Machine Learning (1942)

• 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.

  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.

  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.

  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.

  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

• 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.

  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.

  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

• 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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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

• 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.

  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?

  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

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

  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.

  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

• 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.

  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.

  Graph Analytics, Machine Learning, Social Network Analysis, Twitter

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

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

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

  Exxact, Machine Learning, R, scikit-learn

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

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

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

  Geospatial, Machine Learning, Object Detection, Python

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

  Machine Learning, NLP, Python, Speech Recognition

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

  Andrew Ng, Career, Machine Learning, Research

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

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

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

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

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

  Advice, Machine Learning, Training Data

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

  Data Scientist, Machine Learning, Programming, Python

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

  Bias, Data Science, Data Scientist, Machine Learning

• 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.

  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.

  AI, Explainability, Machine Learning, Modeling

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

  AI, Deep Learning, Machine Learning

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

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

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

  Cancer Detection, Healthcare, Machine Learning, Medical

• 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.

  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.

  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.

  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.

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

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

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

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

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

• [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.

  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.

  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.

  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.

  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.

  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.

  AI, Analytics, Data Science, Machine Learning, Podcast

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

  AI, Blockchain, Machine Learning, Microsoft, Transparency

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

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

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

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

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

  Bias, Data Science, Machine Learning

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

  Bayesian, Machine Learning, Quantum Computing

• 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.

  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.

  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.

  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.

  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

  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.

  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!

  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.

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

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

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

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

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

• 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!

  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.

  Algorithms, Explained, Machine Learning, Modeling

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

  Adversarial, Machine Learning, Safety, Security

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

  Deployment, Jupyter, Machine Learning, Production, Training Data

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

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

• KDnuggets™ News 19:n22, Jun 12: The Modern Open-Source Data Science/Machine Learning Ecosystem; Simplifying the Data Visualisation Process in Python - Jun 12, 2019.
  
  The 6 tools in the modern open-source Data Science ecosystem; Simplifying the Data Visualisation Process in Python; The Infinity Stones of Data Science; Best resources for developers transitioning into data science.

  Data Science Platform, Data Visualization, Machine Learning, Neural Networks, random forests algorithm

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

  Decision Trees, Linear Regression, Machine Learning, Naive Bayes

• The Data Fabric for Machine Learning Part 1-b – Deep Learning on Graphs - Jun 11, 2019.
  
  Deep learning on graphs is taking more importance by the day. Here I’ll show the basics of thinking about machine learning and deep learning on graphs with the library Spektral and the platform MatrixDS.

  Advice, Data Science, Data Scientist, Machine Learning

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

  Cost Function, Machine Learning

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

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

• Math for Machine Learning. - Jun 5, 2019.
  
  This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.

  Book, ebook, Machine Learning, Mathematics, Richard Han

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

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

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

  Boosting, Gradient Boosting, Machine Learning, XGBoost

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

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

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

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

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

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

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

  AI, Deployment, Failure, Machine Learning, Scalability

• DMIR Research Group at the University of Wurzburg: Postdoctoral Researcher in Machine Learning for Time Series Analysis [Wurzburg, Germany] - May 28, 2019.
  
  The DMIR Research Group at the University of Würzburg offers a habilitation position for a postdoctoral researcher in the area of machine learning for temporal data.

  Germany, Machine Learning, Postdoc, Researcher, Time Series, University of Wurzburg, Wurzburg

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

  Pages: 1 2

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

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

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

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

  Data Preparation, Image Processing, Machine Learning

• Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks - May 22, 2019.
  
  We explore Geoffrey Hinton's capsule networks to deal with rotational variance in images.

  Capsule Networks, Computer Vision, Geoff Hinton, Machine Learning

• Extracting Knowledge from Knowledge Graphs Using Facebook’s Pytorch-BigGraph - May 22, 2019.
  
  We are using the state-of-the-art Deep Learning tools to build a model for predict a word using the surrounding words as labels.

  Pages: 1 2

  Deep Learning, Facebook, Machine Learning, NLP, Python, PyTorch, word2vec

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

  Machine Learning, Model Performance, Physics

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

  Advice, Data Science, Data Scientist, Machine Learning

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

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

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

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

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

  Azure ML, Machine Learning, Microsoft Azure, Recommender Systems

• KDnuggets™ News 19:n19, May 15: Data Scientist – Best Job of the Year!; How (not) to use Machine Learning for time series forecasting - May 15, 2019.
  
  "Please, explain." Interpretability of machine learning models; How to fix an Unbalanced Dataset; Data Science Poem; Customer Churn Prediction Using Machine Learning; A Complete Exploratory Data Analysis and Visualization for Text

  Churn, Data Exploration, Data Science, Data Scientist, Interpretability, Machine Learning, Text Analytics, Time Series, Unbalanced

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

  Altexsoft, Churn, Customer Analytics, Machine Learning

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

  Agriculture, AI, Data Science, Machine Learning, Sciforce

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

  Forecasting, Machine Learning, Mistakes, Stationarity, Time Series

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

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

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

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

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

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

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

  Balancing Classes, Data Preparation, Machine Learning, Unbalanced

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

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

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

  Pages: 1 2

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

• Unleash Big Data by SaaS-based End-to-End AutoML - May 6, 2019.
  
  This SaaS-based end-to-end AutoML tool R2 Learn enables data scientists, developers and data analysts to increase productivity, reduce errors and build quality models. Try for Free today!

  AutoML, Machine Learning, R2.ai, SaaS

• Strata SF day 2 Highlights: AI and Politics, Chatbots Insights, Forecasting Uncertainty, Scalable Video Analysis, and more - May 3, 2019.
  
  AI influencing Politics, insights from Chatbots, Enterprise Data Cloud, handling Video Big Data, and more takeaways from Strata Data Conference 2019, San Francisco.

  AI, Big Data, Chatbot, Machine Learning, San Francisco

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

  Decision Trees, Gradient Boosting, Machine Learning, XGBoost

• KDnuggets™ News 19:n17, May 1: The most desired skill in data science; Seeking KDnuggets Editors, work remotely - May 1, 2019.
  
  This week, find out about the most desired skill in data science, learn which projects to include in your portfolio, identify a single strategy for pulling data from a Pandas DataFrame (once and for all), read the results of our Top Data Science and Machine Learning Methods poll, and much more.

  Algorithms, Data Science, Generative Adversarial Network, Machine Learning, Pandas, Portfolio, Python, Recurrent Neural Networks

• Normalization vs Standardization — Quantitative analysis - Apr 30, 2019.
  
  Stop using StandardScaler from Sklearn as a default feature scaling method can get you a boost of 7% in accuracy, even when you hyperparameters are tuned!

  Pages: 1 2

  Data Preprocessing, Data Science, Feature Engineering, Machine Learning, Normalization, Python, Standardization

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

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

• Monash University: Lecturer/Senior Lecturer (Machine Learning and AI) [Melbourne, Australia] - Apr 28, 2019.
  
  The world-class Data Science and AI Group within the Faculty of Information Technology at Monash University, Clayton is expanding further and is seeking multiple Lecturers / Senior Lecturers in Machine Learning and AI.

  Australia, Lecturer, Machine Learning, Melbourne, Monash University