- 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.
- 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?"
- 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.
- 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?
- 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.
- 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.
- A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.)
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- [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.
- 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.
- GPU Accelerated Data Analytics & Machine Learning - Aug 2, 2019.
The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support.
- 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.
- 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.
- 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.
- 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.
- 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?
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Math for Machine Learning - Jul 9, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- 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.
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- 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.
- 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!
- 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.
- 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.
- 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.
- 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!
- 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.
- 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?
- 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.
- 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.
- 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.
- 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.
- 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.
- 5 Ways to Deal with the Lack of Data in Machine Learning - Jun 10, 2019.
Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.
- 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.
- 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.
- Math for Machine Learning. - Jun 5, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- 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.
- 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.
- 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!
- 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?
- 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.
- 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.
- 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.
- 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.
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- 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.
- 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.
- 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.
- 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.
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- 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.
- 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
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- “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.
- [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.
- 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.
- 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.
- 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.
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- 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!
- 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.
- 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.
- How to correctly select a sample from a huge dataset in machine learning - May 1, 2019.
We explain how choosing a small, representative dataset from a large population can improve model training reliability.
- 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.
- 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!
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- 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.
- 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.
- Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
- An introduction to explainable AI, and why we need it - Apr 15, 2019.
We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.
- Avoiding Obvious Insights Using Analyze With Insight Miner - Apr 12, 2019.
Analyze with Insight Miner delivers value for every business user with machine learning. Learn how it was created from Sisense Data Scientist, Ayelet Arditi.
- How can quantum computing be useful for Machine Learning - Apr 12, 2019.
We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.
- All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.
- Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
- Advance Your Data and Analytics Skills, Your Way - Apr 8, 2019.
Find the topics and learning style that resonate with you and your team! Join us for essential training in analytics, data management, business intelligence, machine learning, and more. Save 20% on TDWI seminars with code KD20.
- From Business Intelligence to Machine Intelligence - Apr 5, 2019.
This webinar, Apr 18 @ 1 PM ET, will help listeners understand both the opportunities and limits of AI for decision making. It will underscore the importance of applying appropriate governance and controls to analytic models and use cases.
- Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
- Yeshiva University: Tenure-track Faculty in AI and Machine Learning (Open Rank) [New York, NY] - Apr 2, 2019.
The Katz School of Science and Health at Yeshiva University invites applications for tenure-track faculty in Artificial Intelligence, Machine Learning and Computer Science for its graduate programs.
- Yeshiva University: Program Director / Tenure Track Faculty Member of Artificial Intelligence and Machine Learning [New York, NY] - Apr 2, 2019.
The Katz School of Science and Health at Yeshiva University seeks a dynamic leader to serve as academic and administrative head of its graduate initiatives in Artificial Intelligence and Machine Learning. This is a tenure eligible position depending on experience and qualifications.
- Uber’s Case Study at PAW Industry 4.0: Machine Learning to Enforce Mobile Performance - Apr 1, 2019.
Data scientists, industrial planners, and other machine learning experts will meet at PAW in Las Vegas on June 16-20, 2019 to explore the latest trends and technologies in machine & deep learning for the IoT era.
- Explaining Random Forest® (with Python Implementation) - Mar 29, 2019.
We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.
- Interpolation in Autoencoders via an Adversarial Regularizer - Mar 29, 2019.
Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.
- Top KDnuggets tweets, Mar 20-26: 10 More Free Must-Read Books for Machine Learning and Data Science - Mar 27, 2019.
Also - 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition; 10 Free Must-See Courses for Machine Learning and Data Science; How to Train a Keras Model 20x Faster with a TPU for Free.
- My Best Tips for Agile Data Science Research - Mar 21, 2019.
This post demonstrates how to bring maximum value in minimal time using agile methods in data science research.
- KDnuggets™ News 19:n11, Mar 20: Another 10 Free Must-Read Books for Data Science; 19 Inspiring Women in AI, Big Data, Machine Learning - Mar 20, 2019.
Also: Who is a typical Data Scientist in 2019?; The Pareto Principle for Data Scientists; My favorite mind-blowing Machine Learning/AI breakthroughs; Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision; Advanced Keras - Accurately Resuming a Training Process
- Mastering Fast Gradient Boosting on Google Colaboratory with free GPU - Mar 19, 2019.
CatBoost is a fast implementation of GBDT with GPU support out-of-the-box. Google Colaboratory is a very useful tool with free GPU support.
- Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.
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- [eBook] Standardizing the Machine Learning Lifecycle - Mar 15, 2019.
We explore what makes the machine learning lifecycle so challenging compared to regular software, and share the Databricks approach.
- Top R Packages for Data Cleaning - Mar 15, 2019.
Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.
- My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
- AI: Arms Race 2.0 - Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
- Monash: Research Fellow [Clayton, Australia] - Mar 9, 2019.
The Data Science and AI group is seeking a go-getter Research Fellow to work at the interface of computer science, machine learning and medical research. Apply by April 4, 2019.
- Automated Machine Learning 101: Is Your Company Ready? - Mar 8, 2019.
In this webinar from DataRobot, learn common automated machine learning use cases how automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive, and more!
- Beating the Bookies with Machine Learning - Mar 8, 2019.
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
- 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Mar 7, 2019.
CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.
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