Tag: machine
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month February 2026: exchange with others, documentation, and MLOps The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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The Machine Learning Lessons I’ve Learned Last Month
The Machine Learning Lessons I’ve Learned Last Month Delayed January: deadlines, downtimes, and flow times The post The Machine Learning Lessons I’ve Learned Last Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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An efficient, accurate, and interpretable machine learning method for computing probability of failure
An efficient, accurate, and interpretable machine learning method for computing probability of failure arXiv:2601.21089v1 Announce Type: new Abstract: We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex system as determined by a threshold…
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Machine Learning in Production? What This Really Means
Machine Learning in Production? What This Really Means From notebooks to real-world systems The post Machine Learning in Production? What This Really Means appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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How to Build a Neural Machine Translation System for a Low-Resource Language
How to Build a Neural Machine Translation System for a Low-Resource Language An introduction to neural machine translation The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science. Kaixuan Chen Go to original source
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Data Poisoning in Machine Learning: Why and How People Manipulate Training Data
Data Poisoning in Machine Learning: Why and How People Manipulate Training Data Do you know where your data has been? The post Data Poisoning in Machine Learning: Why and How People Manipulate Training Data appeared first on Towards Data Science. Stephanie Kirmer Go to original source
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Drift Detection in Robust Machine Learning Systems
Drift Detection in Robust Machine Learning Systems A prerequisite for long-term success of machine learning systems The post Drift Detection in Robust Machine Learning Systems appeared first on Towards Data Science. Morris Stallmann Go to original source
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The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel
The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not change the destination, only the path. Each method adds a mechanism that fixes a limitation of the previous one, making the movement faster, more stable, or more adaptive.…
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The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel
The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel AUC measures how well a model ranks positives above negatives, independent of any chosen threshold. The post The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel appeared first on Towards Data Science. angela shi Go to original source
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Machine Learning vs AI Engineer: What Are the Differences?
Machine Learning vs AI Engineer: What Are the Differences? One of the most confusing questions in tech right now is: What is the difference between an AI engineer and a machine learning engineer? Both are six-figure jobs, but if you choose the wrong one, you could waste months of your career learning the wrong skills…
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The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel
The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards…
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The Machine Learning “Advent Calendar” Day 23: CNN in Excel
The Machine Learning “Advent Calendar” Day 23: CNN in Excel A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decision is fully visible. The post The Machine Learning “Advent Calendar” Day 23: CNN in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel
The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel Gradient descent in function space with decision trees The post The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel
The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel From Random Ensembles to Optimization: Gradient Boosting Explained The post The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 19: Bagging in Excel
The Machine Learning “Advent Calendar” Day 19: Bagging in Excel Understanding ensemble learning from first principles in Excel The post The Machine Learning “Advent Calendar” Day 19: Bagging in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel
The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel Understanding forward propagation and backpropagation through explicit formulas The post The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel
The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel Kernel SVM often feels abstract, with kernels, dual formulations, and support vectors. In this article, we take a different path. Starting from Kernel Density Estimation, we build Kernel SVM step by step as a sum of local bells, weighted and selected by hinge loss,…
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The Machine Learning “Advent Calendar” Day 15: SVM in Excel
The Machine Learning “Advent Calendar” Day 15: SVM in Excel Instead of starting with margins and geometry, this article builds the Support Vector Machine step by step from familiar models. By changing the loss function and reusing regularization, SVM appears naturally as a linear classifier trained by optimization. This perspective unifies logistic regression, SVM, and…
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The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel
The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss functions, optimization, gradients, scaling, and interpretation. In this article, we rebuild Linear Regression in Excel, compare the closed-form solution with Gradient Descent, and see how the coefficients evolve step…
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The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel
The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point. It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel. But because it relies on one fixed radius,…
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The Machine Learning “Advent Calendar” Day 9: LOF in Excel
The Machine Learning “Advent Calendar” Day 9: LOF in Excel In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of…
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Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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The Machine Learning “Advent Calendar” Day 5: GMM in Excel
The Machine Learning “Advent Calendar” Day 5: GMM in Excel This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general…
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The Machine Learning “Advent Calendar” Day 4: k-Means in Excel
The Machine Learning “Advent Calendar” Day 4: k-Means in Excel How to implement a training algorithm that finally looks like “real” machine learning The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel
The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel From local distance to global probability The post The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel
The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel Exploring the k-NN classifier with its variants and improvements The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month Christmas connections, Copilot’s costs, careful (no-)choices The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning Projects Employers Want to See
The Machine Learning Projects Employers Want to See What machine learning projects will actually get you interviews and jobs The post The Machine Learning Projects Employers Want to See appeared first on Towards Data Science. Egor Howell Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month October 2025: READMEs, MIGs, and movements The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Machine Learning Meets Panel Data: What Practitioners Need to Know
Machine Learning Meets Panel Data: What Practitioners Need to Know How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden data leakage The post Machine Learning Meets Panel Data: What Practitioners Need to Know appeared first on Towards Data Science. Marco Letta Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month September 2025: library or self-made, Ditto and Launchbar, reading widely and deeply The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Implementing the Coffee Machine Project in Python Using Object Oriented Programming
Implementing the Coffee Machine Project in Python Using Object Oriented Programming Understanding classes, objects, attributes, and methods The post Implementing the Coffee Machine Project in Python Using Object Oriented Programming appeared first on Towards Data Science. Mahnoor Javed Go to original source
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How to Become a Machine Learning Engineer (Step-by-Step)
How to Become a Machine Learning Engineer (Step-by-Step) Your one-stop guide to becoming a machine learning engineer The post How to Become a Machine Learning Engineer (Step-by-Step) appeared first on Towards Data Science. Egor Howell Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month August 2025: logging, lab notebooks, overnight runs The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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How to Benchmark Classical Machine Learning Workloads on Google Cloud
How to Benchmark Classical Machine Learning Workloads on Google Cloud Harnessing CPUs for Practical, Cost-Effective Machine Learning The post How to Benchmark Classical Machine Learning Workloads on Google Cloud appeared first on Towards Data Science. Ehssan Khan Go to original source
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Mo’ Memory, Mo’ Problems: Stream-Native Machine Unlearning
Mo’ Memory, Mo’ Problems: Stream-Native Machine Unlearning arXiv:2508.10193v1 Announce Type: new Abstract: Machine unlearning work assumes a static, i.i.d training environment that doesn’t truly exist. Modern ML pipelines need to learn, unlearn, and predict continuously on production streams of data. We translate the notion of the batch unlearning scenario to the online setting using notions…
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How to Design Machine Learning Experiments — the Right Way
How to Design Machine Learning Experiments — the Right Way The key to successful ML projects isn’t always more resources The post How to Design Machine Learning Experiments — the Right Way appeared first on Towards Data Science. TDS Editors Go to original source
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The Machine, the Expert, and the Common Folks
The Machine, the Expert, and the Common Folks A look at noise, consistency and broken legs The post The Machine, the Expert, and the Common Folks appeared first on Towards Data Science. Lars Nørtoft Reiter Go to original source
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From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT
From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT Learn how machine learning can predict network congestion before it happens The post From Reactive to Predictive: Forecasting Network Congestion with Machine Learning and INT appeared first on Towards Data Science. Shireesh Kumar Singh Go to original source
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Topological Machine Learning with Unreduced Persistence Diagrams
Topological Machine Learning with Unreduced Persistence Diagrams arXiv:2507.07156v1 Announce Type: new Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram. Computing persistence diagrams is often the most computationally demanding step in such a pipeline, however. To explore…
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Build Interactive Machine Learning Apps with Gradio
Build Interactive Machine Learning Apps with Gradio Create a fun text-to-speech demo in minutes The post Build Interactive Machine Learning Apps with Gradio appeared first on Towards Data Science. Ehssan Khan Go to original source
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My Honest Advice for Aspiring Machine Learning Engineers
My Honest Advice for Aspiring Machine Learning Engineers What it really takes to become a machine learning engineer The post My Honest Advice for Aspiring Machine Learning Engineers appeared first on Towards Data Science. Egor Howell Go to original source
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Top Machine Learning Jobs and How to Prepare For Them
Top Machine Learning Jobs and How to Prepare For Them These days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles…
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How to Learn the Math Needed for Machine Learning
How to Learn the Math Needed for Machine Learning Maths can be a scary topic for people. Many of you want to work in machine learning, but the maths skills needed may seem overwhelming. I am here to tell you that it’s nowhere as intimidating as you may think and to give you a roadmap, resources,…
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Optimal Transport for Machine Learners
Optimal Transport for Machine Learners arXiv:2505.06589v1 Announce Type: new Abstract: Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning, especially for designing and evaluating generative models. These course notes cover…
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Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning
Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning You see a math formula you don’t immediately understand. Your instinct? Stop reading. Don’t. That’s exactly what I told myself when I started reading Probabilistic Machine Learning – An Introduction by Kevin P. Murphy. And it was absolutely worth it. It changed how I…
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If I Wanted to Become a Machine Learning Engineer, I’d Do This
If I Wanted to Become a Machine Learning Engineer, I’d Do This If I wanted to become a machine learning engineer again, this is the exact process I would follow. Let’s get into it! First become a data scientist or software engineer I’ve said it before, but a machine learning engineer is not exactly an entry-level position.…
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Physics-informed features in supervised machine learning
Physics-informed features in supervised machine learning arXiv:2504.17112v1 Announce Type: new Abstract: Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature-based machine learning typically relies on applying linear regression to standardized features, without considering their physical meaning. This may limit model explainability,…
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A Data Scientist’s Guide to Docker Containers
A Data Scientist’s Guide to Docker Containers For a ML model to be useful it needs to run somewhere. This somewhere is most likely not your local machine. A not-so-good model that runs in a production environment is better than a perfect model that never leaves your local machine. However, the production machine is usually…
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Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments arXiv:2503.06156v1 Announce Type: new Abstract: Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the…
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On-Device Machine Learning in Spatial Computing
On-Device Machine Learning in Spatial Computing The landscape of computing is undergoing a profound transformation with the emergence of spatial computing platforms(VR and AR). As we step into this new era, the intersection of virtual reality, Augmented Reality, and on-device machine learning presents unprecedented opportunities for developers to create experiences that seamlessly blend digital content…
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How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp)
How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp) Machine learning and AI are among the most popular topics nowadays, especially within the tech space. I am fortunate enough to work and develop with these technologies every day as a machine learning engineer! In this article, I will walk you through my…
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Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning
Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning Introduction Data science is undoubtedly one of the most fascinating fields today. Following significant breakthroughs in machine learning about a decade ago, data science has surged in popularity within the tech community. Each year, we witness increasingly powerful tools that once seemed unimaginable. Innovations such as the Transformer…
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How to Log Your Data with MLflow
How to Log Your Data with MLflow MLflow, MLOps, Data Science Mastering data logging in MLOps for your AI workflow Photo by Chris Liverani on Unsplash Preface Data is one of the most critical components of the machine learning process. In fact, the quality of the data used in training a model often determines the success or failure…
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Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly
Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly Sebastian Raschka has helped demystify deep learning for thousands through his books, tutorials and teachings Sebastian Raschka has helped shape how thousands of data scientists and machine learning engineers learn their craft. As a passionate coder and proponent of open-source software,…
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Learnings from a Machine Learning Engineer — Part 4: The Model
Learnings from a Machine Learning Engineer — Part 4: The Model Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Learnings from a Machine Learning Engineer — Part 3: The Evaluation
Learnings from a Machine Learning Engineer — Part 3: The Evaluation Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Learnings from a Machine Learning Engineer — Part 2: The Data Sets
Learnings from a Machine Learning Engineer — Part 2: The Data Sets Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Learnings from a Machine Learning Engineer — Part 1: The Data
Learnings from a Machine Learning Engineer — Part 1: The Data Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Machine Learning: From 0 to Something
Machine Learning: From 0 to Something How I learned ML foundations to tackle a complex problem Continue reading on Towards Data Science » Ricardo Ribas Go to original source
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Partial Dependence Plots: How to Discover Variables Influencing a Model
Partial Dependence Plots: How to Discover Variables Influencing a Model Have you ever wondered how machine learning models are constructed? ‘Explainability of machine learning models’ and ‘machine learning… Continue reading on Towards Data Science » Mythili Krishnan Go to original source
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What are some of the most interesting applied ml papers/blogs you read in 2024 or projects you worked on
What are some of the most interesting applied ml papers/blogs you read in 2024 or projects you worked on I am looking for some interesting successful/unsuccessful real-world machine learning applications. You are also free to share experiences building applications with machine learning that have actually had some real world impact. Something of this type: LinkedIn…
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What Every Aspiring Machine Learning Engineer Must Know to Succeed
What Every Aspiring Machine Learning Engineer Must Know to Succeed Your Guide to Avoiding Critical Errors with Machine Learning in Production Continue reading on Towards Data Science » Claudia Ng Go to original source
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Master Machine Learning: 4 Classification Models Made Simple
Master Machine Learning: 4 Classification Models Made Simple A Beginner’s Guide to Building Models in 15 Practical Steps Continue reading on Towards Data Science » Leo Anello Go to original source
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The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History? arXiv:2411.18656v1 Announce Type: new Abstract: In today’s world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking,…