Learning - Jan 9, 2020.
  
  This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

  Books, Machine Learning

• Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models - Jan 7, 2020.
  
  There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.

  Ensemble Methods, Explainability, Machine Learning

• H2O Framework for Machine Learning - Jan 6, 2020.
  
  This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

  Automated Machine Learning, AutoML, H2O, Machine Learning, Python

• 10 Best and Free Machine Learning Courses, Online - Dec 26, 2019.
  
  Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

  Coursera, Data Science Education, Deep Learning, edX, Machine Learning, Online Education

• 5 Ways to Apply Ethics to AI - Dec 19, 2019.
  
  Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.

  Algorithms, Bias, Ethics, Goodhart’s Law, Machine Learning, Social Good

• The Ultimate Guide to Model Retraining - Dec 16, 2019.
  
  Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.

  Deployment, Machine Learning, Model Drift, Model Performance, Monitoring, Production, Training Data

• Microsoft Introduces Icebreaker to Address the Famous Ice-Start Challenge in Machine Learning - Dec 16, 2019.
  
  The new technique allows the deployment of machine learning models that operate with minimum training data.

  Data Preparation, Machine Learning, Microsoft

• AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020 - Dec 11, 2019.
  
  We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.

  2020 Predictions, AI, Analytics, Bill Schmarzo, Carla Gentry, Data Science, Doug Laney, Jen Underwood, Kate Strachnyi, Machine Learning, Meta Brown, Ronald van Loon, Tom Davenport, Trends

• Deployment of Machine learning models using Flask - Dec 10, 2019.
  
  This blog will explain the basics of deploying a machine learning algorithm, focusing on developing a Naïve Bayes model for spam message identification, and using Flask to create an API for that model.

  Deployment, Flask, Machine Learning

• Scalable graph machine learning: a mountain we can climb? - Dec 10, 2019.
  
  Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.

  Deep Learning, Graph Analytics, Graph Databases, Machine Learning, Scalability

• 5 Great New Features in Latest Scikit-learn Release - Dec 10, 2019.
  
  From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.

  Data Preparation, Data Preprocessing, Ensemble Methods, Feature Selection, Gradient Boosting, K-nearest neighbors, Machine Learning, Missing Values, Python, scikit-learn, Visualization

• Moving Predictive Maintenance from Theory to Practice - Dec 9, 2019.
  
  Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.

  Deployment, Machine Learning, MathWorks, MATLAB, Predictive Maintenance, Simulation

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
  
  As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.

  2020 Predictions, AI, Ajit Jaokar, Analytics, Andriy Burkov, Anima Anandkumar, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Research, Rosaria Silipo, Xavier Amatriain

• 10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
  
  Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.

  Books, Computer Vision, Courses, Deep Learning, Explainability, Graph Analytics, Interpretability, Machine Learning, NLP, Python

• Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
  
  While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.

  Agile, Andrew Ng, Comet.ml, Machine Learning, Software Engineering

• Artificial Friend or Virtual Foe - Dec 5, 2019.
  
  Is AI making more good than harm?

  AI, Machine Learning, Social Good, Sustainability

• A Doomed Marriage of Machine Learning and Agile - Nov 28, 2019.
  
  Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.

  Agile, Machine Learning, Udacity

• Machine Learning 101: The What, Why, and How of Weighting - Nov 26, 2019.
  
  Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.

  Accuracy, Balancing Classes, Machine Learning, Model Performance, Sports

• Neural Networks 201: All About Autoencoders - Nov 21, 2019.
  
  Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.

  Autoencoder, Machine Learning, Missing Values, Neural Networks

• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead - Nov 20, 2019.
  
  The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

  Interpretability, Machine Learning, Modeling

• GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
  
  Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.

  Automated Machine Learning, GitHub, Machine Learning, Movies, Python

• Testing Your Machine Learning Pipelines - Nov 14, 2019.
  
  Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.

  Machine Learning, Pipeline, Python

• Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
  
  While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.

  Accuracy, Deep Learning, Image Classification, Keras, Machine Learning, TensorFlow, Transfer Learning

• Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
  
  The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.

  Beginners, Classification, Machine Learning, Python, Regression, scikit-learn, Supervised Learning, Unsupervised Learning

• Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
  
  This guide explores research centered on a variety of advanced loss functions for machine learning models.

  Machine Learning, Research

• Top Machine Learning Software Tools for Developers - Nov 1, 2019.
  
  As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

  Developers, Machine Learning

• What is Machine Learning on Code? - Nov 1, 2019.
  
  Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.

  Machine Learning, Programming, Software

• How to Build Your Own Logistic Regression Model in Python - Oct 31, 2019.
  
  A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

  Logistic Regression, Machine Learning, Python

• Why is Machine Learning Deployment Hard? - Oct 29, 2019.
  
  Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

  Deployment, Machine Learning

• How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - Oct 29, 2019.
  
  In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

  Machine Learning, Python, scikit-learn, Software Engineering, Workflow

• How Bayes’ Theorem is Applied in Machine Learning - Oct 28, 2019.
  
  Learn how Bayes Theorem is in Machine Learning for classification and regression!

  Bayes Theorem, Machine Learning, Naive Bayes, Probability

• DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models - Oct 28, 2019.
  
  Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.

  Bayesian Networks, DeepMind, Machine Learning

• Feature Selection: Beyond feature importance? - Oct 24, 2019.
  
  In this post, you will see 3 different techniques of how to do Feature Selection to your datasets and how to build an effective predictive model.

  Feature Selection, Machine Learning

• Intro to Adversarial Machine Learning and Generative Adversarial Networks - Oct 23, 2019.
  
  In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.

  Adversarial, AI, GANs, Generative Adversarial Network, Machine Learning

• How to Easily Deploy Machine Learning Models Using Flask - Oct 17, 2019.
  
  This post aims to make you get started with putting your trained machine learning models into production using Flask API.

  Deployment, Flask, Machine Learning, Python

• The 5 Classification Evaluation Metrics Every Data Scientist Must Know - Oct 16, 2019.
  
  This post is about various evaluation metrics and how and when to use them.

  Data Scientist, Machine Learning, Metrics, Python

• Choosing a Machine Learning Model - Oct 14, 2019.
  
  Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.

  Interpretability, Kaggle, Machine Learning

• 8 Paths to Getting a Machine Learning Job Interview - Oct 10, 2019.
  
  While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job.

  Advice, Career, Jobs, Machine Learning

• Data Preparation for Machine learning 101: Why it’s important and how to do it - Oct 2, 2019.
  
  As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.

  Data Preparation, Data Science, Machine Learning

• What is Hierarchical Clustering? - Sep 27, 2019.
  
  The article contains a brief introduction to various concepts related to Hierarchical clustering algorithm.

  Clustering, Machine Learning, Python

• Data Mapping Using Machine Learning - Sep 27, 2019.
  
  Data mapping is a way to organize various bits of data into a manageable and easy-to-understand system.

  Data Cleaning, Data Preparation, Machine Learning

• Automate Hyperparameter Tuning for Your Models - Sep 20, 2019.
  
  When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?

  Automated Machine Learning, Hyperparameter, Machine Learning, Modeling

• Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning - Sep 19, 2019.
  
  While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.

  Dataset, Machine Learning, scikit-learn, Synthetic Data

• Applying Data Science to Cybersecurity Network Attacks & Events - Sep 19, 2019.
  
  Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.

  Cybersecurity, Data Science, Machine Learning, Python, Security

• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Sep 19, 2019.
  
  “I want to learn machine learning and artificial intelligence, where do I start?” Here.

  Beginners, Data Science, Machine Learning, Python

• Which Data Science Skills are core and which are hot/emerging ones? - Sep 17, 2019.
  
  We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

  Career, Data Science Skills, Data Visualization, Deep Learning, Excel, Machine Learning, Poll, Python, PyTorch, Scala, Skills, Statistics, TensorFlow

• Explore the world of Bioinformatics with Machine Learning - Sep 17, 2019.
  
  The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.

  Bioinformatics, Machine Learning, Python

• Cartoon: Unsupervised Machine Learning? - Sep 14, 2019.
  
  New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?"

  Cartoon, Humor, Machine Learning, Unsupervised Learning, Yann LeCun

• Many Heads Are Better Than One: The Case For Ensemble Learning - Sep 13, 2019.
  
  While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.

  Bagging, Boosting, Ensemble Methods, Machine Learning, XGBoost

• Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
  
  I am a Git god, why do I need another version control system for Machine Learning Projects?

  Data Science, Datasets, Machine Learning, Modeling, Version Control

• There is No Free Lunch in Data Science - Sep 12, 2019.
  
  There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

  Algorithms, Data Science, Machine Learning, Optimization

• Ensemble Methods for Machine Learning: AdaBoost - Sep 12, 2019.
  
  It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier.

  Adaboost, Ensemble Methods, Machine Learning, Python

• 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