- 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.
- 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.
- Getting Started with Distributed Machine Learning with PyTorch and Ray - Mar 3, 2021.
Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.
- Machine Learning Systems Design: A Free Stanford Course - Feb 26, 2021.
This freely-available course from Stanford should give you a toolkit for designing machine learning systems.
- Why Do Machine Learning Projects Fail? - Feb 24, 2021.
At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.
- KDnuggets™ News 21:n08, Feb 24: Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer - Feb 24, 2021.
Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer; Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall; Feature Store as a Foundation for Machine Learning; Approaching (Almost) Any Machine Learning Problem
- Feature Store as a Foundation for Machine Learning - Feb 19, 2021.
With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.
- Approaching (Almost) Any Machine Learning Problem - Feb 18, 2021.
This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- Distributed and Scalable Machine Learning [Webinar] - Feb 17, 2021.
Mike McCarty and Gil Forsyth work at the Capital One Center for Machine Learning, where they are building internal PyData libraries that scale with Dask and RAPIDS. For this webinar, Feb 23 @ 2 pm PST, 5pm EST, they’ll join Hugo Bowne-Anderson and Matthew Rocklin to discuss their journey to scale data science and machine learning in Python.
- Machine Learning for Cybersecurity Certificate at U. of Chicago - Feb 16, 2021.
Hands-On Machine Learning Training from UChicago: 5-week remote Machine Learning for Cybersecurity certificate, Mar 30 - Apr 27. Learn from & network with leading faculty/industry leaders, learn data-driven prevention strategies. Group discounts, tuition support.
- Easy, Open-Source AutoML in Python with EvalML - Feb 16, 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.
- How to Speed up Scikit-Learn Model Training - Feb 11, 2021.
Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?
- Machine Learning – it’s all about assumptions - Feb 11, 2021.
Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.
- A Critical Comparison of Machine Learning Platforms in an Evolving Market - Feb 11, 2021.
There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.
- My machine learning model does not learn. What should I do? - Feb 10, 2021.
This article presents 7 hints on how to get out of the quicksand.
- Microsoft Explores Three Key Mysteries of Ensemble Learning - Feb 8, 2021.
A new paper studies three key puzzling characteristics of deep learning ensembles and some potential explanations.
- Saving and loading models in TensorFlow — why it is important and how to do it - Feb 3, 2021.
So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.
- Machine learning adversarial attacks are a ticking time bomb - Jan 29, 2021.
Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.
- Top 5 Reasons Why Machine Learning Projects Fail - Jan 28, 2021.
The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.
- Machine learning is going real-time - Jan 28, 2021.
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
- Popular Machine Learning Interview Questions, part 2 - Jan 27, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
- Support Vector Machine for Hand Written Alphabet Recognition in R - Jan 27, 2021.
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
- KDnuggets™ News 21:n04, Jan 27: The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search - Jan 27, 2021.
The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search; Data Engineering — the Cousin of Data Science, is Troublesome; Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning; Popular Machine Learning Interview Questions
- Want to Be a Data Scientist? Don’t Start With Machine Learning - Jan 26, 2021.
Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.
- The Ultimate Scikit-Learn Machine Learning Cheatsheet - Jan 25, 2021.
With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.
- Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants - Jan 22, 2021.
Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.
- How to Use MLOps for an Effective AI Strategy - Jan 21, 2021.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.
- Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning - Jan 21, 2021.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
- Popular Machine Learning Interview Questions - Jan 20, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
- K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines - Jan 15, 2021.
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
- KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 13, 2021.
Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers
- Working With Sparse Features In Machine Learning Models - Jan 12, 2021.
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
- 5 Tools for Effortless Data Science - Jan 11, 2021.
The sixth tool is coffee.
- CatalyzeX: A must-have browser extension for machine learning engineers and researchers - Jan 6, 2021.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).
- MLOps: Model Monitoring 101 - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
- All Machine Learning Algorithms You Should Know in 2021 - Jan 4, 2021.
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Dec 31, 2020.
We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- How to easily check if your Machine Learning model is fair? - Dec 24, 2020.
Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.
- Can you trust AutoML? - Dec 23, 2020.
Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.
- 5 strategies for enterprise machine learning for 2021 - Dec 22, 2020.
While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.
- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
- MLOps Is Changing How Machine Learning Models Are Developed - Dec 21, 2020.
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.
- ebook: Fundamentals for Efficient ML Monitoring - Dec 17, 2020.
We've gathered best practices for data science and engineering teams to create an efficient framework to monitor ML models. This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere.
- How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
- KDnuggets™ News 20:n47, Dec 16: A Rising Library Beating Pandas in Performance; R or Python? Why Not Both? - Dec 16, 2020.
Also: 10 Python Skills They Don't Teach in Bootcamp; Data Science Volunteering: Ways to Help; A Journey from Software to Machine Learning Engineer; Data Science and Machine Learning: The Free eBook
- Data Science and Machine Learning: The Free eBook - Dec 15, 2020.
Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.
- State of Data Science and Machine Learning 2020: 3 Key Findings - Dec 15, 2020.
Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here.
- Implementing the AdaBoost Algorithm From Scratch - Dec 10, 2020.
AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.
- A Journey from Software to Machine Learning Engineer - Dec 10, 2020.
In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.
- Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology - Dec 9, 2020.
Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
- AI registers: finally, a tool to increase transparency in AI/ML - Dec 9, 2020.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
- Machine Learning: Cutting Edge Tech with Deep Roots in Other Fields - Dec 8, 2020.
Join INFORMS community of data, analytics, operations research, and statistics professionals and tackle the future together. With nearly 13,000 members around the world, INFORMS is the largest international association for data science professionals.
- Change the Background of Any Video with 5 Lines of Code - Dec 7, 2020.
Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.
- Pruning Machine Learning Models in TensorFlow - Dec 4, 2020.
Read this overview to learn how to make your models smaller via pruning.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 - Dec 3, 2020.
2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
- Is Your Machine Learning Model Likely to Fail? - Nov 27, 2020.
Read about these 5 missteps to avoid in your planning process.
- How to Know if a Neural Network is Right for Your Machine Learning Initiative - Nov 26, 2020.
It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.
- Better data apps with Streamlit’s new layout options - Nov 26, 2020.
Introducing new layout primitives - including columns, containers and expanders!
- Essential Math for Data Science: Integrals And Area Under The Curve - Nov 25, 2020.
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.
- How to Incorporate Tabular Data with HuggingFace Transformers - Nov 25, 2020.
In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.
- Fraud through the eyes of a machine - Nov 24, 2020.
Data structured as a network of relationships can be modeled as a graph, which can then help extract insights into the data through machine learning and rule-based approaches. While these graph representations provide a natural interface to transactional data for humans to appreciate, caution and context must be applied when leveraging machine-based interpretations of these connections.
- Know-How to Learn Machine Learning Algorithms Effectively - Nov 23, 2020.
The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.
- How Machine Learning Works for Social Good - Nov 21, 2020.
We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.
- Cellular Automata in Stream Learning - Nov 20, 2020.
In this post, we will start presenting CA as pattern recognition methods for stream learning. Finally, we will briefly mention two recent CA-based solutions for stream learning. Both are highly interpretable as their cellular structure represents directly the mapping between the feature space and the labels to be predicted.
- Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI - Nov 19, 2020.
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
- Primer on TensorFlow and how PerceptiLabs Makes it Easier - Nov 18, 2020.
With PerceptiLabs, beginners can get started building a model more quickly, and those with more experience can still dive into the code. Given that PerceptiLabs runs TensorFlow behind the scenes, we thought we'd walk through the framework so you can understand its basics, and how it is utilized by PerceptiLabs.
- 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use - Nov 18, 2020.
If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.
- How to Future-Proof Your Data Science Project - Nov 18, 2020.
This article outlines 5 critical elements of ML model selection & deployment.
- 5 Things You Are Doing Wrong in PyCaret - Nov 16, 2020.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. Find out 5 ways to improve your usage of the library.
- Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
- When Machine Learning Knows Too Much About You - Nov 14, 2020.
If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.
- tensorflow + dalex = :) , or how to explain a TensorFlow model - Nov 13, 2020.
Having a machine learning model that generates interesting predictions is one thing. Understanding why it makes these predictions is another. For a tensorflow predictive model, it can be straightforward and convenient develop an explainable AI by leveraging the dalex Python package.
- Predicting Heart Disease Using Machine Learning? Don’t! - Nov 10, 2020.
I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.
- Moving from Data Science to Machine Learning Engineering - Nov 10, 2020.
The world of machine learning — and software — is changing. Read this article to find out how, and what you can do to stay ahead of it.
- Doing the impossible? Machine learning with less than one example - Nov 9, 2020.
Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
- Change the Background of Any Image with 5 Lines of Code - Nov 9, 2020.
Blur, color, grayscale and change the background of any image with a picture using PixelLib.
- Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read - Nov 5, 2020.
There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.
- Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know - Nov 4, 2020.
The terms “interpretability,” “explainability” and “black box” are tossed about a lot in the context of machine learning, but what do they really mean, and why do they matter?
- Top 38 Python Libraries for Data Science, Data Visualization & Machine Learning - Nov 2, 2020.
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
- Dealing with Imbalanced Data in Machine Learning - Oct 29, 2020.
This article presents tools & techniques for handling data when it's imbalanced.
- Exploring the Significance of Machine Learning for Algorithmic Trading with Stefan Jansen - Oct 28, 2020.
The immense expansion of digital data has increased the demand for proficiency in trading strategies that use machine learning (ML). Learn more from author Stefan Jansen, and get his latest book on the subject from Packt Publishing.
- An Introduction to AI, updated - Oct 28, 2020.
We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.
- DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.
- Behavior Analysis with Machine Learning and R: The free eBook - Oct 22, 2020.
Check out this new free ebook to learn how to leverage the power of machine learning to analyze behavioral patterns from sensor data and electronic records using R.
- 5 Must-Read Data Science Papers (and How to Use Them) - Oct 20, 2020.
Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.
- Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
This article covers using scikit-learn to obtain the optimal number of features for your machine learning project.
- How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
- DOE SMART Visualization Platform 1.5M Prize Challenge - Oct 16, 2020.
The U.S. Department of Energy’s (DOE) Office of Fossil Energy (FE) will award up to $1.5 million to winning innovators in a prize challenge to support FE’s SMART initiative. Registration deadline to participate in the challenge is 11:59 p.m. EDT Friday, Jan 22, 2021.
- Fast Gradient Boosting with CatBoost - Oct 16, 2020.
In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.
- Machine Learning’s Greatest Omission: Business Leadership - Oct 15, 2020.
Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.
- Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
The new release makes Ludwig one of the most complete open source AutoML stacks in the market.
- 5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
Our focus for this piece is to establish the best practices that make an ML project successful.
- Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases.
- Annotated Machine Learning Research Papers - Oct 9, 2020.
Check out this collection of annotated machine learning research papers, and no longer fear their reading.
- How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems - Oct 8, 2020.
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.
- Free Introductory Machine Learning Course From Amazon - Oct 7, 2020.
Amazon's Machine Learning University offers an introductory course titled Accelerated Machine Learning, which is a good starting place for those looking for a foundation in generalized practical ML.
- 5 Challenges to Scaling Machine Learning Models - Oct 7, 2020.
ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.
- KDnuggets™ News 20:n38, Oct 7: 10 Essential Skills You Need to Know to Start Doing Data Science; The Best Free Data Science eBooks: 2020 Update - Oct 7, 2020.
Also: Comparing the Top Business Intelligence Tools: Power BI vs Tableau vs Qlik vs Domo; 5 Concepts Every Data Scientist Should Know; Understanding Transformers, the Data Science Way; 10 Best Machine Learning Courses in 2020
- 10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
- New U. of Chicago Machine Learning for Cybersecurity Certificate Gives Professionals Tools to Detect and Prevent Attacks - Oct 5, 2020.
Machine learning has become an essential tool for IT security professionals seeking to detect and prevent attacks and vulnerabilities. The Center for Data and Computing (CDAC) convened a trio of University of Chicago computer science faculty to produce an innovative new remote Machine Learning for Cybersecurity certificate that will be offered for the first time this autumn.
- Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code - Oct 5, 2020.
Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.
- Machine Learning Model Deployment - Sep 30, 2020.
Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.
- Missing Value Imputation – A Review - Sep 29, 2020.
Detecting and handling missing values in the correct way is important, as they can impact the results of the analysis, and there are algorithms that can’t handle them. So what is the correct way?
- International alternatives to Kaggle for Data Science / Machine Learning competitions - Sep 29, 2020.
While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.
- LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale - Sep 23, 2020.
The reference architecture is powering mission critical machine learning workflows within LinkedIn.
- How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy - Sep 23, 2020.
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.
- KDnuggets™ News 20:n36, Sep 23: New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project - Sep 23, 2020.
New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python; Online Certificates/Courses in AI, Data Science, Machine Learning; Can Neural Networks Show Imagination?
- Machine Learning from Scratch: Free Online Textbook - Sep 22, 2020.
If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.
- What an Argentine Writer and a Hungarian Mathematician Can Teach Us About Machine Learning Overfitting - Sep 21, 2020.
This article presents some beautiful ideas about intelligence and how they related to modern machine learning.
- Coursera’s Machine Learning for Everyone Fulfills Unmet Training Needs - Sep 17, 2020.
Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.
- Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities - Sep 16, 2020.
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
- The Maslow’s hierarchy your data science team needs - Sep 15, 2020.
Domino Data Lab was announced as a leader for the second year in a row in the recently released “Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning (PAML), Q3 2020” analyst report. True to our data science roots, we’ve built a Maslow’s hierarchy of data science team needs.