- Adventures in MLOps with Github Actions, Iterative.ai, Label Studio and NBDEV - Sep 16, 2021.
This article documents the authors' experience building their custom MLOps approach.
- The Machine & Deep Learning Compendium Open Book - Sep 16, 2021.
After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.
- Introduction to Automated Machine Learning - Sep 15, 2021.
AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.
- Top 18 Low-Code and No-Code Machine Learning Platforms - Sep 8, 2021.
Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.
- Math 2.0: The Fundamental Importance of Machine Learning - Sep 8, 2021.
Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.
- KDnuggets™ News 21:n34, Sep 8: Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained - Sep 8, 2021.
Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained; Data Science Cheat Sheet 2.0; 6 Cool Python Libraries That I Came Across Recently; Best Resources to Learn Natural Language Processing in 2021
- How Machine Learning Leverages Linear Algebra to Solve Data Problems - Sep 7, 2021.
Why you should learn the fundamentals of linear algebra.
- Fast AutoML with FLAML + Ray Tune - Sep 6, 2021.
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster.
- Antifragility and Machine Learning - Sep 6, 2021.
Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?
- 6 Cool Python Libraries That I Came Across Recently - Sep 3, 2021.
Check out these awesome Python libraries for Machine Learning.
- How to solve machine learning problems in the real world - Sep 2, 2021.
Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.
- How is Machine Learning Beneficial in Mobile App Development? - Sep 1, 2021.
Mobile app developers have a lot to gain by implementing AI & Machine Learning from the revolutionary changes that these disruptive technologies can offer. This is due to AI and ML's potential to strengthen mobile applications, providing for smoother user experiences capable of leveraging powerful features.
- The Significance of Data-centric AI - Aug 27, 2021.
How a systematic way of maintaining data quality can do wonders to your model performance.
- Automated Data Labeling with Machine Learning - Aug 26, 2021.
Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.
- Learning Data Science and Machine Learning: First Steps After The Roadmap - Aug 24, 2021.
Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
- Enhancing Machine Learning Personalization through Variety - Aug 19, 2021.
Personalization drives growth and is a touchstone of good customer experience. Personalization driven through machine learning can enable companies to improve this experience while improving ROI for marketing campaigns. However, challenges exist in these techniques for when personalization makes sense and how and when specific options are recommended.
- Model Drift in Machine Learning – How To Handle It In Big Data - Aug 17, 2021.
Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?
- Agile Data Labeling: What it is and why you need it - Aug 16, 2021.
The notion of Agile in software development has made waves across industries with its revolution for productivity. Can the same benefits be applied to the often arduous task of annotating data sets for machine learning?
- Introduction to Statistical Learning Second Edition - Aug 13, 2021.
The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.
- MLOps And Machine Learning Roadmap - Aug 12, 2021.
A 16–20 week roadmap to review machine learning and learn MLOps.
- How to Detect and Overcome Model Drift in MLOps - Aug 12, 2021.
This article has a look at model drift, and how to detect and overcome it in production MLOps.
- 2021 State of Production Machine Learning Survey - Aug 11, 2021.
We invite you to take the 2021 State of Production Machine Learning survey and help shed light on the latest trends in the adoption of machine learning (ML) in the industry.
- Visualizing Bias-Variance - Aug 10, 2021.
In this article, we'll explore some different perspectives of what the bias-variance trade-off really means with the help of visualizations.
- Artificial Intelligence vs Machine Learning in Cybersecurity - Aug 5, 2021.
Artificial Intelligence and Machine Learning are the next-gen technology used in various fields. With the rise in online threats, it has become essential to include these technologies in cybersecurity. In this post, we will know what roles do AI and ML play in cybersecurity.
- Mastering Clustering with a Segmentation Problem - Aug 3, 2021.
The one stop shop for implementing the most widely used models in Python for unsupervised clustering.
- 30 Most Asked Machine Learning Questions Answered - Aug 3, 2021.
There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.
- 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Aug 2, 2021.
While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.
- 10 Machine Learning Model Training Mistakes - Jul 30, 2021.
These common ML model training mistakes are easy to overlook but costly to redeem.
- Building Machine Learning Pipelines using Snowflake and Dask - Jul 28, 2021.
In this post, I want to share some of the tools that I have been exploring recently and show you how I use them and how they helped improve the efficiency of my workflow. The two I will talk about in particular are Snowflake and Dask. Two very different tools but ones that complement each other well especially as part of the ML Lifecycle.
- KDnuggets™ News 21:n28, Jul 28: Design patterns in machine learning; The Best NLP Course is Free - Jul 28, 2021.
What are the Design patterns for Machine Learning and why you should know them? For more advanced readers, how to use Kafka Connect to create an open source data pipeline for processing real-time data; The state-of-the-art NLP course is freely available; Python Data Structures Compared; Update your Machine Learning skills this summer.
- Machine Learning Skills – Update Yours This Summer - Jul 27, 2021.
The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.
- ColabCode: Deploying Machine Learning Models From Google Colab - Jul 22, 2021.
New to ColabCode? Learn how to use it to start a VS Code Server, Jupyter Lab, or FastAPI.
- Design patterns in machine learning - Jul 21, 2021.
Can we abstract best practices to real design patterns yet?
- KDnuggets™ News 21:n27, Jul 21: Top 6 Data Science Online Courses in 2021; Geometric Foundations of Deep Learning - Jul 21, 2021.
Top 6 Data Science Online Courses in 2021; Geometric foundations of Deep Learning; Google’s Director of Research Advice for Learning Data Science; SQL, Syllogisms, and Explanations; How to Create Unbiased Machine Learning Models
- When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule - Jul 20, 2021.
Machine learning models degrade with time, and need to be regularly updated. In the article, we suggest how to approach retraining and plan for it in advance.
- How Much Memory is your Machine Learning Code Consuming? - Jul 19, 2021.
Learn how to quickly check the memory footprint of your machine learning function/module with one line of command. Generate a nice report too.
- Advice for Learning Data Science from Google’s Director of Research - Jul 19, 2021.
Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.
- How to Create Unbiased Machine Learning Models - Jul 16, 2021.
In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5 - Jul 16, 2021.
Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.
- Pushing No-Code Machine Learning to the Edge - Jul 16, 2021.
Discover the power of no-code machine learning, and what it can accomplish when pushed to edge devices.
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4 - Jul 9, 2021.
With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.
- MLOps is an Engineering Discipline: A Beginner’s Overview - Jul 8, 2021.
MLOps = ML + DEV + OPS. MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning.
- Predict Customer Churn (the right way) using PyCaret - Jul 5, 2021.
A step-by-step guide on how to predict customer churn the right way using PyCaret that actually optimizes the business objective and improves ROI.
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3 - Jul 2, 2021.
Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.
- From Scratch: Permutation Feature Importance for ML Interpretability - Jun 30, 2021.
Use permutation feature importance to discover which features in your dataset are useful for prediction — implemented from scratch in Python.
- KDnuggets™ News 21:n24, Jun 30: What will the demand for Data Scientists be in 10 years?; Add A New Dimension To Your Photos Using Python - Jun 30, 2021.
What will the demand for Data Scientists be in 10 years? Will Data Scientists be extinct?; Add A New Dimension To Your Photos Using Python; Data Scientists are from Mars and Software Developers are from Venus; How to Train a Joint Entities and Relation Extraction Classifier using BERT Transformer with spaCy 3; In-Warehouse Machine Learning and the Modern Data Science Stack
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2 - Jun 25, 2021.
As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.
- In-Warehouse Machine Learning and the Modern Data Science Stack - Jun 24, 2021.
As your organization matures its data science portfolio and capabilities, establishing a modern data stack is vital to enabling such growth. Here, we overview various in-data warehouse machine learning services, and discuss each of their benefits and requirements.
- Create and Deploy Dashboards using Voila and Saturn Cloud - Jun 23, 2021.
Working with and training large datasets, maintaining them all in one place, and deploying them to production is a challenging job. In this article, we covered what Saturn Cloud is and how it can speed up your end-to-end pipeline, how to create dashboards using Voila and Python and publish them to production in just a few easy steps.
- Amazing Low-Code Machine Learning Capabilities with New Ludwig Update - Jun 22, 2021.
Integration with Ray, MLflow and TabNet are among the top features of this release.
- High Performance Deep Learning, Part 1 - Jun 18, 2021.
Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.
- Dashboards for Interpreting & Comparing Machine Learning Models - Jun 17, 2021.
This article discusses using Interpret to create dashboards for machine learning models.
- An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021.
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.
- 9 Deadly Sins of Machine Learning Dataset Selection - Jun 11, 2021.
Avoid endless pain in model debugging by focusing on datasets upfront.
- Feature Selection – All You Ever Wanted To Know - Jun 10, 2021.
Although your data set may contain a lot of information about many different features, selecting only the "best" of these to be considered by a machine learning model can mean the difference between a model that performs well--with better performance, higher accuracy, and more computational efficiency--and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore.
- KDnuggets™ News 21:n21, Jun 9: 5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning - Jun 9, 2021.
5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning; Will There Be a Shortage of Data Science Jobs in the Next 5 Years?; How to Make Python Code Run Incredibly Fast; Stop (and Start) Hiring Data Scientists
- The only Jupyter Notebooks extension you truly need - Jun 8, 2021.
Now you don’t need to restart the kernel after editing the code in your custom imports.
- 5 Data Science Open-source Projects You Should Consider Contributing to - Jun 7, 2021.
As you prepare to interview for a position in data science or are looking to jump to the next level, now is the time to enhance your skills and your resume with by working on rea, open-source projects. Here, we suggest a great selection of projects you can contribute to and help build something awesome, so, all you need to do choose one and tackle it head on.
- PyCaret 101: An introduction for beginners - Jun 7, 2021.
This article is a great overview of how to get started with PyCaret for all your machine learning projects.
- Beyond Brainless AI with a Feature Store - Jun 4, 2021.
AI-powered products that are limited to the data available within its application are like jellyfish: its autonomic system makes it functional, but it lacks a brain. However, you can evolve your models with data enriched "brains" through the help of a feature store.
- 10 Deadly Sins of Machine Learning Model Training - Jun 4, 2021.
These mistakes are easy to overlook but costly to redeem.
- Machine Learning Model Interpretation - Jun 2, 2021.
Read this overview of using Skater to build machine learning visualizations.
- How I Doubled My Income with Data Science and Machine Learning - Jun 1, 2021.
Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.
- Supercharge Your Machine Learning Experiments with PyCaret and Gradio - May 31, 2021.
A step-by-step tutorial to develop and interact with machine learning pipelines rapidly.
- Essential Machine Learning Algorithms: A Beginner’s Guide - May 26, 2021.
Machine Learning as a technology, ensures that our current gadgets and their software get smarter by the day. Here are the algorithms that you ought to know about to understand Machine Learning’s varied and extensive functionalities and their affectivity.
- Where Did You Apply Analytics, Data Science, Machine Learning in 2020/2021? - May 25, 2021.
Take part in the latest KDnuggets survey, and let us know where you have been applying Analytics, Data Science, Machine Learning in 2020/2021.
- Write and train your own custom machine learning models using PyCaret - May 25, 2021.
A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.
- How to Deal with Categorical Data for Machine Learning - May 24, 2021.
Check out this guide to implementing different types of encoding for categorical data, including a cheat sheet on when to use what type.
- Data Validation in Machine Learning is Imperative, Not Optional - May 24, 2021.
Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.
- Data Scientist, Data Engineer & Other Data Careers, Explained - May 19, 2021.
In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
- Easy MLOps with PyCaret + MLflow - May 18, 2021.
A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.
- Best Python Books for Beginners and Advanced Programmers - May 14, 2021.
Let's take a look at nine of the best Python books for both beginners and advanced programmers, covering topics such as data science, machine learning, deep learning, NLP, and more.
- The next-generation of AutoML frameworks - May 14, 2021.
AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No longer black-boxes, these powerful tools offer self-documenting capabilities and native Python notebook support.
- DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning - May 14, 2021.
In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
- The Explainable Boosting Machine - May 13, 2021.
As accurate as gradient boosting, as interpretable as linear regression.
- Machine Learning Pipeline Optimization with TPOT - May 12, 2021.
Let's revisit the automated machine learning project TPOT, and get back up to speed on using open source AutoML tools on our way to building a fully-automated prediction pipeline.
- KDnuggets™ News 21:n18, May 12: Data Preparation in SQL, with Cheat Sheet!; Rebuilding 7 Python Projects - May 12, 2021.
Data Preparation in SQL, with Cheat Sheet!; Rebuilding My 7 Python Projects; Applying Python’s Explode Function to Pandas DataFrames; Essential Linear Algebra for Data Science and Machine Learning; Similarity Metrics in NLP
- Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists - May 11, 2021.
Browser extensions are a productivity secret weapon for hackers and developers. Many machine learning practitioners use Chrome, and this list features must-have Chrome extensions for machine learning engineers and data scientists that you should check out today.
- A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know - May 6, 2021.
This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.
- Feature stores – how to avoid feeling that every day is Groundhog Day - May 6, 2021.
Feature stores stop the duplication of each task in the ML lifecycle. You can reuse features and pipelines for different models, monitor models consistently, and sidestep data leakage with this MLOps technology that everyone is talking about.
- What makes a winning entry in a Machine Learning competition? - May 5, 2021.
So you want to show your grit in a Kaggle-style competition? Many, many others have the same idea, including domain experts and non-experts, and academic and corporate teams. What does it take for your bright ideas and skills to come out on top of thousands of competitors?
- The Machine Learning Research Championed by the Biggest AI Labs in the World - May 5, 2021.
How Google, Microsoft, Facebook, DeepMind, OpenAI, Amazon and IBM think about the future of AI.
- Disentangling AI, Machine Learning, and Deep Learning - May 4, 2021.
The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.
- XGBoost Explained: DIY XGBoost Library in Less Than 200 Lines of Python - May 3, 2021.
Understand how XGBoost work with a simple 200 lines codes that implement gradient boosting for decision trees.
- Gradient Boosted Decision Trees – A Conceptual Explanation - Apr 30, 2021.
Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.
- FluDemic – using AI and Machine Learning to get ahead of disease - Apr 30, 2021.
We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.
- Feature Engineering of DateTime Variables for Data Science, Machine Learning - Apr 29, 2021.
Learn how to make more meaningful features from DateTime type variables to be used by Machine Learning Models.
- Best Podcasts for Machine Learning - Apr 28, 2021.
Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.
- Multiple Time Series Forecasting with PyCaret - Apr 27, 2021.
A step-by-step tutorial to forecast multiple time series with PyCaret.
- Improving model performance through human participation - Apr 23, 2021.
Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.
- Data Science Books You Should Start Reading in 2021 - Apr 23, 2021.
Check out this curated list of the best data science books for any level.
- The Three Edge Case Culprits: Bias, Variance, and Unpredictability - Apr 22, 2021.
Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?
- Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1 - Apr 22, 2021.
New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.
- How Uber manages Machine Learning Experiments with Comet.ml - Apr 21, 2021.
At Uber, where ML is fundamental to most products, a mechanism to manage offline experiments easily is needed to improve developer velocity. To solve for this, Uber AI was looking for a solution that will potentially complement and extend its in-house experiment management and collaboration capabilities.
- Time Series Forecasting with PyCaret Regression Module - Apr 21, 2021.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.
- Free From Stanford: Machine Learning with Graphs - Apr 19, 2021.
Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher teaches their topic of expertise. Accessible materials include slides, videos, and more.
- 6 Mistakes To Avoid While Training Your Machine Learning Model - Apr 15, 2021.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
- Continuous Training for Machine Learning – a Framework for a Successful Strategy - Apr 14, 2021.
A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.
- KDnuggets™ News 21:n14, Apr 14: A/B Testing: Common Questions and Answers in Data Science Interviews; Interpretable Machine Learning: The Free eBook - Apr 14, 2021.
Common Questions and Answers on A/B testing in Data Science Interviews; Interpretable Machine Learning: The Free eBook; Why machine learning struggles with causality; Deep Learning Recommendation Models: A Deep Dive; and more.
- Automated Anomaly Detection Using PyCaret - Apr 13, 2021.
Learn to automate anomaly detection using the open source machine learning library PyCaret.
- 7 Must-Haves in your Data Science CV - Apr 13, 2021.
If you are looking for a new role as a Data Scientist -- either as a first job fresh out of school, a career change, or a shift to another organization -- then check off as many of these critical points as possible to stand out in the crowd and pass the hiring manager's initial CV screen.
- Zero-Shot Learning: Can you classify an object without seeing it before? - Apr 12, 2021.
Developing machine learning models that can perform predictive functions on data it has never seen before has become an important research area called zero-shot learning. We tend to be pretty great at recognizing things in the world we never saw before, and zero-shot learning offers a possible path toward mimicking this powerful human capability.
- Why machine learning struggles with causality - Apr 8, 2021.
If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021.
Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
- How Noisy Labels Impact Machine Learning Models - Apr 6, 2021.
Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.
- How to Dockerize Any Machine Learning Application - Apr 6, 2021.
How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.
- The Best Machine Learning Frameworks & Extensions for TensorFlow - Apr 5, 2021.
Check out this curated list of useful frameworks and extensions for TensorFlow.
- How to deploy Machine Learning/Deep Learning models to the web - Apr 5, 2021.
The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
- Awesome Tricks And Best Practices From Kaggle - Apr 5, 2021.
Easily learn what is only learned by hours of search and exploration.
- Shapash: Making Machine Learning Models Understandable - Apr 2, 2021.
Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
- Easy AutoML in Python - Apr 1, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- Overview of MLOps - Mar 26, 2021.
Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.
- Data Science Curriculum for Professionals - Mar 25, 2021.
If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
- KDnuggets™ News 21:n12, Mar 24: More Data Science Cheat sheets; Top YouTube Channels for Machine Learning - Mar 24, 2021.
Happy with your job or not? Either way, vote in KDnuggets Poll on Data Job Satisfaction
to help us understand the current situation.
In this issue, More data science cheatsheets; How to create your data science portfolio; The best machine learning frameworks and extensions for scikit-learn; Top youtube channels for machine learning; dbt, the ETL and ELT disrupter;
- Top YouTube Machine Learning Channels - Mar 23, 2021.
These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.
- The Best Machine Learning Frameworks & Extensions for Scikit-learn - Mar 22, 2021.
Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.
- Learning from machine learning mistakes - Mar 19, 2021.
Read this article and discover how to find weak spots of a regression model.
- More Data Science Cheatsheets - Mar 18, 2021.
It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.
- Automating Machine Learning Model Optimization - Mar 17, 2021.
This articles presents an overview of using Bayesian Tuning and Bandits for machine learning.
- Data Validation and Data Verification – From Dictionary to Machine Learning - Mar 16, 2021.
In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.
- 10 Amazing Machine Learning Projects of 2020 - Mar 15, 2021.
So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
- A Beginner’s Guide to the CLIP Model - Mar 11, 2021.
CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.
- A Machine Learning Model Monitoring Checklist: 7 Things to Track - Mar 11, 2021.
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
- KDnuggets™ News 21:n10, Mar 10: More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training - Mar 10, 2021.
More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training; Dask and Pandas: No Such Thing as Too Much Data; 9 Skills You Need to Become a Data Engineer; 8 Women in AI Who Are Striving to Humanize the World
- 4 Machine Learning Concepts I Wish I Knew When I Built My First Model - Mar 9, 2021.
Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.
- 8 Women in AI Who Are Striving to Humanize the World - Mar 8, 2021.
Some exceptional female researchers and engineers are working on projects to make the world a better place with the help of AI, data science, and machine learning.
- More Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2021.
Useful resources to help more women enter and succeed in AI, Data Science, and Machine Learning fields.
- Speeding up Scikit-Learn Model Training - Mar 5, 2021.
If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.
- Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - Mar 5, 2021.
PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.
- Reducing the High Cost of Training NLP Models With SRU++ - Mar 4, 2021.
The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.