🕷️ Crawler Inspector

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Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 95 (from laksa023)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa095.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://developers.google.com/machine-learning/crash-course\')), getAhrefsUnparsedNoserviceFromURL(\'https://developers.google.com/machine-learning/crash-course\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/developers.google.com\/machine-learning\/crash-course","crawl_time":1770438377,"first_indexed_time":0,"http_code":200,"src_unparsed":"com,google!developers,\/machine-learning\/crash-course s443","src_root_hash":"744624608793826895","history_drop_reason":null,"meta_title":"Machine Learning | Google for Developers","meta_descriptions":[],"attrs_boilerpipe_text":"Skip to main content\nMachine Learning Crash Course\nGoogle's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises.\nWhat's new in Machine Learning Crash Course?\nSince 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC.\nCourse Modules\nEach Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below.\nML Models\nThese modules cover the fundamentals of building regression and classification\n   models.\nLinear Regression\nAn introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning.\nLogistic Regression\nAn introduction to logistic regression, where ML models are designed to predict the probability of a given outcome.\nClassification\nAn introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC.\nData\nThese modules cover fundamental techniques and best practices for working with machine learning data.\nWorking with Categorical Data\nLearn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses.\nDatasets, Generalization, and Overfitting\nAn introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model.\nAdvanced ML models\nThese modules cover advanced ML model architectures.\nNeural Networks\nAn introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions.\nEmbeddings\nLearn how embeddings allow you to do machine learning on large feature vectors.\nNew\nIntro to Large Language Models\nAn introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained.\nReal-world ML\nThese modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering.\nProduction ML Systems\nLearn how a machine learning production system works across a breadth of components.\nNew\nAutoML\nLearn principles and best practices for using automated machine learning.\nML Fairness\nLearn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data.\n[[[\"Easy to understand\",\"easyToUnderstand\",\"thumb-up\"],[\"Solved my problem\",\"solvedMyProblem\",\"thumb-up\"],[\"Other\",\"otherUp\",\"thumb-up\"]],[[\"Missing the information I need\",\"missingTheInformationINeed\",\"thumb-down\"],[\"Too complicated \/ too many steps\",\"tooComplicatedTooManySteps\",\"thumb-down\"],[\"Out of date\",\"outOfDate\",\"thumb-down\"],[\"Samples \/ code issue\",\"samplesCodeIssue\",\"thumb-down\"],[\"Other\",\"otherDown\",\"thumb-down\"]],[],[],[]]","attrs_markdown":"[Skip to main content](https:\/\/developers.google.com\/machine-learning\/crash-course#main-content)\n\n- [Machine Learning](https:\/\/developers.google.com\/machine-learning)\n\n[ML Concepts](https:\/\/developers.google.com\/machine-learning\/crash-course)\n\n- Introduction\n- [Introduction to ML](https:\/\/developers.google.com\/machine-learning\/intro-to-ml)\n\n- ML models\n- [Linear regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression)\n- [Logistic regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/logistic-regression)\n- [Classification](https:\/\/developers.google.com\/machine-learning\/crash-course\/classification)\n\n- Data\n- [Working with numerical data](https:\/\/developers.google.com\/machine-learning\/crash-course\/numerical-data)\n- [Working with categorical data](https:\/\/developers.google.com\/machine-learning\/crash-course\/categorical-data)\n- [Datasets, generalization, and overfitting](https:\/\/developers.google.com\/machine-learning\/crash-course\/overfitting)\n\n- Advanced ML models\n- [Neural networks](https:\/\/developers.google.com\/machine-learning\/crash-course\/neural-networks)\n- [Embeddings](https:\/\/developers.google.com\/machine-learning\/crash-course\/embeddings)\n- [Intro to Large Language Models](https:\/\/developers.google.com\/machine-learning\/crash-course\/llm)\n\n- Real-world ML\n- [Production ML systems](https:\/\/developers.google.com\/machine-learning\/crash-course\/production-ml-systems)\n- [Automated machine learning](https:\/\/developers.google.com\/machine-learning\/crash-course\/automl)\n- [Fairness](https:\/\/developers.google.com\/machine-learning\/crash-course\/fairness)\n\nMore\n\n[Foundational courses](https:\/\/developers.google.com\/machine-learning\/foundational-courses)\n\n[Advanced courses](https:\/\/developers.google.com\/machine-learning\/advanced-courses)\n\n[Guides](https:\/\/developers.google.com\/machine-learning\/guides)\n\n[Glossary](https:\/\/developers.google.com\/machine-learning\/glossary)\n\n- [All terms](https:\/\/developers.google.com\/machine-learning\/glossary)\n- [Clustering](https:\/\/developers.google.com\/machine-learning\/glossary\/clustering)\n- [Decision Forests](https:\/\/developers.google.com\/machine-learning\/glossary\/df)\n- [Fundamentals](https:\/\/developers.google.com\/machine-learning\/glossary\/fundamentals)\n- [GCP](https:\/\/developers.google.com\/machine-learning\/glossary\/googlecloud)\n- [Generative AI](https:\/\/developers.google.com\/machine-learning\/glossary\/generative)\n- [Metrics](https:\/\/developers.google.com\/machine-learning\/glossary\/metrics)\n- [Responsible AI](https:\/\/developers.google.com\/machine-learning\/glossary\/responsible-ai)\n- [TensorFlow](https:\/\/developers.google.com\/machine-learning\/glossary\/tensorflow)\n\n- [English](https:\/\/developers.google.com\/machine-learning\/crash-course)\n- [Deutsch](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=de)\n- [Español](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=es)\n- [Español – América Latina](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=es-419)\n- [Français](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=fr)\n- [Indonesia](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=id)\n- [Italiano](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=it)\n- [Polski](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=pl)\n- [Português – Brasil](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=pt-br)\n- [Tiếng Việt](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=vi)\n- [Türkçe](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=tr)\n- [Русский](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=ru)\n- [Українська](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=uk)\n- [עברית](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=he)\n- [العربيّة](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=ar)\n- [فارسی](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=fa)\n- [हिंदी](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=hi)\n- [বাংলা](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=bn)\n- [ภาษาไทย](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=th)\n- [中文 – 简体](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=zh-cn)\n- [中文 – 繁體](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=zh-tw)\n- [日本語](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=ja)\n- [한국어](https:\/\/developers.google.com\/machine-learning\/crash-course?hl=ko)\n\n[Sign in](https:\/\/developers.google.com\/_d\/signin?continue=https%3A%2F%2Fdevelopers.google.com%2Fmachine-learning%2Fcrash-course&prompt=select_account)\n\n[Home](https:\/\/developers.google.com\/machine-learning\/crash-course)\n\n[Crash Course](https:\/\/developers.google.com\/machine-learning\/crash-course\/prereqs-and-prework)\n\nMore\n\n- [Machine Learning](https:\/\/developers.google.com\/machine-learning)\n\n- [ML Concepts](https:\/\/developers.google.com\/machine-learning\/crash-course)\n  - More\n  - [Home](https:\/\/developers.google.com\/machine-learning\/crash-course)\n  - [Crash Course](https:\/\/developers.google.com\/machine-learning\/crash-course\/prereqs-and-prework)\n- [Foundational courses](https:\/\/developers.google.com\/machine-learning\/foundational-courses)\n- [Advanced courses](https:\/\/developers.google.com\/machine-learning\/advanced-courses)\n- [Guides](https:\/\/developers.google.com\/machine-learning\/guides)\n- [Glossary](https:\/\/developers.google.com\/machine-learning\/glossary)\n  - More\n\n- Introduction\n- [Introduction to ML](https:\/\/developers.google.com\/machine-learning\/intro-to-ml)\n- ML models\n- [Linear regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression)\n- [Logistic regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/logistic-regression)\n- [Classification](https:\/\/developers.google.com\/machine-learning\/crash-course\/classification)\n- Data\n- [Working with numerical data](https:\/\/developers.google.com\/machine-learning\/crash-course\/numerical-data)\n- [Working with categorical data](https:\/\/developers.google.com\/machine-learning\/crash-course\/categorical-data)\n- [Datasets, generalization, and overfitting](https:\/\/developers.google.com\/machine-learning\/crash-course\/overfitting)\n- Advanced ML models\n- [Neural networks](https:\/\/developers.google.com\/machine-learning\/crash-course\/neural-networks)\n- [Embeddings](https:\/\/developers.google.com\/machine-learning\/crash-course\/embeddings)\n- [Intro to Large Language Models](https:\/\/developers.google.com\/machine-learning\/crash-course\/llm)\n- Real-world ML\n- [Production ML systems](https:\/\/developers.google.com\/machine-learning\/crash-course\/production-ml-systems)\n- [Automated machine learning](https:\/\/developers.google.com\/machine-learning\/crash-course\/automl)\n- [Fairness](https:\/\/developers.google.com\/machine-learning\/crash-course\/fairness)\n\n- [All terms](https:\/\/developers.google.com\/machine-learning\/glossary)\n- [Clustering](https:\/\/developers.google.com\/machine-learning\/glossary\/clustering)\n- [Decision Forests](https:\/\/developers.google.com\/machine-learning\/glossary\/df)\n- [Fundamentals](https:\/\/developers.google.com\/machine-learning\/glossary\/fundamentals)\n- [GCP](https:\/\/developers.google.com\/machine-learning\/glossary\/googlecloud)\n- [Generative AI](https:\/\/developers.google.com\/machine-learning\/glossary\/generative)\n- [Metrics](https:\/\/developers.google.com\/machine-learning\/glossary\/metrics)\n- [Responsible AI](https:\/\/developers.google.com\/machine-learning\/glossary\/responsible-ai)\n- [TensorFlow](https:\/\/developers.google.com\/machine-learning\/glossary\/tensorflow)\n\n- [Home](https:\/\/developers.google.com\/)\n- [Products](https:\/\/developers.google.com\/products)\n- [Machine Learning](https:\/\/developers.google.com\/machine-learning)\n- [ML Concepts](https:\/\/developers.google.com\/machine-learning\/crash-course)\n\nSend feedback\n\n## Machine Learning Crash Course\nGoogle's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises.\n\n[Start Crash Course](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression) [Browse course modules](https:\/\/developers.google.com\/machine-learning\/crash-course#course-modules) [View prerequisites](https:\/\/developers.google.com\/machine-learning\/crash-course\/prereqs-and-prework) [Help Center](https:\/\/support.google.com\/machinelearningeducation)\n\n### What's new in Machine Learning Crash Course?\nSince 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC.\n\n## Course Modules\nEach Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below.\n\n## ML Models\nThese modules cover the fundamentals of building regression and classification models.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Linear_Regression_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression)\n\n### [Linear Regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression)\nAn introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Logistic_Regression_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/logistic-regression)\n\n### [Logistic Regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/logistic-regression)\nAn introduction to logistic regression, where ML models are designed to predict the probability of a given outcome.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Classification_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/classification)\n\n### [Classification](https:\/\/developers.google.com\/machine-learning\/crash-course\/classification)\nAn introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC.\n\n## Data\nThese modules cover fundamental techniques and best practices for working with machine learning data.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Numerical_Data_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/numerical-data)\n\n### [Working with Numerical Data](https:\/\/developers.google.com\/machine-learning\/crash-course\/numerical-data)\nLearn how to analyze and transform numerical data to help train ML models more effectively.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Categorical_Data_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/categorical-data)\n\n### [Working with Categorical Data](https:\/\/developers.google.com\/machine-learning\/crash-course\/categorical-data)\nLearn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Generalization_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/overfitting)\n\n### [Datasets, Generalization, and Overfitting](https:\/\/developers.google.com\/machine-learning\/crash-course\/overfitting)\nAn introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model.\n\n## Advanced ML models\nThese modules cover advanced ML model architectures.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Neural_Nets_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/neural-networks)\n\n### [Neural Networks](https:\/\/developers.google.com\/machine-learning\/crash-course\/neural-networks)\nAn introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Embedding_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/embeddings)\n\n### [Embeddings](https:\/\/developers.google.com\/machine-learning\/crash-course\/embeddings)\nLearn how embeddings allow you to do machine learning on large feature vectors.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Large_Lanuage_Model_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/llm)\n\nNew\n\n### [Intro to Large Language Models](https:\/\/developers.google.com\/machine-learning\/crash-course\/llm)\nAn introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained.\n\n## Real-world ML\nThese modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Production_ML_Systems_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/production-ml-systems)\n\n### [Production ML Systems](https:\/\/developers.google.com\/machine-learning\/crash-course\/production-ml-systems)\nLearn how a machine learning production system works across a breadth of components.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Auto_ML_Yellow_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/automl)\n\nNew\n\n### [AutoML](https:\/\/developers.google.com\/machine-learning\/crash-course\/automl)\nLearn principles and best practices for using automated machine learning.\n\n[![](https:\/\/developers.google.com\/static\/machine-learning\/crash-course\/images\/MLCC_Fairness_no_text.png)](https:\/\/developers.google.com\/machine-learning\/crash-course\/fairness)\n\n### [ML Fairness](https:\/\/developers.google.com\/machine-learning\/crash-course\/fairness)\nLearn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data.\n\nNeed to tell us more?\n\n\\[\\[\\[\"Easy to understand\",\"easyToUnderstand\",\"thumb-up\"\\],\\[\"Solved my problem\",\"solvedMyProblem\",\"thumb-up\"\\],\\[\"Other\",\"otherUp\",\"thumb-up\"\\]\\],\\[\\[\"Missing the information I need\",\"missingTheInformationINeed\",\"thumb-down\"\\],\\[\"Too complicated \/ too many steps\",\"tooComplicatedTooManySteps\",\"thumb-down\"\\],\\[\"Out of date\",\"outOfDate\",\"thumb-down\"\\],\\[\"Samples \/ code issue\",\"samplesCodeIssue\",\"thumb-down\"\\],\\[\"Other\",\"otherDown\",\"thumb-down\"\\]\\],\\[\\],\\[\\],\\[\\]\\]\n\n- ### Connect\n  - 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We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC.\n\n## Course Modules\nEach Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below.\n\n## ML Models\nThese modules cover the fundamentals of building regression and classification models.\n\n### [Linear Regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/linear-regression)\nAn introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning.\n\n### [Logistic Regression](https:\/\/developers.google.com\/machine-learning\/crash-course\/logistic-regression)\nAn introduction to logistic regression, where ML models are designed to predict the probability of a given outcome.\n\n### [Classification](https:\/\/developers.google.com\/machine-learning\/crash-course\/classification)\nAn introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC.\n\n## Data\nThese modules cover fundamental techniques and best practices for working with machine learning data.\n\n### [Working with Categorical Data](https:\/\/developers.google.com\/machine-learning\/crash-course\/categorical-data)\nLearn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses.\n\n### [Datasets, Generalization, and Overfitting](https:\/\/developers.google.com\/machine-learning\/crash-course\/overfitting)\nAn introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model.\n\n## Advanced ML models\nThese modules cover advanced ML model architectures.\n\n### [Neural Networks](https:\/\/developers.google.com\/machine-learning\/crash-course\/neural-networks)\nAn introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions.\n\n### [Embeddings](https:\/\/developers.google.com\/machine-learning\/crash-course\/embeddings)\nLearn how embeddings allow you to do machine learning on large feature vectors.\n\nNew\n\n### [Intro to Large Language Models](https:\/\/developers.google.com\/machine-learning\/crash-course\/llm)\nAn introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained.\n\n## Real-world ML\nThese modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering.\n\n### [Production ML Systems](https:\/\/developers.google.com\/machine-learning\/crash-course\/production-ml-systems)\nLearn how a machine learning production system works across a breadth of components.\n\nNew\n\n### [AutoML](https:\/\/developers.google.com\/machine-learning\/crash-course\/automl)\nLearn principles and best practices for using automated machine learning.\n\n### [ML Fairness](https:\/\/developers.google.com\/machine-learning\/crash-course\/fairness)\nLearn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data.\n\n\\[\\[\\[\"Easy to understand\",\"easyToUnderstand\",\"thumb-up\"\\],\\[\"Solved my problem\",\"solvedMyProblem\",\"thumb-up\"\\],\\[\"Other\",\"otherUp\",\"thumb-up\"\\]\\],\\[\\[\"Missing the information I need\",\"missingTheInformationINeed\",\"thumb-down\"\\],\\[\"Too complicated \/ too many steps\",\"tooComplicatedTooManySteps\",\"thumb-down\"\\],\\[\"Out of date\",\"outOfDate\",\"thumb-down\"\\],\\[\"Samples \/ code issue\",\"samplesCodeIssue\",\"thumb-down\"\\],\\[\"Other\",\"otherDown\",\"thumb-down\"\\]\\],\\[\\],\\[\\],\\[\\]\\]","meta_canonical":null}

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Boilerpipe Text	Skip to main content Machine Learning Crash Course Google's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises. What's new in Machine Learning Crash Course? Since 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC. Course Modules Each Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below. ML Models These modules cover the fundamentals of building regression and classification models. Linear Regression An introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning. Logistic Regression An introduction to logistic regression, where ML models are designed to predict the probability of a given outcome. Classification An introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC. Data These modules cover fundamental techniques and best practices for working with machine learning data. Working with Categorical Data Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses. Datasets, Generalization, and Overfitting An introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model. Advanced ML models These modules cover advanced ML model architectures. Neural Networks An introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions. Embeddings Learn how embeddings allow you to do machine learning on large feature vectors. New Intro to Large Language Models An introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained. Real-world ML These modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering. Production ML Systems Learn how a machine learning production system works across a breadth of components. New AutoML Learn principles and best practices for using automated machine learning. ML Fairness Learn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data. [[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[]]
Markdown	[Skip to main content](https://developers.google.com/machine-learning/crash-course#main-content) - [Machine Learning](https://developers.google.com/machine-learning) [ML Concepts](https://developers.google.com/machine-learning/crash-course) - Introduction - [Introduction to ML](https://developers.google.com/machine-learning/intro-to-ml) - ML models - [Linear regression](https://developers.google.com/machine-learning/crash-course/linear-regression) - [Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression) - [Classification](https://developers.google.com/machine-learning/crash-course/classification) - Data - [Working with numerical data](https://developers.google.com/machine-learning/crash-course/numerical-data) - [Working with categorical data](https://developers.google.com/machine-learning/crash-course/categorical-data) - [Datasets, generalization, and overfitting](https://developers.google.com/machine-learning/crash-course/overfitting) - Advanced ML models - [Neural networks](https://developers.google.com/machine-learning/crash-course/neural-networks) - [Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings) - [Intro to Large Language Models](https://developers.google.com/machine-learning/crash-course/llm) - Real-world ML - [Production ML systems](https://developers.google.com/machine-learning/crash-course/production-ml-systems) - [Automated machine learning](https://developers.google.com/machine-learning/crash-course/automl) - [Fairness](https://developers.google.com/machine-learning/crash-course/fairness) More [Foundational courses](https://developers.google.com/machine-learning/foundational-courses) [Advanced courses](https://developers.google.com/machine-learning/advanced-courses) [Guides](https://developers.google.com/machine-learning/guides) [Glossary](https://developers.google.com/machine-learning/glossary) - [All terms](https://developers.google.com/machine-learning/glossary) - [Clustering](https://developers.google.com/machine-learning/glossary/clustering) - [Decision Forests](https://developers.google.com/machine-learning/glossary/df) - [Fundamentals](https://developers.google.com/machine-learning/glossary/fundamentals) - [GCP](https://developers.google.com/machine-learning/glossary/googlecloud) - [Generative AI](https://developers.google.com/machine-learning/glossary/generative) - [Metrics](https://developers.google.com/machine-learning/glossary/metrics) - [Responsible AI](https://developers.google.com/machine-learning/glossary/responsible-ai) - [TensorFlow](https://developers.google.com/machine-learning/glossary/tensorflow) - [English](https://developers.google.com/machine-learning/crash-course) - [Deutsch](https://developers.google.com/machine-learning/crash-course?hl=de) - [Español](https://developers.google.com/machine-learning/crash-course?hl=es) - [Español – América Latina](https://developers.google.com/machine-learning/crash-course?hl=es-419) - [Français](https://developers.google.com/machine-learning/crash-course?hl=fr) - [Indonesia](https://developers.google.com/machine-learning/crash-course?hl=id) - 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[中文 – 简体](https://developers.google.com/machine-learning/crash-course?hl=zh-cn) - [中文 – 繁體](https://developers.google.com/machine-learning/crash-course?hl=zh-tw) - [日本語](https://developers.google.com/machine-learning/crash-course?hl=ja) - [한국어](https://developers.google.com/machine-learning/crash-course?hl=ko) [Sign in](https://developers.google.com/_d/signin?continue=https%3A%2F%2Fdevelopers.google.com%2Fmachine-learning%2Fcrash-course&prompt=select_account) [Home](https://developers.google.com/machine-learning/crash-course) [Crash Course](https://developers.google.com/machine-learning/crash-course/prereqs-and-prework) More - [Machine Learning](https://developers.google.com/machine-learning) - [ML Concepts](https://developers.google.com/machine-learning/crash-course) - More - [Home](https://developers.google.com/machine-learning/crash-course) - [Crash Course](https://developers.google.com/machine-learning/crash-course/prereqs-and-prework) - [Foundational courses](https://developers.google.com/machine-learning/foundational-courses) - [Advanced courses](https://developers.google.com/machine-learning/advanced-courses) - [Guides](https://developers.google.com/machine-learning/guides) - [Glossary](https://developers.google.com/machine-learning/glossary) - More - Introduction - [Introduction to ML](https://developers.google.com/machine-learning/intro-to-ml) - ML models - [Linear regression](https://developers.google.com/machine-learning/crash-course/linear-regression) - [Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression) - [Classification](https://developers.google.com/machine-learning/crash-course/classification) - Data - [Working with numerical data](https://developers.google.com/machine-learning/crash-course/numerical-data) - [Working with categorical data](https://developers.google.com/machine-learning/crash-course/categorical-data) - [Datasets, generalization, and overfitting](https://developers.google.com/machine-learning/crash-course/overfitting) - Advanced ML models - [Neural networks](https://developers.google.com/machine-learning/crash-course/neural-networks) - [Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings) - [Intro to Large Language Models](https://developers.google.com/machine-learning/crash-course/llm) - Real-world ML - [Production ML systems](https://developers.google.com/machine-learning/crash-course/production-ml-systems) - [Automated machine learning](https://developers.google.com/machine-learning/crash-course/automl) - [Fairness](https://developers.google.com/machine-learning/crash-course/fairness) - [All terms](https://developers.google.com/machine-learning/glossary) - [Clustering](https://developers.google.com/machine-learning/glossary/clustering) - [Decision Forests](https://developers.google.com/machine-learning/glossary/df) - [Fundamentals](https://developers.google.com/machine-learning/glossary/fundamentals) - [GCP](https://developers.google.com/machine-learning/glossary/googlecloud) - [Generative AI](https://developers.google.com/machine-learning/glossary/generative) - [Metrics](https://developers.google.com/machine-learning/glossary/metrics) - [Responsible AI](https://developers.google.com/machine-learning/glossary/responsible-ai) - [TensorFlow](https://developers.google.com/machine-learning/glossary/tensorflow) - [Home](https://developers.google.com/) - [Products](https://developers.google.com/products) - [Machine Learning](https://developers.google.com/machine-learning) - [ML Concepts](https://developers.google.com/machine-learning/crash-course) Send feedback ## Machine Learning Crash Course Google's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises. [Start Crash Course](https://developers.google.com/machine-learning/crash-course/linear-regression) [Browse course modules](https://developers.google.com/machine-learning/crash-course#course-modules) [View prerequisites](https://developers.google.com/machine-learning/crash-course/prereqs-and-prework) [Help Center](https://support.google.com/machinelearningeducation) ### What's new in Machine Learning Crash Course? Since 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC. ## Course Modules Each Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below. ## ML Models These modules cover the fundamentals of building regression and classification models. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Linear_Regression_no_text.png)](https://developers.google.com/machine-learning/crash-course/linear-regression) ### [Linear Regression](https://developers.google.com/machine-learning/crash-course/linear-regression) An introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Logistic_Regression_no_text.png)](https://developers.google.com/machine-learning/crash-course/logistic-regression) ### [Logistic Regression](https://developers.google.com/machine-learning/crash-course/logistic-regression) An introduction to logistic regression, where ML models are designed to predict the probability of a given outcome. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Classification_no_text.png)](https://developers.google.com/machine-learning/crash-course/classification) ### [Classification](https://developers.google.com/machine-learning/crash-course/classification) An introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC. ## Data These modules cover fundamental techniques and best practices for working with machine learning data. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Numerical_Data_no_text.png)](https://developers.google.com/machine-learning/crash-course/numerical-data) ### [Working with Numerical Data](https://developers.google.com/machine-learning/crash-course/numerical-data) Learn how to analyze and transform numerical data to help train ML models more effectively. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Categorical_Data_no_text.png)](https://developers.google.com/machine-learning/crash-course/categorical-data) ### [Working with Categorical Data](https://developers.google.com/machine-learning/crash-course/categorical-data) Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Generalization_no_text.png)](https://developers.google.com/machine-learning/crash-course/overfitting) ### [Datasets, Generalization, and Overfitting](https://developers.google.com/machine-learning/crash-course/overfitting) An introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model. ## Advanced ML models These modules cover advanced ML model architectures. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Neural_Nets_no_text.png)](https://developers.google.com/machine-learning/crash-course/neural-networks) ### [Neural Networks](https://developers.google.com/machine-learning/crash-course/neural-networks) An introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Embedding_no_text.png)](https://developers.google.com/machine-learning/crash-course/embeddings) ### [Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings) Learn how embeddings allow you to do machine learning on large feature vectors. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Large_Lanuage_Model_no_text.png)](https://developers.google.com/machine-learning/crash-course/llm) New ### [Intro to Large Language Models](https://developers.google.com/machine-learning/crash-course/llm) An introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained. ## Real-world ML These modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Production_ML_Systems_no_text.png)](https://developers.google.com/machine-learning/crash-course/production-ml-systems) ### [Production ML Systems](https://developers.google.com/machine-learning/crash-course/production-ml-systems) Learn how a machine learning production system works across a breadth of components. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Auto_ML_Yellow_no_text.png)](https://developers.google.com/machine-learning/crash-course/automl) New ### [AutoML](https://developers.google.com/machine-learning/crash-course/automl) Learn principles and best practices for using automated machine learning. [![](https://developers.google.com/static/machine-learning/crash-course/images/MLCC_Fairness_no_text.png)](https://developers.google.com/machine-learning/crash-course/fairness) ### [ML Fairness](https://developers.google.com/machine-learning/crash-course/fairness) Learn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data. 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Readable Markdown	[Skip to main content](https://developers.google.com/machine-learning/crash-course#main-content) ## Machine Learning Crash Course Google's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises. ### What's new in Machine Learning Crash Course? Since 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC. ## Course Modules Each Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below. ## ML Models These modules cover the fundamentals of building regression and classification models. ### [Linear Regression](https://developers.google.com/machine-learning/crash-course/linear-regression) An introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning. ### [Logistic Regression](https://developers.google.com/machine-learning/crash-course/logistic-regression) An introduction to logistic regression, where ML models are designed to predict the probability of a given outcome. ### [Classification](https://developers.google.com/machine-learning/crash-course/classification) An introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC. ## Data These modules cover fundamental techniques and best practices for working with machine learning data. ### [Working with Categorical Data](https://developers.google.com/machine-learning/crash-course/categorical-data) Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses. ### [Datasets, Generalization, and Overfitting](https://developers.google.com/machine-learning/crash-course/overfitting) An introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model. ## Advanced ML models These modules cover advanced ML model architectures. ### [Neural Networks](https://developers.google.com/machine-learning/crash-course/neural-networks) An introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions. ### [Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings) Learn how embeddings allow you to do machine learning on large feature vectors. New ### [Intro to Large Language Models](https://developers.google.com/machine-learning/crash-course/llm) An introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained. ## Real-world ML These modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering. ### [Production ML Systems](https://developers.google.com/machine-learning/crash-course/production-ml-systems) Learn how a machine learning production system works across a breadth of components. New ### [AutoML](https://developers.google.com/machine-learning/crash-course/automl) Learn principles and best practices for using automated machine learning. ### [ML Fairness](https://developers.google.com/machine-learning/crash-course/fairness) Learn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data. \[\[\["Easy to understand","easyToUnderstand","thumb-up"\],\["Solved my problem","solvedMyProblem","thumb-up"\],\["Other","otherUp","thumb-up"\]\],\[\["Missing the information I need","missingTheInformationINeed","thumb-down"\],\["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"\],\["Out of date","outOfDate","thumb-down"\],\["Samples / code issue","samplesCodeIssue","thumb-down"\],\["Other","otherDown","thumb-down"\]\],\[\],\[\],\[\]\]
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