Forwarded from Machine Learning with Python
This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesβsuch as feature engineering or balancing response variablesβor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
https://dafriedman97.github.io/mlbook/content/introduction.html
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Forwarded from Machine Learning with Python
"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
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π Understanding Convolutional Neural Networks (CNNs) Through Excel
π Category: DEEP LEARNING
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Demystify the 'black box' of deep learning by exploring Convolutional Neural Networks (CNNs) with a surprising tool: Microsoft Excel. This hands-on approach breaks down the fundamental operations of CNNs, such as convolution and pooling layers, into understandable spreadsheet calculations. By visualizing the mechanics step-by-step, this method offers a uniquely intuitive and accessible way to grasp how these powerful neural networks learn and process information, making complex AI concepts tangible for developers and data scientists at any level.
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π Category: DEEP LEARNING
π Date: 2025-11-17 | β±οΈ Read time: 12 min read
Demystify the 'black box' of deep learning by exploring Convolutional Neural Networks (CNNs) with a surprising tool: Microsoft Excel. This hands-on approach breaks down the fundamental operations of CNNs, such as convolution and pooling layers, into understandable spreadsheet calculations. By visualizing the mechanics step-by-step, this method offers a uniquely intuitive and accessible way to grasp how these powerful neural networks learn and process information, making complex AI concepts tangible for developers and data scientists at any level.
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π Category: MACHINE LEARNING
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A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
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π Category: MACHINE LEARNING
π Date: 2025-11-30 | β±οΈ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
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π The Machine Learning βAdvent Calendarβ Day 1: k-NN Regressor in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
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π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
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π The Machine Learning βAdvent Calendarβ Day 2: k-NN Classifier in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-02 | β±οΈ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
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π Category: MACHINE LEARNING
π Date: 2025-12-02 | β±οΈ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
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π The Machine Learning βAdvent Calendarβ Day 3: GNB, LDA and QDA in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
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π Category: MACHINE LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
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π The Machine Learning βAdvent Calendarβ Day 5: GMM in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-05 | β±οΈ Read time: 6 min read
Explore Gaussian Mixture Models (GMM), a powerful clustering algorithm that serves as a natural extension and improvement over k-Means. This guide, part of a Machine Learning Advent Calendar series, uniquely demonstrates how to implement and understand GMMs entirely within Microsoft Excel. It's a practical approach for grasping core ML concepts without requiring a dedicated coding environment, making advanced data science techniques more accessible.
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π Category: MACHINE LEARNING
π Date: 2025-12-05 | β±οΈ Read time: 6 min read
Explore Gaussian Mixture Models (GMM), a powerful clustering algorithm that serves as a natural extension and improvement over k-Means. This guide, part of a Machine Learning Advent Calendar series, uniquely demonstrates how to implement and understand GMMs entirely within Microsoft Excel. It's a practical approach for grasping core ML concepts without requiring a dedicated coding environment, making advanced data science techniques more accessible.
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π The Machine Learning βAdvent Calendarβ Day 4: k-Means in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-04 | β±οΈ Read time: 7 min read
Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice.
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π Category: MACHINE LEARNING
π Date: 2025-12-04 | β±οΈ Read time: 7 min read
Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice.
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