Tag: Machine Learning (1340)

• 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!

  Tags: 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.

  Tags: 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?

  Tags: Algorithms, Classification, Machine Learning, R, Unbalanced

• KDnuggets™ News 20:n02, Jan 15: Top 5 Must-have Data Science Skills; Learn Machine Learning with THIS Book - Jan 15, 2020.
  
  This week: learn the 5 must-have data science skills for the new year; find out which book is THE book to get started learning machine learning; pick up some Python tips and tricks; learn SQL, but learn it the hard way; and find an introductory guide to learning common NLP techniques.

  Tags: Books, Data Science, Data Science Skills, Machine Learning, NLP, Programming, Python, SQL, Tips

• 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: Ensemble Methods, Explainability, Machine Learning

• Live Webinar: Learn how to build better machine learning pipelines - Jan 6, 2020.
  
  In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.

  Tags: cnvrg.io, Machine Learning, MLOps, Pipeline, Webinar

• 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.

  Tags: 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.

  Tags: 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.

  Tags: Algorithms, Bias, Ethics, 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.

  Tags: 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.

  Tags: Data Preparation, Machine Learning, Microsoft

• KDnuggets Poll: How well do current AutoML solutions work? - Dec 14, 2019.
  
  Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions.

  Tags: Automated Machine Learning, AutoML, Machine Learning, Poll

• Dusting Under the Bed: Machine Learners’ Responsibility for the Future of Our Society - Dec 13, 2019.
  
  The Machine Learning community must shape the world so that AI is built and implemented with a focus on the entire outcome for our society, and not just optimized for accuracy and/or profit.

  Tags: Algorithms, Bias, Ethics, Machine Learning, Social Good

• 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.

  Tags: 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

• KDnuggets™ News 19:n47, Dec 11: 10 Free Top Notch Machine Learning Courses; AI, Analytics, ML, DS Main Developments and Key Trends - Dec 11, 2019.
  
  We asked top experts: What were the main developments in AI, Data Science, Deep Learning, and Machine Learning Research in 2019, and what key trends do you expect in 2020? Read their answers, and also check 10 Free Top Notch Machine Learning Courses; 4 Hottest Trends in Data Science; The Essential Toolbox for Data Cleaning, and more

  Tags: 2020 Predictions, Courses, Data Cleaning, Machine Learning

• 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

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

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

  Tags: AI, Machine Learning, Social Good, Sustainability

• Two Years In The Life of AI, Machine Learning, Deep Learning and Java - Nov 29, 2019.
  
  Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.

  Tags: AI, Career, Deep Learning, Java, Machine Learning

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

  Tags: Agile, Machine Learning, Udacity

• KDnuggets™ News 19:n45, Nov 27: Interpretable vs black box models; Advice for New and Junior Data Scientists - Nov 27, 2019.
  
  This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification

  Tags: Advice, Attention, Data Scientist, Machine Learning, Modeling, Neural Networks, NLP, Programming, Python, Text Classification

• 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.

  Tags: 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.

  Tags: 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.

  Tags: Interpretability, Machine Learning, Modeling

• How to apply machine learning and deep learning methods to audio analysis - Nov 19, 2019.
  
  Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.

  Tags: Audio, Comet.ml, Machine Learning, Speech Recognition

• 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.

  Tags: Automated Machine Learning, GitHub, Machine Learning, Movies, Python

• Tips for a cost-effective machine learning project - Nov 15, 2019.
  
  Spoiler: you don’t need a VM running 24/7 to handle 16 requests a day.

  Tags: Machine Learning, Tips

• 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.

  Tags: 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.

  Tags: 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.

  Tags: Beginner, Classification, Machine Learning, Python, Regression, scikit-learn, Supervised Learning, Unsupervised Learning

• How I Got Better at Machine Learning - Nov 13, 2019.
  
  Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.

  Tags: Advice, Machine Learning, Tips

• MLOps for production-level machine learning [Nov 14 Webinar] - Nov 12, 2019.
  
  This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

  Tags: cnvrg.io, Deployment, DevOps, Machine Learning, MLOps

• 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.

  Tags: Machine Learning, Research

• The Last Defense Against Another AI Winter - Nov 6, 2019.
  
  My short answer is this: Yes, another AI Winter will be here if you don’t deploy more ML solutions. You and your Data Science teams are the last line of defense against the AI Winter. You need to solve five key challenges to keep the momentum up.

  Tags: AI, Machine Learning

• 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.

  Tags: Developers, Machine Learning

• MLOps for production-level machine learning - Nov 1, 2019.
  
  This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

  Tags: cnvrg.io, Deployment, DevOps, Machine Learning, MLOps

• 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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!

  Tags: 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.

  Tags: 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.

  Tags: 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.

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

• Building an intelligent Digital Assistant - Oct 18, 2019.
  
  In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.

  Tags: AI, Deep Learning, Machine Learning, Optimization

• 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.

  Tags: 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.

  Tags: Data Scientist, Machine Learning, Metrics, Python

• KDnuggets™ News 19:n39, Oct 16: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI - Oct 16, 2019.
  
  This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

  Tags: AI, Data Science, Deep Learning, Interview, Machine Learning, Metrics, NLP, Word Embeddings

• 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.

  Tags: Interpretability, Kaggle, Machine Learning

• Upcoming Webinar, Machine Learning Vital Signs: Metrics and Monitoring Models in Production - Oct 11, 2019.
  
  In this upcoming webinar on Oct 23 @ 10 AM PT, learn why you should invest time in monitoring your machine learning models, the dangers of not paying attention to how a model’s performance can change over time, metrics you should be gathering for each model and what they tell you, and much more.

  Tags: Domino, Machine Learning, Metrics, Monitoring, Production

• There is No Such Thing as a Free Lunch - Oct 11, 2019.
  
  You have heard the expression “there is no such thing as a free lunch” – well in machine learning the same principle holds. In fact there is even a theorem with the same name.

  Tags: AI, Deep Learning, Machine Learning, Optimization

• 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.

  Tags: Advice, Career, Jobs, Machine Learning

• Data Science is Boring (Part 2) - Oct 9, 2019.
  
  Why I love boring ML problems and how I think about them.

  Tags: Career Advice, Data Science, Machine Learning

• Training a Machine Learning Engineer - Oct 3, 2019.
  
  There is no clear outline on how to study Machine Learning/Deep Learning due to which many individuals apply all the possible algorithms that they have heard of and hope that one of implemented algorithms work for their problem in hand. Below, I've listed out some of the steps that one should adopt while solving a machine learning problem.

  Tags: Architecture, Datasets, Machine Learning, Machine Learning Engineer

• 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.

  Tags: Data Preparation, Data Science, Machine Learning

• Will Machine Learning End Retail? Data Science Seattle Oct 17, 2019 - Sep 30, 2019.
  
  In advance of the Data Science Salon taking place in Seattle on Oct 17, we asked our speakers to shed some light on how Artificial Intelligence and Machine Learning are impacting one of America’s most disruptive industries. Read for more insight, and then register with KDnuggets exclusive link for 20% off tickets.

  Tags: Data Science, Formulated, Machine Learning, Retail, Salon, Seattle

• Webinar: Build auto-adaptive machine learning models with Kubernetes - Sep 27, 2019.
  
  This live webinar, Oct 2 2019, will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Save your spot now.

  Tags: AutoML, cnvrg.io, Kubernetes, Machine Learning

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

  Tags: 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.

  Tags: Data Cleaning, Data Preparation, Machine Learning

• Beyond Explainability: A Practical Guide to Managing Risks in Machine Learning Models - Sep 20, 2019.
  
  This white paper provides the first-ever standard for managing risk in AI and ML, focusing on both practical processes and technical best practices “beyond explainability” alone. Download now.

  Tags: Explainability, Immuta, Machine Learning, Privacy, Risks, White Paper

• 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?

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: Beginners, Data Science, Machine Learning, Python

• Data Science is Boring (Part 1) - Sep 18, 2019.
  
  Read about how one data scientist copes with his boring days of deploying machine learning.

  Tags: Career Advice, Data Science, Machine Learning

• 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.

  Tags: 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.

  Tags: 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?"

  Tags: 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.

  Tags: 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?

  Tags: 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.

  Tags: 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.

  Tags: 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.

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

• Classification vs Prediction - Sep 12, 2019.
  
  It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.

  Tags: Advice, Data Science, Data Scientist, Machine Learning

• 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.)

  Tags: Bias, Data Science, Data Scientist, Machine Learning

• The Death of Centralized AI and the Rise of Open AI - Aug 29, 2019.
  
  Centralized AI is giving way to more democratic AI systems, which are becoming more and more accessible to data scientists, both through code and through open ecosystems.

  Tags: AI, Machine Learning, Privacy

• 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.

  Tags: 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.

  Tags: 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.

  Tags: 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.

  Tags: Cancer Detection, Healthcare, Machine Learning, Medical

• U. of Miami: Faculty Positions, with expertise in AI/Data Science/ML or related areas [Miami, FL] - Aug 15, 2019.
  
  The positions require research and teaching expertise in AI/Data Science, or related areas including Data Extraction, Data Visualization, Machine Learning, and Intelligent Actuators.

  Tags: AI, Faculty, FL, Machine Learning, Miami, University of Miami

• 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.

  Tags: Advice, Data Science, Machine Learning, Statistics

• PhD student position in computational science with focus on chemistry [Umeå, Sweden] - Aug 13, 2019.
  
  Umea University, Sweden is seeking a PhD-student in computational science with focus on chemistry. The position is for 4 years of research including courses on graduate level.

  Tags: Chemistry, Machine Learning, PhD position, Sweden, Umea University

• 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.

  Tags: 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.

  Tags: 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.

  Tags: Algorithms, Collinearity, Machine Learning, R, Random Forests

• [video] Introduction to Generative Adversarial Networks (for beginners and advanced Data Scientists) - Aug 5, 2019.
  
  Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.

  Tags: Beginners, GANs, Machine Learning, Youtube

• 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.

  Tags: Machine Learning, Report, Survey

• GPU Accelerated Data Analytics & Machine Learning - Aug 2, 2019.
  
  The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support.

  Tags: Analytics, GPU, Machine Learning, Python

• 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.

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

• A Data Science Playbook for explainable ML/xAI - Jul 30, 2019.
  
  This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.

  Tags: Data Science, Domino, Explainable AI, Interpretability, Machine Learning, 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.

  Tags: 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.

  Tags: 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?

  Tags: AI, Cloudfactory, 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.

  Tags: 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.

  Tags: 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.

  Tags: Bayesian, Machine Learning, Quantum Computing

• Online Workshop: How to set up Kubernetes for all your machine learning workflows - Jul 17, 2019.
  
  Join this free live online workshop, Jul 31 @12 PM ET, to learn how to set up your Kubernetes cluster, so you can run Spark, TensorFlow, and any ML framework instantly, touching on the entire machine learning pipeline from model training to model deployment.

  Tags: Apache Spark, cnvrg.io, Kubernetes, Machine Learning, TensorFlow

• 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.

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

• KDnuggets™ News 19:n25, Jul 10: 5 Probability Distributions for Data Scientists; What the Machine Learning Engineer Job is Really Like - Jul 10, 2019.
  
  This edition of the KDnuggets newsletter is double-sized after taking the holiday week off. Learn about probability distributions every data scientist should know, what the machine learning engineering job is like, making the most money with the least amount of risk, the difference between NLP and NLU, get a take on Nvidia's new data science workstation, and much, much more.

  Tags: Data Science, Data Scientist, Distribution, Machine Learning, Machine Learning Engineer, NLP, NVIDIA, Probability, Risk Modeling

• Math for Machine Learning - Jul 9, 2019.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  Tags: Book, ebook, Machine Learning, Mathematics, Richard Han

• 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

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

• Why do we need AWS SageMaker? - Jun 26, 2019.
  
  Today, there are several platforms available in the industry that aid software developers, data scientists as well as a layman in developing and deploying machine learning models within no time.

  Tags: AWS, Cloud Computing, Data Science, Deep Learning, Jupyter, Machine Learning, Sagemaker

• KDnuggets™ News 19:n24, Jun 26: Understand Cloud Services; Pandas Tips & Tricks; Master Data Preparation w/ Python - Jun 26, 2019.
  
  Happy summer! This week on KDnuggets: Understanding Cloud Data Services; How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat; 7 Steps to Mastering Data Preparation for Machine Learning with Python; Examining the Transformer Architecture: The OpenAI GPT-2 Controversy; Data Literacy: Using the Socratic Method; and much more!

  Tags: Cloud, Data Preparation, Machine Learning, NLP, OpenAI, Pandas, 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.

  Tags: 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.

  Tags: 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.

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

• KDnuggets™ News 19:n23, Jun 19: Useful Stats for Data Scientists; Python, TensorFlow & R Winners in Latest Job Report - Jun 19, 2019.
  
  This week on KDnuggets: 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; How to Learn Python for Data Science the Right Way; The Machine Learning Puzzle, Explained; Scalable Python Code with Pandas UDFs; and much more!

  Tags: Data Science, Data Scientist, Machine Learning, Pandas, Python, R, Report, SAS, Scalability, Statistics, TensorFlow

• 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.

  Tags: 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?

  Tags: 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.

  Tags: Deployment, Jupyter, Machine Learning, Production, Training Data