{"id":18963740,"url":"https://github.com/drakearch/kaggle-courses","last_synced_at":"2025-04-16T05:31:26.190Z","repository":{"id":41471394,"uuid":"206448350","full_name":"drakearch/kaggle-courses","owner":"drakearch","description":"Kaggle courses and tutorials to get you started in the Data Science world.","archived":false,"fork":false,"pushed_at":"2022-04-11T00:04:58.000Z","size":9115,"stargazers_count":195,"open_issues_count":0,"forks_count":63,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-11-08T14:22:56.588Z","etag":null,"topics":["data-science","deep-learning","machine-learning","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/drakearch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-09-05T01:29:45.000Z","updated_at":"2024-10-28T07:41:04.000Z","dependencies_parsed_at":"2022-07-07T16:21:51.179Z","dependency_job_id":null,"html_url":"https://github.com/drakearch/kaggle-courses","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drakearch%2Fkaggle-courses","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drakearch%2Fkaggle-courses/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drakearch%2Fkaggle-courses/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drakearch%2Fkaggle-courses/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/drakearch","download_url":"https://codeload.github.com/drakearch/kaggle-courses/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249201117,"owners_count":21229004,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","deep-learning","machine-learning","pandas","python"],"created_at":"2024-11-08T14:21:26.952Z","updated_at":"2025-04-16T05:31:24.385Z","avatar_url":"https://github.com/drakearch.png","language":"Jupyter Notebook","funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# Kaggle Courses\n\n- [Python](#python)\n- [Pandas](#pandas)\n- [Data Visualization](#data-visualization)\n- [Intro to Machine Learning](#intro-to-machine-learning)\n- [Intermediate Machine Learning](#intermediate-machine-learning)\n- [Data Cleaning](#data-cleaning)\n- [Feature Engineering](#feature-engineering)\n- [Feature Engineering (2019)](#feature-engineering-2019)\n- [Geospatial Analysis](#geospatial-analysis)\n- [Time Series](#time-series)\n- [Machine Learning Explainability](#machine-learning-explainability)\n- [Intro to AI Ethics](#intro-to-ai-ethics)\n- [Intro to Deep Learning](#intro-to-deep-learning)\n- [Deep Learning](#deep-learning)\n- [Computer Vision](#computer-vision)\n- [Natural Language Processing](#natural-language-processing)\n- [Intro to Game AI and Reinforcement Learning](#intro-to-game-ai-and-reinforcement-learning)\n- [Intro to SQL](#intro-to-sql)\n- [Advanced SQL](#advanced-sql)\n- [Microchallenges](#microchallenges)\n\n\n## Python\n\n1. [Hello, Python](python/01-syntax-variables-and-numbers.ipynb)  \nA quick introduction to Python syntax, variable assignment, and numbers.\n\n2. [Functions and Getting Help](python/02-functions-and-getting-help.ipynb)  \nCalling functions and defining our own, and using Python's builtin documentation.\n\n3. [Booleans and Conditionals](python/03-booleans-and-conditionals.ipynb)  \nUsing booleans for branching logic.\n\n4. [Lists and Tuples](python/04-lists.ipynb)  \nLists and the things you can do with them. Includes indexing, slicing and mutating.\n\n5. [Loops and List Comprehensions](python/05-loops-and-list-comprehensions.ipynb)  \nFor and while loops, and a much-loved Python feature: list comprehensions.\n\n6. [Strings and Dictionaries](python/06-strings-and-dictionaries.ipynb)  \nWorking with strings and dictionaries, two fundamental Python data types.\n\n7. [Working with External Libraries](python/07-working-with-external-libraries.ipynb)  \nImports, operator overloading, and survival tips for venturing into the world of external libraries.\n\n\n## Pandas\n\n1. [Exercise: Creating, Reading and Writing](pandas/01-creating-reading-and-writing.ipynb)   \nYou can't work with data if you can't read it. Get started here.\n\n2. [Exercise: Indexing, Selecting \u0026 Assigning](pandas/02-indexing-selecting-assigning.ipynb)  \nPro data scientists do this dozens of times a day. You can, too!\n\n3. [Exercise: Summary Functions and Maps](pandas/03-summary-functions-and-maps.ipynb)   \nExtract insights from your data.\n\n4. [Exercise: Grouping and Sorting](pandas/04-grouping-and-sorting.ipynb)  \nScale up your level of insight. The more complex the dataset, the more this matters.\n\n5. [Exercise: Data Types and Missing Values](pandas/05-data-types-and-missing-values.ipynb)  \nDeal with the most common progress-blocking problems.\n\n6. [Exercise: Renaming and Combining](pandas/06-renaming-and-combining.ipynb)  \nData comes in from many sources. Help it all make sense together.\n\n\n## Data Visualization\n\n1. [Hello, Seaborn](data_visualization/01-hello-seaborn.ipynb)   \nYour first introduction to coding for data visualization.\n\n2. [Line Charts](data_visualization/02-line-charts.ipynb)  \nVisualize trends over time.\n\n3. [Bar Charts and Heatmaps](data_visualization/03-bar-charts-and-heatmaps.ipynb)   \nUse color or length to compare categories in a dataset.\n\n4. [Scatter Plots](data_visualization/04-scatter-plots.ipynb)  \nLeverage the coordinate plane to explore relationships between variables.\n\n5. [Distributions](data_visualization/05-distributions.ipynb)  \nCreate histograms and density plots.\n\n6. [Choosing Plot Types and Custom Styles](data_visualization/06-choosing-plot-types-and-custom-styles.ipynb)  \nCustomize your charts and make them look snazzy.\n\n7. [Final Project](data_visualization/07-final-project.ipynb)  \nPractice for real-world application.\n\n\n## Intro to Machine Learning\n\n1. [How Models Work](https://www.kaggle.com/dansbecker/how-models-work)   \nThe first step if you're new to machine learning.\n\n2. [Basic Data Exploration](intro_to_machine_learning/02-explore-your-data.ipynb)  \nLoad and understand your data.\n\n3. [Your First Machine Learning Model](intro_to_machine_learning/03-your-first-machine-learning-model.ipynb)   \nBuilding your first model. Hurray!\n\n4. [Model Validation](intro_to_machine_learning/04-model-validation.ipynb)  \nMeasure the performance of your model ? so you can test and compare alternatives.\n\n1. [Underfitting and Overfitting](intro_to_machine_learning/05-underfitting-and-overfitting.ipynb)  \nFine-tune your model for better performance.\n\n6. [Random Forests](intro_to_machine_learning/06-random-forests.ipynb)  \nUsing a more sophisticated machine learning algorithm.\n\n7. [Exercise: Machine Learning Competitions](intro_to_machine_learning/07-machine-learning-competitions.ipynb)  \nEnter the world of machine learning competitions to keep improving and see your progress.\n\n\n## Intermediate Machine Learning\n\n1. [Introduction](intermediate_machine_learning/01-introduction.ipynb)   \nReview what you need for this Micro-Course.\n\n2. [Missing Values](intermediate_machine_learning/02-missing-values.ipynb)  \nMissing values happen. Be prepared for this common challenge in real datasets.\n\n3. [Categorical Variables](intermediate_machine_learning/03-categorical-variables.ipynb)   \nThere's a lot of non-numeric data out there. Here's how to use it for machine learning.\n\n4. [Pipelines](intermediate_machine_learning/04-pipelines.ipynb)  \nA critical skill for deploying (and even testing) complex models with pre-processing.\n\n5. [Cross-Validation](intermediate_machine_learning/05-cross-validation.ipynb)  \nA better way to test your models.\n\n6. [XGBoost](intermediate_machine_learning/06-xgboost.ipynb)  \nThe most accurate modeling technique for structured data.\n\n7. [Data Leakage](intermediate_machine_learning/07-data-leakage.ipynb)  \nFind and fix this problem that ruins your model in subtle ways.\n\n\n## Data Cleaning\n\n1. [Handling Missing Values](data_cleaning/01-handling-missing-values.ipynb)   \nDrop missing values, or fill them in with an automated workflow.\n\n2. [Scaling and Normalization](data_cleaning/02-scaling-and-normalization.ipynb)   \nTransform numeric variables to have helpful properties.\n\n3. [Parsing Dates](data_cleaning/03-parsing-dates.ipynb)   \nHelp Python recognize dates as composed of day, month, and year.\n\n4. [Character Encodings](data_cleaning/04-character-encodings.ipynb)   \nAvoid UnicodeDecodeErrors when loading CSV files.\n\n5. [Inconsistent Data Entry](data_cleaning/05-inconsistent-data-entry.ipynb)   \nEfficiently fix typos in your data.\n\n\n## Feature Engineering\n\n1. [What Is Feature Engineering](https://www.kaggle.com/code/ryanholbrook/what-is-feature-engineering)   \nLearn the steps and principles of creating better features\n\n2. [Mutual Information](feature_engineering/02-mutual-information.ipynb)   \nLocate features with the most potential.\n\n3. [Creating Features](feature_engineering/03-creating-features.ipynb)   \nTransform features with Pandas to suit your model.\n\n4. [Clustering With K-Means](feature_engineering/04-clustering-with-k-means.ipynb)   \nUntangle complex spatial relationships with cluster labels.\n\n5. [Principal Component Analysis](feature_engineering/05-principal-component-analysis.ipynb)   \nDiscover new features by analyzing variation.\n\n6. [Target Encoding](feature_engineering/06-target-encoding.ipynb)   \nBoost any categorical feature with this powerful technique.\n\n7. [Feature Engineering for House Prices](https://www.kaggle.com/code/ryanholbrook/feature-engineering-for-house-prices)   \nApply what you've learned, and join the House Prices competition!\n\n\n## Feature Engineering (2019)\n\n1. [Baseline Model](feature_engineering_2019/01-baseline-model.ipynb)   \nBuilding a baseline model as a starting point for feature engineering.\n\n2. [Categorical Encodings](feature_engineering_2019/02-categorical-encodings.ipynb)  \nThere are many ways to encode categorical data for modeling. Some are pretty clever.\n\n3. [Feature Generation](feature_engineering_2019/03-feature-generation.ipynb)    \nThe frequently useful case where you can combine data from multiple rows into useful features.\n\n4. [Feature Selection](feature_engineering_2019/04-feature-selection.ipynb)  \nYou can make a lot of features. Here's how to get the best set of features for your model.\n\n\n## Geospatial Analysis\n\n1. [Your First Map](geospatial_analysis/01-your-first-map.ipynb)   \nGet started with plotting in GeoPandas.\n\n2. [Coordinate Reference Systems](geospatial_analysis/02-coordinate-reference-systems.ipynb)  \nIt's pretty amazing that we can represent the Earth's surface in 2 dimensions!\n\n3. [Interactive Maps](geospatial_analysis/03-interactive-maps.ipynb)   \nLearn how to make interactive heatmaps, choropleth maps, and more!\n\n4. [Manipulating Geospatial Data](geospatial_analysis/04-manipulating-geospatial-data.ipynb)  \nFind locations with just the name of a place. And, learn how to join data based on spatial relationships.\n\n5. [Proximity Analysis](geospatial_analysis/05-proximity-analysis.ipynb)  \nMeasure distance, and explore neighboring points on a map.\n\n\n## Time Series\n\n1. [Linear Regression With Time Series](time_series/01-linear-regression-with-time-series.ipynb)   \nUse two features unique to time series: lags and time steps.\n\n2. [Trend](time_series/02-trend.ipynb)   \nModel long-term changes with moving averages and the time dummy.\n\n3. [Seasonality](time_series/03-seasonality.ipynb)   \nCreate indicators and Fourier features to capture periodic change.\n\n4. [Time Series as Features](time_series/04-time-series-as-features.ipynb)   \nPredict the future from the past with a lag embedding.\n\n5. [Hybrid Models](time_series/05-hybrid-models.ipynb)   \nCombine the strengths of two forecasters with this powerful technique.\n\n6. [Forecasting With Machine Learning](time_series/06-forecasting-with-machine-learning.ipynb)   \nApply ML to any forecasting task with these four strategies.\n\n\n## Machine Learning Explainability\n\n1. [Use Cases for Model Insights](https://www.kaggle.com/dansbecker/use-cases-for-model-insights)   \nWhy and when do you need insights?\n\n2. [Permutation Importance](machine_learning_explainability/02-permutation-importance.ipynb)   \nWhat features does your model think are important?\n\n3. [Partial Plots](machine_learning_explainability/03-partial-plots.ipynb)   \nHow does each feature affect your predictions?\n\n4. [SHAP Values](machine_learning_explainability/04-shap-values.ipynb)   \nUnderstand individual predictions.\n\n5. [Advanced Uses of SHAP Values](machine_learning_explainability/05-advanced-uses-of-shap-values.ipynb)  \nAggregate SHAP values for even more detailed model insights.\n\n\n## Intro to AI Ethics\n\n1. [Introduction to AI Ethics](https://www.kaggle.com/var0101/introduction-to-ai-ethics)   \nLearn what to expect from the course.\n\n2. [Human-Centered Design for AI](intro_to_ai_ethics/02-human-centered-design-for-ai.ipynb)   \nDesign systems that serve people’s needs.  Navigate issues in several real-world scenarios.\n\n3. [Identifying Bias in AI](intro_to_ai_ethics/03-identifying-bias-in-ai.ipynb)   \nBias can creep in at any stage in the pipeline. Investigate a simple model that identifies toxic text.\n\n4. [AI Fairness](intro_to_ai_ethics/04-ai-fairness.ipynb)   \nLearn about four different types of fairness.  Assess a toy model trained to judge credit card applications.\n\n5. [Model Cards](intro_to_ai_ethics/05-model-cards.ipynb)   \nIncrease transparency by communicating key information about machine learning models.\n\n\n## Intro to Deep Learning\n\n1. [A Single Neuron](intro_to_deep_learning/01-a-single-neuron.ipynb)   \nLearn about linear units, the building blocks of deep learning.\n\n2. [Deep Neural Networks](intro_to_deep_learning/02-deep-neural-networks.ipynb)   \nAdd hidden layers to your network to uncover complex relationships.\n\n3. [Stochastic Gradient Descent](intro_to_deep_learning/03-stochastic-gradient-descent.ipynb)   \nUse Keras and Tensorflow to train your first neural network.\n\n4. [Overfitting and Underfitting](intro_to_deep_learning/04-overfitting-and-underfitting.ipynb)   \nImprove performance with extra capacity or early stopping.\n\n5. [Dropout and Batch Normalization](intro_to_deep_learning/05-dropout-and-batch-normalization.ipynb)   \nAdd these special layers to prevent overfitting and stabilize training.\n\n6. [Binary Classification](intro_to_deep_learning/06-binary-classification.ipynb)   \nApply deep learning to another common task.\n\n\n## Deep Learning\n\n1. [Intro to DL for Computer Vision](deep_learning/01-intro-to-dl-for-computer-vision.ipynb)   \nA quick overview of how models work on images.\n\n2. [Building Models From Convolutions](https://www.kaggle.com/dansbecker/building-models-from-convolutions)  \nScale up from simple building blocks to models with beyond human capabilities.\n\n3. [TensorFlow Programming](deep_learning/03-tensorflow-programming.ipynb)   \nStart writing code using TensorFlow and Keras.\n\n4. [Transfer Learning](deep_learning/04-transfer-learning.ipynb)  \nA powerful technique to build highly accurate models even with limited data.\n\n5. [Data Augmentation](deep_learning/05-data-augmentation.ipynb)  \nLearn a simple trick that effectively increases amount of data available for model training.\n\n6. [A Deeper Understanding of Deep Learning](https://www.kaggle.com/dansbecker/a-deeper-understanding-of-deep-learning)  \nHow Stochastic Gradient Descent and Back-Propagation train your deep learning model.\n\n7. [Deep Learning From Scratch](deep_learning/07-deep-learning-from-scratch.ipynb)  \nBuild models without transfer learning. Especially important for uncommon image types.\n\n8. [Dropout and Strides for Larger Models](deep_learning/08-dropout-and-strides-for-larger-models.ipynb)  \nMake your models faster and reduce overfitting.\n\n\n## Computer Vision\n\n1. [The Convolutional Classifier](computer_vision/01-the-convolutional-classifier.ipynb)   \nCreate your first computer vision model with Keras.\n\n2. [Convolution and ReLU](computer_vision/02-convolution-and-relu.ipynb)   \nDiscover how convnets create features with convolutional layers.\n\n3. [Maximum Pooling](computer_vision/03-maximum-pooling.ipynb)   \nLearn more about feature extraction with maximum pooling.\n\n4. [The Sliding Window](computer_vision/04-the-sliding-window.ipynb)   \nExplore two important parameters: stride and padding.\n\n5. [Custom Convnets](computer_vision/05-custom-convnets.ipynb)   \nDesign your own convnet.\n\n6. [Data Augmentation](computer_vision/06-data-augmentation.ipynb)   \nBoost performance by creating extra training data.\n\n7. [Create Your First Submission](https://www.kaggle.com/ryanholbrook/create-your-first-submission)   \nUse Kaggle's free TPUs to make a submission to the Petals to the Metal competition!\n\n8. [Getting Started: TPUs + Cassava Leaf Disease](https://www.kaggle.com/jessemostipak/getting-started-tpus-cassava-leaf-disease)   \nUse Kaggle's free TPUs to make a submission to the Cassava Leaf Disease Classification competition.\n\n\n## Natural Language Processing\n\n1. [Intro to NLP](natural_language_processing/01-intro-to-nlp.ipynb)   \nGet started with NLP.\n\n2. [Text Classification](natural_language_processing/02-text-classification.ipynb)   \nCombine machine learning with your newfound NLP skills.\n\n3. [Word Vectors](natural_language_processing/03-word-vectors.ipynb)   \nExplore an idea that ushered in a new generation of NLP techniques.\n\n\n## Intro to Game AI and Reinforcement Learning\n\n1. [Play the Game](intro_to_game_ai_and_reinforcement_learning/01-play-the-game.ipynb)   \nWrite your first game-playing agent.\n\n2. [One-Step Lookahead](intro_to_game_ai_and_reinforcement_learning/02-one-step-lookahead.ipynb)   \nMake your agent smarter with a few simple changes.\n\n3. [N-Step Lookahead](intro_to_game_ai_and_reinforcement_learning/03-n-step-lookahead.ipynb)   \nUse the minimax algorithm to dramatically improve your agent.\n\n4. [Deep Reinforcement Learning](intro_to_game_ai_and_reinforcement_learning/04-deep-reinforcement-learning.ipynb)   \nExplore advanced techniques for creating intelligent agents.\n\n\n## Intro to SQL\n\n1. [Getting Started With SQL and BigQuery](intro_to_sql/01-getting-started-with-sql-and-bigquery.ipynb)   \nLearn the workflow for handling big datasets with BigQuery and SQL.\n\n2. [Select, From \u0026 Where](intro_to_sql/02-select-from-where.ipynb)  \nThe foundational compontents for all SQL queries.\n\n3. [Group By, Having \u0026 Count](intro_to_sql/03-group-by-having-count.ipynb)   \nGet more interesting insights directly from your SQL queries.\n\n4. [Order By](intro_to_sql/04-order-by.ipynb)  \nOrder your results to focus on the most important data for your use case.\n\n5. [As \u0026 With](intro_to_sql/05-as-with.ipynb)  \nOrganize your query for better readability. This becomes especially important for complex queries.\n\n6. [Joining Data](intro_to_sql/06-joining-data.ipynb)  \nCombine data sources. Critical for almost all real-world data problems.\n\n\n## Advanced SQL\n\n1. [JOINs and UNIONs](advanced_sql/01-joins-and-unions.ipynb)   \nCombine information from multiple tables.\n\n2. [Analytic Functions](advanced_sql/02-analytic-functions.ipynb)  \nPerform complex calculations on groups of rows.\n\n3. [Nested and Repeated Data](advanced_sql/03-nested-and-repeated-data.ipynb)   \nLearn to query complex datatypes in BigQuery.\n\n4. [Writing Efficient Queries](advanced_sql/04-writing-efficient-queries.ipynb)  \nWrite queries to run faster and use less data.\n\n\n## Microchallenges\n\n1. [Blackjack Microchallenge](microchallenges/01-blackjack-microchallenge.ipynb)   \nTest your logic and programming skills with by building a better BlackJack player.\n\n2. [Airline Price Optimization Micro-Challenge](microchallenges/02-airline-price-optimization-micro-challenge.ipynb)  \nCan you set the best airfare prices in our Airline Sales simulator.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrakearch%2Fkaggle-courses","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdrakearch%2Fkaggle-courses","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrakearch%2Fkaggle-courses/lists"}