- Top 3 Free Resources to Learn Linear Algebra for Machine Learning - Mar 3, 2022.
This article will solely focus on learning linear algebra, as it forms the backbone of machine learning model implementation.
- How to Create a Dataset for Machine Learning - Feb 28, 2022.
Datasets - properly curated and labeled - remain a scarce resource. What can be done about this?
- Vanishing Gradient Problem, Explained - Feb 25, 2022.
This blog post aims to describe the vanishing gradient problem and explain how use of the sigmoid function resulted in it.
- Essential Machine Learning Algorithms: A Beginner’s Guide - Feb 22, 2022.
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 effectiveness.
- The Challenges of Creating Features for Machine Learning - Feb 21, 2022.
What are the challenges of creating features for machine learning and how can we mitigate them.
- How You Can Use Machine Learning to Automatically Label Data - Feb 18, 2022.
AI and machine learning can provide us with these tools. This guide will explore how we can use machine learning to label data.
- An Easy Guide to Choose the Right Machine Learning Algorithm - Feb 17, 2022.
There's no free lunch in machine learning. So, determining which algorithm to use depends on many factors from the type of problem at hand to the type of output you are looking for. This guide offers several considerations to review when exploring the right ML approach for your dataset.
- No Brainer AutoML with AutoXGB - Feb 17, 2022.
Learn how to train, optimize, and build API with a few lines of code using AutoXGB.
- Free MIT Courses on Calculus: The Key to Understanding Deep Learning - Feb 14, 2022.
Calculus is the key to fully understanding how neural networks function. Go beyond a surface understanding of this mathematics discipline with these free course materials from MIT.
- Top 5 Free Machine Learning Courses - Feb 14, 2022.
Give a boost to your career and learn job-ready machine learning skills by taking the best free online courses.
- How to Learn Math for Machine Learning - Feb 11, 2022.
So how much math do you need to know in order to work in the data science industry? The answer: Not as much as you think.
- Decision Tree Algorithm, Explained - Feb 9, 2022.
All you need to know about decision trees and how to build and optimize decision tree classifier.
- An Overview of Logistic Regression - Feb 4, 2022.
Logistic regression is an extension of linear regression to solve classification problems. Read more on the specifics of this algorithm here.
- Effective Testing for Machine Learning - Jan 31, 2022.
Given how uncertain ML projects are, this is an incremental strategy that you can adopt as your project matures; it includes test examples to provide a clear idea of how these tests look in practice, and a complete project implementation is available on GitHub. By the end of the post, you’ll be able to develop more robust ML pipelines.
- Learn Machine Learning 4X Faster by Participating in Competitions - Jan 25, 2022.
Participating in competitions has taught me everything about machine learning and how It can help you learn multiple domains faster than online courses.
- 3 Reasons Why Data Scientists Should Use LightGBM - Jan 24, 2022.
There are many great boosting Python libraries for data scientists to reap the benefits of. In this article, the author discusses LightGBM benefits and how they are specific to your data science job.
- Transfer Learning for Image Recognition and Natural Language Processing - Jan 14, 2022.
Read the second article in this series on Transfer Learning, and learn how to apply it to Image Recognition and Natural Language Processing.
- A (Much) Better Approach to Evaluate Your Machine Learning Model - Jan 12, 2022.
Using one or two performance metrics seems sufficient to claim that your ML model is good — chances are that it’s not.
- Interpretable Neural Networks with PyTorch - Jan 11, 2022.
Learn how to build feedforward neural networks that are interpretable by design using PyTorch.
- A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment - Jan 11, 2022.
How to use scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment.
- What is Transfer Learning? - Jan 5, 2022.
During transfer learning, the knowledge leveraged and rapid progress from a source task is used to improve the learning and development to a new target task. Read on for a deeper dive on the subject.
- Why Do Machine Learning Models Die In Silence? - Jan 5, 2022.
A critical problem for companies when integrating machine learning in their business processes is not knowing why they don't perform well after a while. The reason is called concept drift. Here's an informational guide to understanding the concept well.
- Hands-on Reinforcement Learning Course Part 3: SARSA - Jan 3, 2022.
This is part 3 of my hands-on course on reinforcement learning, which takes you from zero to HERO . Today we will learn about SARSA, a powerful RL algorithm.
- 4 Reasons Why You Shouldn’t Use Machine Learning - Dec 29, 2021.
It's time to learn: machine learning is not a Swiss Army knife.
- How AI/ML Technology Integration Will Help Business in Achieving Goals in 2022 - Dec 29, 2021.
AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.
- Versioning Machine Learning Experiments vs Tracking Them - Dec 27, 2021.
Learn how to improve ML reproducibility by treating experiments as code.
- Alternative Feature Selection Methods in Machine Learning - Dec 24, 2021.
Feature selection methodologies go beyond filter, wrapper and embedded methods. In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score.
- AI and climate change have a complicated relationship - Dec 23, 2021.
Learn about the importance of environmental AI and its carbon impact in this comprehensive review.
- 6 Predictive Models Every Beginner Data Scientist Should Master - Dec 23, 2021.
Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
- Machine learning does not produce value for my business. Why? - Dec 22, 2021.
What is going on when machine learning can't make the jump from testing to production, and so doesn't add any business value?
- Federated Learning: Collaborative Machine Learning with a Tutorial on How to Get Started - Dec 21, 2021.
Read on to learn more about the intricacies of federated learning and what it can do for machine learning on sensitive data.
- How to Speed Up XGBoost Model Training - Dec 20, 2021.
XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.
- Cloud ML In Perspective: Surprises of 2021, Projections for 2022 - Dec 16, 2021.
Let’s take a closer look on Cloud ML market in 2021 in retrospective (with occasional drills into realities of 2020, too). Read this in-depth analysis.
- Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022 - Dec 14, 2021.
We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.
- Feature Selection: Where Science Meets Art - Dec 14, 2021.
From heuristic to algorithmic feature selection techniques for data science projects.
- Introduction to Clustering in Python with PyCaret - Dec 13, 2021.
A step-by-step, beginner-friendly tutorial for unsupervised clustering tasks in Python using PyCaret.
- Main 2021 Developments and Key 2022 Trends in AI, Data Science, Machine Learning Technology - Dec 10, 2021.
Our panel of leading experts reviews 2021 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
- Deep Neural Networks Don’t Lead Us Towards AGI - Dec 9, 2021.
Machine learning techniques continue to evolve with increased efficiency for recognition problems. But, they still lack the critical element of intelligence, so we remain a long way from attaining AGI.
- Should You Become a Freelance Artificial Intelligence Engineer? - Dec 8, 2021.
Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2021 and Key Trends for 2022 - Dec 8, 2021.
2021 has almost come and gone. We saw some standout advancements in AI, Analytics, Machine Learning, Data Science, Deep Learning Research this past year, and the future, starting with 2022, looks bright. As per KDnuggets tradition, our collection of experts have contributed their insights on the matter. Read on to find out more.
- KDnuggets™ News 21:n46, Dec 8: How to Get Certified as a Data Scientist; 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022 - Dec 8, 2021.
How to Get Certified as a Data Scientist; 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022; 2021: A Year Full of Amazing AI papers — A Review; What Does a Data Scientist Do?; A $9B AI Failure, Examined
- Introduction to Binary Classification with PyCaret - Dec 7, 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 it for binary classification.
- A Beginner’s Guide to End to End Machine Learning - Dec 6, 2021.
Learn to train, tune, deploy and monitor machine learning models.
- Using PyCaret’s New Time Series Module - Dec 3, 2021.
PyCaret’s new time series module is now available in beta. Staying true to the simplicity of PyCaret, it is consistent with the existing API and comes with a lot of functionalities.
- Building Massively Scalable Machine Learning Pipelines with Microsoft Synapse ML - Nov 30, 2021.
The new platform provides a single API to abstract dozens of ML frameworks and databases.
- Common Misconceptions About Differential Privacy - Nov 24, 2021.
This article will clarify some common misconceptions about differential privacy and what it guarantees.
- 3 Differences Between Coding in Data Science and Machine Learning - Nov 19, 2021.
The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.
- KDnuggets™ News 21:n44, Nov 17: Don’t Waste Time Building Your Data Science Network; 19 Data Science Project Ideas for Beginners - Nov 17, 2021.
Don’t Waste Time Building Your Data Science Network; 19 Data Science Project Ideas for Beginners; How I Redesigned over 100 ETL into ELT Data Pipelines; Anecdotes from 11 Role Models in Machine Learning; The Ultimate Guide To Different Word Embedding Techniques In NLP
- Anecdotes from 11 Role Models in Machine Learning - Nov 12, 2021.
The skills needed to create good data are also the skills needed for good leadership.
- Dream Come True: Building websites by thinking about them - Nov 11, 2021.
From the mind to the computer, make websites using your imagination!
- The Common Misconceptions About Machine Learning - Nov 9, 2021.
Beginners in the field can often have many misconceptions about machine learning that sometimes can be a make-it-or-break-it moment for the individual switching careers or starting fresh. This article clearly describes the ground truth realities about learning new ML skills and eventually working professionally as a machine learning engineer.
- Machine Learning Safety: Unsolved Problems - Nov 5, 2021.
There remain critical challenges in machine learning that, if left resolved, could lead to unintended consequences and unsafe use of AI in the future. As an important and active area of research, roadmaps are being developed to help guide continued ML research and use toward meaningful and robust applications.
- AI Infinite Training & Maintaining Loop - Nov 4, 2021.
Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.
- 7 of The Coolest Machine Learning Topics of 2021 at ODSC West - Nov 3, 2021.
At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types, or register for a free AI Expo Pass to see what some big names in AI are doing now.
- Visual Scoring Techniques for Classification Models - Nov 3, 2021.
Read this article assessing a model performance in a broader context.
- KDnuggets™ News 21:n42, Nov 3: Google Recommendations Before Taking Their Machine Learning Course; Guide to Data Science Jobs - Nov 3, 2021.
What Google Recommends You do Before Taking Their Machine Learning or Data Science Course; A Guide to 14 Different Data Science Jobs; Analyze Python Code in Jupyter Notebooks; Machine Learning Model Development and Model Operations: Principles and Practices; Want to Join a Bank? Everything Data Scientists Need to Know About Working in Fintech
- Design Patterns for Machine Learning Pipelines - Nov 2, 2021.
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.
- Machine Learning Model Development and Model Operations: Principles and Practices - Oct 27, 2021.
The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
- Getting Started with PyTorch Lightning - Oct 26, 2021.
As a library designed for production research, PyTorch Lightning streamlines hardware support and distributed training as well, and we’ll show how easy it is to move training to a GPU toward the end.
- Guide To Finding The Right Predictive Maintenance Machine Learning Techniques - Oct 25, 2021.
What happens to a life so dependent on machines, when that particular machine breaks down? This is precisely why there’s a dire need for predictive maintenance with machine learning.
- Introduction to AutoEncoder and Variational AutoEncoder (VAE) - Oct 22, 2021.
Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
- KDnuggets™ News 21:n40, Oct 20: The 20 Python Packages You Need For Machine Learning and Data Science; Ace Data Science Interviews with Portfolio Projects - Oct 20, 2021.
The 20 Python Packages You Need For Machine Learning and Data Science; How to Ace Data Science Interview by Working on Portfolio Projects; Deploying Your First Machine Learning API; Real Time Image Segmentation Using 5 Lines of Code; What is Clustering and How Does it Work?
- Real Time Image Segmentation Using 5 Lines of Code - Oct 18, 2021.
PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code. PixelLib now provides support for PyTorch backend to perform faster, more accurate segmentation and extraction of objects in images and videos using PointRend segmentation architecture.
- Serving ML Models in Production: Common Patterns - Oct 18, 2021.
Over the past couple years, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready.
- How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method - Oct 15, 2021.
Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?
- Deploying Your First Machine Learning API - Oct 14, 2021.
Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- The 20 Python Packages You Need For Machine Learning and Data Science - Oct 14, 2021.
Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
- Building Multimodal Models: Using the widedeep Pytorch package - Oct 13, 2021.
This article gets you started on the open-source widedeep PyTorch framework developed by Javier Rodriguez Zaurin.
- Dealing with Data Leakage - Oct 8, 2021.
Target leakage and data leakage represent challenging problems in machine learning. Be prepared to recognize and avoid these potentially messy problems.
- Building and Operationalizing Machine Learning Models: Three tips for success - Oct 7, 2021.
With more enterprises implementing machine learning to improve revenue and operations, properly operationalizing the ML lifecycle in a holistic way is crucial for data teams to make their projects efficient and effective.
- KDnuggets™ News 21:n37, Sep 29: Nine Tools I Wish I Mastered Before My PhD in Machine Learning; Path to Full Stack Data Science - Sep 29, 2021.
Whether you have a PhD or not, learn these very useful 9 tools to increase your mastery of Machine Learning; Check this detailed path to becoming a full stack Data Scientist; Then do one of these 20 Machine Learning Projects that will help you get a job; See a Breakdown of Deep Learning Frameworks; and more.
- Data Analysis Using Scala - Sep 24, 2021.
It is very important to choose the right tool for data analysis. On the Kaggle forums, where international Data Science competitions are held, people often ask which tool is better. R and Python are at the top of the list. In this article we will tell you about an alternative stack of data analysis technologies, based on Scala.
- 20 Machine Learning Projects That Will Get You Hired - Sep 22, 2021.
If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.
- Nine Tools I Wish I Mastered Before My PhD in Machine Learning - Sep 22, 2021.
Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- KDnuggets™ News 21:n36, Sep 22: The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python - Sep 22, 2021.
The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python; Introduction to Automated Machine Learning; How to be a Data Scientist without a STEM degree; What Is The Real Difference Between Data Engineers and Data Scientists?
- How to Find Weaknesses in your Machine Learning Models - Sep 20, 2021.
FreaAI: a new method from researchers at IBM.
- 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.
- 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.
- 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