{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Angela Shi & Kezhan Shi",
  "title_listed": "The Machine Learning Advent Calendar Day 22: Embeddings in Excel",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/the-machine-learning-advent-calendar-day-22-embeddings-in-excel/",
  "publish_date": "2025-12-23",
  "corpus_stage": "",
  "notes": "",
  "id": "6ded6a9b2e27",
  "collected_at": "2026-10-02T01:38:54",
  "title": "The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel\nUnderstanding text embeddings through simple models and Excel\n[Angela and Kezhan Shi](https://towardsdatascience.com/author/angela.shi/)\nDecember 22, 20257 min read\n![](../images/000.jpg)\nEmbeddings in Excel\nEmbeddings in Excel\nFor the remaining days of this series, we will talk about **deep learning**.\nAnd when people talk about deep learning, we immediately think of these images of deep neural networks architectures, with many layers, neurons, and parameters.\nIn practice, the real shift introduced by deep learning is elsewhere.\nIt is about **learning data representations**.\nIn this article, we focus on **text embeddings** , explain their role in the machine learning landscape, and show how they can be **understood and explored in Excel**.\n## 1. Classic Machine earning vs. Deep learning\nWe will discuss, in this part, why embedding is introduced.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n### 1.1 Where does deep learning fit?\nTo understand embeddings, we first need to clarify the place of deep learning.\nWe will use the term **classic machine learning** to describe methods that do not rely on deep architectures.\nAll the previous articles deal with classic machine learning, that can be described in two complementary ways.\nLearning paradigms\n  * Supervised learning\n  * Unsupervised learning\n\n\nModel families\n  * Distance-based models\n  * Tree-based models\n  * Weight-based models\n\n\nAcross this series, we have already studied the learning algorithms behind these models. In particular, we have seen that **gradient descent** applies to all weight-based models, from linear regression to neural networks.\nDeep learning is often reduced to neural networks with many layers.\nBut this explanation is incomplete.\nFrom an optimization point of view, deep learning does not introduce a new learning rule.\nSo what does it introduce?\n### 1.2 Deep learning as data representation learning\nDeep learning is about **how features are created**.\nInstead of manually designing features, deep learning **learns representations automatically** , often through multiple successive transformations.\nThis also raises an important conceptual question:\n**Where is the boundary between feature engineering and model learning?**\nSome examples make this clearer:\n  * Polynomial regression is still a linear model, but the features are polynomial\n  * Kernel methods project data into a high-dimensional feature space\n  * Density-based methods implicitly transform the data before learning\n\n\nDeep learning continues this idea, but at scale.\nFrom this perspective, deep learning belongs to:\n  * the **feature engineering philosophy** , for representation\n  * the **weight-based model family** , for learning\n\n\n### 1.3 Images and convolutional neural networks\nImages are represented as **pixels**.\nFrom a technical point of view, image data is already numerical and structured: a grid of numbers. However, the **information** contained in these pixels is not structured in a way that classical models can easily exploit.\nPixels do not explicitly encode: edges, shapes, textures, or objects.\nConvolutional Neural Networks (CNNs) are designed to **create information from pixels**. They apply filters to detect local patterns, then progressively combine them into higher-level representations.\nI have published a [this article](https://towardsdatascience.com/understanding-convolutional-neural-networks-cnns-through-excel/) showing how CNNs can be implemented in Excel to make this process explicit.\n![](../images/001.gif)\nCNN in Excel - all images by author\nFor images, the challenge is **not** to make the data numerical, but to **extract meaningful representations** from already numerical data.\n### 1.4 Text data: a different problem\nText presents a fundamentally different challenge.\nUnlike images, text is **not numerical by nature**.\nBefore modeling context or order, the first problem is more basic:\n**How do we represent words numerically?**\nCreating a numerical representation for text the first step.\nIn deep learning for text, this step is handled by **embeddings**.\nEmbeddings transform discrete symbols (words) into vectors that models can work with. Once embeddings exist, we can then model: context, order and relationships between words.\nIn this article, we focus on this first and essential step:  \n**how embeddings create numerical representations for text** , and how this process can be explored in Excel.\n## 2. Two ways to learn text embeddings\nIn this article, we will use the **IMDB movie reviews dataset** to illustrate both approaches. The dataset is distributed under the Apache License 2.0.\nThere are two main ways to learn embeddings for text, and we will do both with this dataset:\n  * supervised: we will create embeddings to predict the sentiment\n  * unsupervised or self-supervised: we will use word2vec algorithm\n\n\nIn both cases, the goal is the same:  \nto transform words into numerical vectors that can be used by machine learning models.\nIf you test [**UNDERSTAND_ML**](https://ko-fi.com/angelashi/link/UNDERSTAND_ML), you may notice a difference.\nBefore comparing these two approaches, we first need to clarify what embeddings are and how they relate to classic machine learning.\n![](../images/body-29b44394cffd040085e2.png)\nIMDB dataset image by author -**Apache License 2.0**\n### 2.1 Embeddings and classic machine learning\nIn classic machine learning, categorical data is usually handled with:\n  * **label encoding** , which assigns fixed integers but introduces artificial order\n  * **one-hot encoding** , which removes order but produces high-dimensional sparse vectors\n\n\nHow they can be used depend on the nature of the models.\n**Distance-based models** cannot effectively use one-hot encoding, because all categories end up being equally distant from each other. Label encoding could work only if we can attribute meaningful numerical values for the categories, which is generally not the case in classic models.\n**Weight-based models** can use one-hot encoding, because the model learns a weight for each category. In contrast, with label encoding, the numerical values are fixed and cannot be adjusted to represent meaningful relationships.\n**Tree-based models** treat all variables as categorical splits rather than numerical magnitudes, which makes label encoding acceptable in practice. However, most implementations, including scikit-learn, still require numerical inputs. As a result, categories must be converted to numbers, either through label encoding or one-hot encoding. If the numerical values carried semantic meaning, this would again be beneficial.\nOverall, this highlights a limitation of classic approaches:  \n**category values are fixed and not learned**.\nEmbeddings extend this idea by **learning the representation itself**.  \nEach word is associated with a trainable vector, turning the representation of categories into a learning problem rather than a preprocessing step.\n### 2.2 Supervised embeddings\nIn supervised learning, embeddings are learned as part of a prediction task.\nFor example, the IMDB dataset has labels about the in sentiment analysis. So we can create a very simple architecture: \nIn our case, we can use a very simple architecture: each word is mapped to a **one-dimensional embedding**\nThis is possible because the objective is binary sentiment classification.\n![](../images/body-5a08e42300d9e701a925.png)\nOnce training is complete, we can **export the embeddings** and explore them in Excel.\nWhen plotting the embeddings on the x-axis and word frequency on the y-axis, a clear pattern appears:\n  * positive values are associated with words such as _excellent_ or _wonderful_ ,\n  * negative values are associated with words such as _worst_ or _waste_\n\n\nDepending on the initialization, the sign can be inverted, since the logistic regression layer also has parameters that influence the final prediction.\n![](../images/body-d2d1018c00bcccc5664a.png)\nFinally, in Excel, we reconstruct the full pipeline that corresponds to the architecture we define early.\n**Input column**  \nThe input text (a review) is cut into words, and each row corresponds to one word.\n**Embedding search**  \nUsing a lookup function, the embedding value associated with each word is retrieved from the embedding table learned during training.\n**Global average**  \nThe global average embedding is computed by averaging the embeddings of all words seen so far. This corresponds to a very simple sentence representation: the mean of word vectors.\n**Probability prediction**  \nThe averaged embedding is then passed through a logistic function to produce a sentiment probability.\n![](../images/body-3410cc087b73e73fad11.png)\nWhat we observe\n  * Words with strongly **positive embeddings** (for example _excellent_ , _love_ , _fun_) push the average upward.\n  * Words with strongly **negative embeddings** (for example _worst_ , _horrible_ , _waste_) pull the average downward.\n  * Neutral or weakly weighted words have little influence.\n\n\nAs more words are added, the global average embedding stabilizes, and the sentiment prediction becomes more confident.\n### 2.3 Word2Vec: embeddings from co-occurrence\nIn Word2Vec, **similarity does not mean that two words have the same meaning**.  \nIt means that they **appear in similar contexts**.\nWord2Vec learns word embeddings by looking at **which words tend to co-occur** within a fixed window in the text. Two words are considered similar if they often appear **around the same neighboring words** , even if their meanings are opposite.\nAs shown in the Excel sheet below, we compute the cosine similarity for the word _good_ and retrieve the most similar words.\n![](../images/body-8eccc79175f42edb65c4.png)\nFrom the model’s perspective, the surrounding words are almost identical. The only thing that changes is the adjective itself.\nAs a result, Word2Vec learns that **“good” and “bad” play a similar role in language** , even though their meanings are opposite.\nSo, Word2Vec captures **distributional similarity** , not semantic polarity.\nA useful way to think about it is:\n_Words are close if they are used in the same places._\n### 2.4 How embeddings are used\nIn modern systems such as **RAG (Retrieval-Augmented Generation)** , embeddings are often used to retrieve documents or passages for question answering.\nHowever, this approach has limitations.\nMost commonly used embeddings are trained in a **self-supervised** way, based on co-occurrence or contextual prediction objectives. As a result, they capture general language similarity, not task-specific meaning.\nThis means that:\n  * embeddings may retrieve text that is linguistically similar but **not relevant**\n  * semantic proximity does not guarantee **answer correctness**\n\n\nOther embedding strategies can be used, including task-adapted or supervised embeddings, but they often remain self-supervised at their core.\nUnderstanding how embeddings are created, what they encode, and what they do not encode is therefore essential before using them in downstream systems such as RAG.\n## Conclusion\nEmbeddings are learned numerical representations of words that make similarity measurable.\nWhether learned through supervision or through co-occurrence, embeddings map words to vectors based on how they are used in data. By exporting them to Excel, we can inspect these representations directly, compute similarities, and understand what they capture and what they do not.\nThis makes embeddings less mysterious and clarifies their role as a foundation for more complex systems such as retrieval or RAG.\n···\nAll the Excel files are available through this [Kofi link](https://ko-fi.com/s/4ddca6dff1). Your support means a lot to me. The price will increase during the month, so early supporters get the best value.\n![](../images/body-428dd0533eb4950db3bd.png)\nAll Excel/Google sheet files for ML and DL\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel\nUnderstanding text embeddings through simple models and Excel\n[Angela and Kezhan Shi](https://towardsdatascience.com/author/angela.shi/)\nDecember 22, 20257 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/embeddings-in-excel.jpg)\nEmbeddings in Excel\nEmbeddings in Excel\nFor the remaining days of this series, we will talk about **deep learning**.\nAnd when people talk about deep learning, we immediately think of these images of deep neural networks architectures, with many layers, neurons, and parameters.\nIn practice, the real shift introduced by deep learning is elsewhere.\nIt is about **learning data representations**.\nIn this article, we focus on **text embeddings** , explain their role in the machine learning landscape, and show how they can be **understood and explored in Excel**.\n## 1. Classic Machine earning vs. Deep learning\nWe will discuss, in this part, why embedding is introduced.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n### 1.1 Where does deep learning fit?\nTo understand embeddings, we first need to clarify the place of deep learning.\nWe will use the term **classic machine learning** to describe methods that do not rely on deep architectures.\nAll the previous articles deal with classic machine learning, that can be described in two complementary ways.\nLearning paradigms\n  * Supervised learning\n  * Unsupervised learning\n\n\nModel families\n  * Distance-based models\n  * Tree-based models\n  * Weight-based models\n\n\nAcross this series, we have already studied the learning algorithms behind these models. In particular, we have seen that **gradient descent** applies to all weight-based models, from linear regression to neural networks.\nDeep learning is often reduced to neural networks with many layers.\nBut this explanation is incomplete.\nFrom an optimization point of view, deep learning does not introduce a new learning rule.\nSo what does it introduce?\n### 1.2 Deep learning as data representation learning\nDeep learning is about **how features are created**.\nInstead of manually designing features, deep learning **learns representations automatically** , often through multiple successive transformations.\nThis also raises an important conceptual question:\n**Where is the boundary between feature engineering and model learning?**\nSome examples make this clearer:\n  * Polynomial regression is still a linear model, but the features are polynomial\n  * Kernel methods project data into a high-dimensional feature space\n  * Density-based methods implicitly transform the data before learning\n\n\nDeep learning continues this idea, but at scale.\nFrom this perspective, deep learning belongs to:\n  * the **feature engineering philosophy** , for representation\n  * the **weight-based model family** , for learning\n\n\n### 1.3 Images and convolutional neural networks\nImages are represented as **pixels**.\nFrom a technical point of view, image data is already numerical and structured: a grid of numbers. However, the **information** contained in these pixels is not structured in a way that classical models can easily exploit.\nPixels do not explicitly encode: edges, shapes, textures, or objects.\nConvolutional Neural Networks (CNNs) are designed to **create information from pixels**. They apply filters to detect local patterns, then progressively combine them into higher-level representations.\nI have published a [this article](https://towardsdatascience.com/understanding-convolutional-neural-networks-cnns-through-excel/) showing how CNNs can be implemented in Excel to make this process explicit.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/cnn-excel-digits-1024x394.gif)\nCNN in Excel - all images by author\nFor images, the challenge is **not** to make the data numerical, but to **extract meaningful representations** from already numerical data.\n### 1.4 Text data: a different problem\nText presents a fundamentally different challenge.\nUnlike images, text is **not numerical by nature**.\nBefore modeling context or order, the first problem is more basic:\n**How do we represent words numerically?**\nCreating a numerical representation for text the first step.\nIn deep learning for text, this step is handled by **embeddings**.\nEmbeddings transform discrete symbols (words) into vectors that models can work with. Once embeddings exist, we can then model: context, order and relationships between words.\nIn this article, we focus on this first and essential step:  \n**how embeddings create numerical representations for text** , and how this process can be explored in Excel.\n## 2. Two ways to learn text embeddings\nIn this article, we will use the **IMDB movie reviews dataset** to illustrate both approaches. The dataset is distributed under the Apache License 2.0.\nThere are two main ways to learn embeddings for text, and we will do both with this dataset:\n  * supervised: we will create embeddings to predict the sentiment\n  * unsupervised or self-supervised: we will use word2vec algorithm\n\n\nIn both cases, the goal is the same:  \nto transform words into numerical vectors that can be used by machine learning models.\nIf you test [**UNDERSTAND_ML**](https://ko-fi.com/angelashi/link/UNDERSTAND_ML), you may notice a difference.\nBefore comparing these two approaches, we first need to clarify what embeddings are and how they relate to classic machine learning.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-401-1024x489.png)\nIMDB dataset image by author -**Apache License 2.0**\n### 2.1 Embeddings and classic machine learning\nIn classic machine learning, categorical data is usually handled with:\n  * **label encoding** , which assigns fixed integers but introduces artificial order\n  * **one-hot encoding** , which removes order but produces high-dimensional sparse vectors\n\n\nHow they can be used depend on the nature of the models.\n**Distance-based models** cannot effectively use one-hot encoding, because all categories end up being equally distant from each other. Label encoding could work only if we can attribute meaningful numerical values for the categories, which is generally not the case in classic models.\n**Weight-based models** can use one-hot encoding, because the model learns a weight for each category. In contrast, with label encoding, the numerical values are fixed and cannot be adjusted to represent meaningful relationships.\n**Tree-based models** treat all variables as categorical splits rather than numerical magnitudes, which makes label encoding acceptable in practice. However, most implementations, including scikit-learn, still require numerical inputs. As a result, categories must be converted to numbers, either through label encoding or one-hot encoding. If the numerical values carried semantic meaning, this would again be beneficial.\nOverall, this highlights a limitation of classic approaches:  \n**category values are fixed and not learned**.\nEmbeddings extend this idea by **learning the representation itself**.  \nEach word is associated with a trainable vector, turning the representation of categories into a learning problem rather than a preprocessing step.\n### 2.2 Supervised embeddings\nIn supervised learning, embeddings are learned as part of a prediction task.\nFor example, the IMDB dataset has labels about the in sentiment analysis. So we can create a very simple architecture: \nIn our case, we can use a very simple architecture: each word is mapped to a **one-dimensional embedding**\nThis is possible because the objective is binary sentiment classification.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-405.png)\nOnce training is complete, we can **export the embeddings** and explore them in Excel.\nWhen plotting the embeddings on the x-axis and word frequency on the y-axis, a clear pattern appears:\n  * positive values are associated with words such as _excellent_ or _wonderful_ ,\n  * negative values are associated with words such as _worst_ or _waste_\n\n\nDepending on the initialization, the sign can be inverted, since the logistic regression layer also has parameters that influence the final prediction.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-403-1024x418.png)\nFinally, in Excel, we reconstruct the full pipeline that corresponds to the architecture we define early.\n**Input column**  \nThe input text (a review) is cut into words, and each row corresponds to one word.\n**Embedding search**  \nUsing a lookup function, the embedding value associated with each word is retrieved from the embedding table learned during training.\n**Global average**  \nThe global average embedding is computed by averaging the embeddings of all words seen so far. This corresponds to a very simple sentence representation: the mean of word vectors.\n**Probability prediction**  \nThe averaged embedding is then passed through a logistic function to produce a sentiment probability.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-404-1024x563.png)\nWhat we observe\n  * Words with strongly **positive embeddings** (for example _excellent_ , _love_ , _fun_) push the average upward.\n  * Words with strongly **negative embeddings** (for example _worst_ , _horrible_ , _waste_) pull the average downward.\n  * Neutral or weakly weighted words have little influence.\n\n\nAs more words are added, the global average embedding stabilizes, and the sentiment prediction becomes more confident.\n### 2.3 Word2Vec: embeddings from co-occurrence\nIn Word2Vec, **similarity does not mean that two words have the same meaning**.  \nIt means that they **appear in similar contexts**.\nWord2Vec learns word embeddings by looking at **which words tend to co-occur** within a fixed window in the text. Two words are considered similar if they often appear **around the same neighboring words** , even if their meanings are opposite.\nAs shown in the Excel sheet below, we compute the cosine similarity for the word _good_ and retrieve the most similar words.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-400-1024x583.png)\nFrom the model’s perspective, the surrounding words are almost identical. The only thing that changes is the adjective itself.\nAs a result, Word2Vec learns that **“good” and “bad” play a similar role in language** , even though their meanings are opposite.\nSo, Word2Vec captures **distributional similarity** , not semantic polarity.\nA useful way to think about it is:\n_Words are close if they are used in the same places._\n### 2.4 How embeddings are used\nIn modern systems such as **RAG (Retrieval-Augmented Generation)** , embeddings are often used to retrieve documents or passages for question answering.\nHowever, this approach has limitations.\nMost commonly used embeddings are trained in a **self-supervised** way, based on co-occurrence or contextual prediction objectives. As a result, they capture general language similarity, not task-specific meaning.\nThis means that:\n  * embeddings may retrieve text that is linguistically similar but **not relevant**\n  * semantic proximity does not guarantee **answer correctness**\n\n\nOther embedding strategies can be used, including task-adapted or supervised embeddings, but they often remain self-supervised at their core.\nUnderstanding how embeddings are created, what they encode, and what they do not encode is therefore essential before using them in downstream systems such as RAG.\n## Conclusion\nEmbeddings are learned numerical representations of words that make similarity measurable.\nWhether learned through supervision or through co-occurrence, embeddings map words to vectors based on how they are used in data. By exporting them to Excel, we can inspect these representations directly, compute similarities, and understand what they capture and what they do not.\nThis makes embeddings less mysterious and clarifies their role as a foundation for more complex systems such as retrieval or RAG.\n···\nAll the Excel files are available through this [Kofi link](https://ko-fi.com/s/4ddca6dff1). Your support means a lot to me. The price will increase during the month, so early supporters get the best value.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/11/image-205-1024x348.png)\nAll Excel/Google sheet files for ML and DL\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657699750_yw6uqg.jpeg)\nWritten By\n## [Angela and Kezhan Shi](https://towardsdatascience.com/author/angela.shi/)\n[See all from Angela and Kezhan Shi](https://towardsdatascience.com/author/angela.shi/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Deep Learning](https://towardsdatascience.com/tag/deep-learning/)[Text Embedding](https://towardsdatascience.com/tag/text-embedding/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fthe-machine-learning-advent-calendar-day-22-embeddings-in-excel%2F&title=The%20Machine%20Learning%20%E2%80%9CAdvent%20Calendar%E2%80%9D%20Day%2022%3A%20Embeddings%20in%20Excel)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fthe-machine-learning-advent-calendar-day-22-embeddings-in-excel%2F&title=The%20Machine%20Learning%20%E2%80%9CAdvent%20Calendar%E2%80%9D%20Day%2022%3A%20Embeddings%20in%20Excel)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fthe-machine-learning-advent-calendar-day-22-embeddings-in-excel%2F&text=The%20Machine%20Learning%20%E2%80%9CAdvent%20Calendar%E2%80%9D%20Day%2022%3A%20Embeddings%20in%20Excel)\n\n\nTowards Data Science is a community publication. 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