- Weak Supervision Modeling, Explained - May 27, 2022.
This article dives into weak supervision modeling and truly understanding the label model.
- Dynamic Time Warping Algorithm in Time Series, Explained - May 26, 2022.
The article contains an explanation of the Dynamic Time Warping algorithm.
- Operationalizing Machine Learning from PoC to Production - May 20, 2022.
Most companies haven’t seen ROI from machine learning since the benefit is only realized when the models are in production. Here’s how to make sure your ML project works.
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability - May 20, 2022.
We give a taxonomy of the trustworthy GNNs in privacy, robustness, fairness, and explainability. For each aspect, we categorize existing works into various categories, give general frameworks in each category, and more.
- HuggingFace Has Launched a Free Deep Reinforcement Learning Course - May 17, 2022.
Hugging Face has released a free course on Deep RL. It is self-paced and shares a lot of pointers on theory, tutorials, and hands-on guides.
- Popular Machine Learning Algorithms - May 16, 2022.
This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.
- Reinforcement Learning for Newbies - May 16, 2022.
A simple guide to reinforcement learning for a complete beginner. The blog includes definitions with examples, real-life applications, key concepts, and various types of learning resources.
- Centroid Initialization Methods for k-means Clustering - May 13, 2022.
This article is the first in a series of articles looking at the different aspects of k-means clustering, beginning with a discussion on centroid initialization.
- The “Hello World” of Tensorflow - May 13, 2022.
In this article, we will build a beginner-friendly machine learning model using TensorFlow.
- Deep Learning For Compliance Checks: What’s New? - May 12, 2022.
By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands.
- 5 Free Hosting Platform For Machine Learning Applications - May 12, 2022.
Learn about the free and easy-to-deploy hosting platform for your machine learning projects.
- Machine Learning’s Sweet Spot: Pure Approaches in NLP and Document Analysis - May 10, 2022.
While it is true that Machine Learning today isn’t ready for prime time in many business cases that revolve around Document Analysis, there are indeed scenarios where a pure ML approach can be considered.
- Machine Learning Key Terms, Explained - May 9, 2022.
Read this overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.
- Everything You Need to Know About Tensors - May 6, 2022.
In this article, we will cover the basics of the tensors.
- Image Classification with Convolutional Neural Networks (CNNs) - May 4, 2022.
In this article, we’ll look at what Convolutional Neural Networks are and how they work.
- Top 10 Machine Learning Demos: Hugging Face Spaces Edition - May 2, 2022.
Hugging Face Spaces allows you to have an interactive experience with the machine learning models, and we will be discovering the best application to get some inspiration.
- MLOps: The Best Practices and How To Apply Them - Apr 28, 2022.
Here are some of the best practices for implementing MLOps successfully.
- A Simple Guide to Machine Learning Visualisations - Apr 26, 2022.
Create simple, effective machine learning plots with Yellowbrick
- Optimizing Genes with a Genetic Algorithm - Apr 22, 2022.
In the simplest terms genetic algorithms simulate a population where each individual is a possible “solution” and let survival of the fittest do its thing.
- A Community for Synthetic Data is Here and This is Why We Need It - Apr 22, 2022.
The first open-source platform for synthetic data is here to help educate the broader machine learning and computer vision communities on the emerging technology.
- Machine Learning Books You Need To Read In 2022 - Apr 21, 2022.
I have a list of Machine Learning books you need to read in 2022; beginner, intermediate, expert, and for everybody.
- Deploy a Machine Learning Web App with Heroku - Apr 18, 2022.
In this article, you will learn to deploy a fully functional ML web application in under 3 minutes.
- Nearest Neighbors for Classification - Apr 12, 2022.
Learn about the K-Nearest Neighbors machine learning algorithm for classification.
- Naïve Bayes Algorithm: Everything You Need to Know - Apr 8, 2022.
Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.
- 4 Factors to Identify Machine Learning Solvable Problems - Apr 6, 2022.
The near future holds incredible possibility for machine learning to solve real world problems. But we need to be be able to determine which problems are solvable by ML and which are not.
- Logistic Regression for Classification - Apr 4, 2022.
Deep dive into Logistic Regression with practical examples.
- DBSCAN Clustering Algorithm in Machine Learning - Apr 4, 2022.
An introduction to the DBSCAN algorithm and its implementation in Python.
- What is an MLOps Engineer? - Apr 1, 2022.
And why you should consider becoming one.
- Machine Learning Pipeline Optimization with TPOT - Mar 31, 2022.
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.
- Loss Functions: An Explainer - Mar 31, 2022.
A loss function measures how wrong the model is in terms of its ability to estimate the relationship between x and y. Find out about several common loss functions here.
- Time Series Forecasting with Ploomber, Arima, Python, and Slurm - Mar 29, 2022.
In this blog you will see how the authors took a raw .ipynb notebook that does time series forecasting with Arima, modularized it into a Ploomber pipeline, and ran parallel jobs on Slurm.
- MLOps Is a Mess But That’s to be Expected - Mar 25, 2022.
In this post, I want to focus the discussion about the state of machine learning operations (MLOps) today, where we are, where we are going.
- WTF is a Tensor?!? - Mar 24, 2022.
A tensor is a container which can house data in N dimensions, along with its linear operations, though there is nuance in what tensors technically are and what we refer to as tensors in practice.
- A New Way of Managing Deep Learning Datasets - Mar 23, 2022.
Create, version-control, query, and visualize image, audio, and video datasets using Hub 2.0 by Activeloop.
- Risk Management Framework for AI/ML Models - Mar 23, 2022.
How sound risk management acts as a catalyst to building successful AI/ML models.
- DIY Automated Machine Learning with Streamlit - Mar 22, 2022.
In this article, we will create an automated machine learning web app you can actually use.
- Linear vs Logistic Regression: A Succinct Explanation - Mar 21, 2022.
Linear Regression and Logistic Regression are two well-used Machine Learning Algorithms that both branch off from Supervised Learning. Linear Regression is used to solve Regression problems whereas Logistic Regression is used to solve Classification problems. Read more here.
- From Google Colab to a Ploomber Pipeline: ML at Scale with GPUs - Mar 17, 2022.
In this short blog, we’ll review the process of taking a POC data science pipeline (ML/Deep learning/NLP) that was conducted on Google Colab, and transforming it into a pipeline that can run parallel at scale and works with Git so the team can collaborate on.
- Machine Learning Algorithms for Classification - Mar 14, 2022.
In this article, we will be going through the algorithms that can be used for classification tasks.
- The Significance of Data Quality in Making a Successful Machine Learning Model - Mar 10, 2022.
Good quality data becomes imperative and a basic building block of an ML pipeline. The ML model can only be as good as its training data.
- How To Use Synthetic Data To Overcome Data Shortages For Machine Learning Model Training - Mar 9, 2022.
It takes time and considerable resources to collect, document, and clean data before it can be used. But there is a way to address this challenge – by using synthetic data.
- Building a Tractable, Feature Engineering Pipeline for Multivariate Time Series - Mar 8, 2022.
A time series feature engineering pipeline requires different transformations such as imputation and window aggregation, which follows a sequence of stages. This article demonstrates the building of a pipeline to derive multivariate time series features such that the features can then be easily tracked and validated.
- Build a Machine Learning Web App in 5 Minutes - Mar 7, 2022.
In this article, you will learn to export your models and use them outside a Jupyter Notebook environment. You will build a simple web application that is able to feed user input into a machine learning model, and display an output prediction to the user.
- 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Mar 4, 2022.
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.
- What is Adversarial Machine Learning? - Mar 3, 2022.
In the Cybersecurity sector Adversarial machine learning attempts to deceive and trick models by creating unique deceptive inputs, to confuse the model resulting in a malfunction in the model.
- 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.
- Design Patterns in Machine Learning for MLOps - Feb 23, 2022.
This article outlines some of the most common design patterns encountered when creating successful Machine Learning solutions.
- 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.
- Random Forest® vs Decision Tree: Key Differences - Feb 18, 2022.
Check out this reasoned comparison of 2 critical machine learning algorithms to help you better make an informed decision.
- 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.
- 7 Steps to Mastering Machine Learning with Python in 2022 - Feb 1, 2022.
Are you trying to teach yourself machine learning from scratch, but aren’t sure where to start? I will attempt to condense all the resources I’ve used over the years into 7 steps that you can follow to teach yourself machine learning.
- 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.
- What Google Recommends You do Before Taking Their Machine Learning or Data Science Course - Oct 28, 2021.
First steps to learning data science & machine learning are the foundations.
- 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.