- 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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- Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
- KDnuggets™ News 19:n10, Mar 6: What no one will tell you about data science job applications; The rise of ML Engineering - Mar 6, 2019.
Also most impactful AI trends of 2018: The rise of ML Engineering; How to do Everything in Computer Vision; GANs Need Some Attention, Too; OpenAI GPT-2.
- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
- Most impactful AI trends of 2018: The rise of ML Engineering - Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
- [Webinar] Managing the Complete Machine Learning Lifecycle - Feb 28, 2019.
Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now.
- Join the future of AI and Data at DATAx San Francisco this May with Microsoft, Google and so many more - Feb 27, 2019.
Join us as we bring you the leading innovations and insights to the fast-paced world of AI & Data from Machine Learning, Healthcare, Marketing, Gaming analytics.
- Acquiring Labeled Data to Train Your Models at Low Costs - Feb 27, 2019.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
- 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
- Where did you apply Analytics, Data Science, Machine Learning in 2018? - Feb 25, 2019.
Where did you apply Analytics, Machine Learning, and Data Science in 2018? Take part in the latest KDnuggets poll to share your input, and see what others have to say.
- What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
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- State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
- The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
- Accelerating Time Series Analysis with Automated Machine Learning - Feb 14, 2019.
This IDC Solution Spotlight examines how automated machine learning tools can augment the analysis, modeling, and prediction of time series data to deliver easily understood and actionable insights for businesses in a simple and agile fashion. Get the report now.
- An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
- KDnuggets™ News 19:n07, Feb 13: The Best and Worst Data Visualizations of 2018; Gartner 2019 Magic Quadrant for Data Science Platforms - Feb 13, 2019.
Also: Data-science? Agile? Cycles?; How I used NLP (Spacy) to screen Data Science Resumes; Neural Networks - an Intuition; A Quick Guide to Feature Engineering; Understanding Gradient Boosting Machines
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
- How to Adopt Machine Learning: Interviews with Technical & Business Leaders - Feb 11, 2019.
This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.
- QCon.ai San Francisco: Applied AI Software Conference for Developers – KDnuggets Offer - Feb 8, 2019.
QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.
- 10 Trending Data Science Topics at ODSC East 2019 - Feb 7, 2019.
ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.
- Neural Networks – an Intuition - Feb 7, 2019.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
- The Essential Data Science Venn Diagram - Feb 4, 2019.
A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.
- Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
- KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning - Jan 30, 2019.
Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
- 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
- Machine Learning Security - Jan 25, 2019.
We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy - Jan 23, 2019.
We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.
- Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
- What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
- The Hundred-Page Machine Learning Book - Jan 17, 2019.
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples - Jan 17, 2019.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
- How to build an API for a machine learning model in 5 minutes using Flask - Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
- KDnuggets™ News 19:n03, Jan 16: Top 10 Books on NLP and Text Analysis; End To End Guide For Machine Learning Projects - Jan 16, 2019.
Also: Why Vegetarians Miss Fewer Flights - Five Bizarre Insights from Data; 4 Myths of Big Data and 4 Ways to Improve with Deep Data; The Role of the Data Engineer is Changing; How to solve 90% of NLP problems: a step-by-step guide
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
- End To End Guide For Machine Learning Projects - Jan 14, 2019.
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data - Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup - Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- Top KDnuggets tweets, Jan 02-08: 10 Free Must-Read Books for Machine Learning and Data Science - Jan 9, 2019.
Also: Papers with Code: A Fantastic GitHub Resource; Most Recommended #DataScience and #MachineLearning Books by Top MS programs;10 More Free Must-Read Books for ML and DS
- [Webinar] Accelerating Machine Learning on Databricks - Jan 9, 2019.
In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.