{"id":18339954,"url":"https://github.com/mxagar/project_compilation","last_synced_at":"2026-04-25T12:36:45.219Z","repository":{"id":60703185,"uuid":"491088116","full_name":"mxagar/project_compilation","owner":"mxagar","description":"This repository compiles some of the projects I have worked 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["computer-vision","data-science","deep-learning","image-processing","machine-learning","portfolio","robotics","virtual-reality"],"created_at":"2024-11-05T20:19:58.153Z","updated_at":"2026-04-25T12:36:45.200Z","avatar_url":"https://github.com/mxagar.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Mikel Sagardia - Project Portfolio\n\nThis repository compiles links to some of the projects I have worked on or I am currently working on:\n\n- :mortar_board: [Some Public Research Projects](#some-public-research-projects)\n- :soccer: [Some Side Projects](#some-side-projects)\n- :books: [Some of My Guides on AI MOOCs and Books](#some-of-my-guides-on-ai-moocs-and-books)\n- :mailbox: [Contact and Other Information](#contact-and-other-information)\n\u003c!--\n- :space_invader: [Other Projects](#other-projects)\n--\u003e\n\n## Some Public Research Projects\n\n\u003ctable\u003e\n\u003ctr\u003e\n    \u003ctd width=500\u003e\n        Realtime Collision Avoidance for Robots with Arbitrary Geometries\n    \u003c/td\u003e\n    \u003ctd width=100\u003e\n        \u003ca href=\"https://youtu.be/OqWwkPrrcII\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://ieeexplore.ieee.org/document/8446527\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd width=250\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/OqWwkPrrcII\"\u003e\n            \u003cimg src=\"./assets/sagardia_HUG_robot.jpg\" alt=\"Collision Avoidance HUG\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\n        A Platform for Bimanual Virtual Assembly Training with Haptic Feedback in Large Multi-Object Environments\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003ca href=\"https://youtu.be/marxNRb4e-c\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://dl.acm.org/doi/10.1145/2993369.2993386\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/marxNRb4e-c\"\u003e\n            \u003cimg src=\"./assets/sagardia_car_assembly.jpg\" alt=\"Virtual Car Assembly\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=180\u003e\n        VR-OOS: The DLR’s Virtual Reality Simulator for Telerobotic On-Orbit Servicing With Haptic Feedback\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://youtu.be/D9Jbew5Zmpw\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://ieeexplore.ieee.org/document/7119040\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/D9Jbew5Zmpw\"\u003e\n            \u003cimg src=\"./assets/sagardia_space_justin_vroos.jpg\" alt=\"Real and Virtual Robotic Satellite Maintenance\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=180\u003e\n        Realtime Physics Simulations with Fast and Robust Collision Detection and Force Computation Integrated to Bullet\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://youtu.be/Fsb0f1t4IbE\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://diglib.eg.org/handle/10.2312/eurovr.20141341.065-076\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/Fsb0f1t4IbE\"\u003e\n            \u003cimg src=\"./assets/sagardia_bunny_teapot_datastrucs.jpg\" alt=\"Real Time Collision Computation Physics\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=180\u003e\n        Multimodal Evaluation of the Differences between Real and Virtual Assemblies\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://youtu.be/En_IXwSNVco\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://ieeexplore.ieee.org/document/8013101\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/En_IXwSNVco\"\u003e\n            \u003cimg src=\"./assets/sagardia_vr_vs_reality.jpg\" alt=\"Virtual vs. Real Assemblies User Study\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=180\u003e\n        Ultrapiano: A Novel Human-Machine Interface Applied to Virtual Reality\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://youtu.be/1yoU1f_zwiY\"\u003eVideo\u003c/a\u003e,\n        \u003ca href=\"https://ieeexplore.ieee.org/document/6907142\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://youtu.be/1yoU1f_zwiY\"\u003e\n            \u003cimg src=\"./assets/sagardia_ultrapiano.jpg\" alt=\"The Ultrapiano: Hand Manipulation with Ultrasound\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=180\u003e\n        Narrow Passage Sampling in the Observation of Robotic Assembly Tasks\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/7487125\"\u003ePaper\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/7487125\"\u003e\n            \u003cimg src=\"./assets/sagardia_peginhole_sequence.jpg\" alt=\"Robot Assemblies with Support Online Simulations\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd width=160\u003e\n        My PhD Thesis: \u003cem\u003eVirtual Manipulations with Force Feedback in Complex Interaction Scenarios\u003c/em\u003e\n    \u003c/td\u003e\n    \u003ctd width=40\u003e\n        \u003ca href=\"https://mediatum.ub.tum.de/?id=1463136\"\u003eDissertation\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n        \u003cp align=\"center\"\u003e\n            \u003ca href=\"https://mediatum.ub.tum.de/?id=1463136\"\u003e\n            \u003cimg src=\"./assets/sagardia_phd_summary.jpg\" alt=\"PhD Thesis\"\n            width=200\u003e\n            \u003c/a\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n\u003c!--\n- Realtime Collision Avoidance for Robots with Arbitrary Geometries: [Video](https://youtu.be/OqWwkPrrcII) | [Paper](https://ieeexplore.ieee.org/document/8446527).\n- A Platform for Bimanual Virtual Assembly Training with Haptic Feedback in Large Multi-Object Environments: [Video](https://youtu.be/marxNRb4e-c) | [Paper](https://dl.acm.org/doi/10.1145/2993369.2993386).\n- VR-OOS: The DLR’s Virtual Reality Simulator for Telerobotic On-Orbit Servicing With Haptic Feedback: [Video](https://youtu.be/D9Jbew5Zmpw) | [Paper](https://ieeexplore.ieee.org/document/7119040).\n- Realtime Physics Simulations with Fast and Robust Collision Detection and Force Computation Integrated to Bullet: [Video](https://youtu.be/Fsb0f1t4IbE) | [Paper](https://diglib.eg.org/handle/10.2312/eurovr.20141341.065-076).\n- Multimodal Evaluation of the Differences between Real and Virtual Assemblies: [Video](https://youtu.be/En_IXwSNVco) | [Paper](https://ieeexplore.ieee.org/document/8013101).\n- Ultrapiano: A Novel Human-Machine Interface Applied to Virtual Reality: [Video](https://youtu.be/1yoU1f_zwiY) | [Paper](https://ieeexplore.ieee.org/document/6907142).\n- Narrow Passage Sampling in the Observation of Robotic Assembly Tasks: [Paper](https://ieeexplore.ieee.org/abstract/document/7487125).\n- My PhD Thesis: *Virtual Manipulations with Force Feedback in Complex Interaction Scenarios* : [Dissertation](https://mediatum.ub.tum.de/?id=1463136).\n--\u003e\n\n## Some Side Projects\n\nPlease, note that these are some of my *side* projects, which might or might not be finished; in any case, the project status should be reported in each project page.\n\n| Topic / Project | Link | Type of Data | Methods | Tools |\n|---|---|---|---|---|\n| A Retrieval-Augmented Generation (RAG) Chatbot Deployed Using Azure OpenAI Services | [Github](https://github.com/mxagar/azure-rag-app) | Text :page_facing_up: | LLMs, RAG, Retrieval, Azure Deployment | LangChain, FastAPI, Azure AI Search, Azure OpenAI, Azure Document Intelligence |\n| A Multi-Model Architecture for Machine Learning Services | [Github](https://github.com/mxagar/multimodal_ml_service) | Images :city_sunrise:, Text :page_facing_up:, Tabular :bar_chart: | Software Architecture (Domain-Driven Design), Design Patterns, Machine Learning Pipelines, MLOps | Pytorch, Scikit-Learn, ONNX, LabelStudio, AWS S3, MLflow, Pytest |\n| Generating Image Vector Representations Using *SimCLR* | [Github](https://github.com/mxagar/simclr_pytorch_flowers) | Images :city_sunrise: | Contrastive Learning, CNN | Pytorch \u0026 Pytorch Lightning, Tensorboard |\n| Face Generation with a Convolutional Generative Adversarial Network (GAN) | [Github](https://github.com/mxagar/face_generator_gan) | Images :city_sunrise: | GAN, CNN | Pytorch |\n| Image Captioning: Image Description Text Generator Combining CNNs and RNNs | [Github](https://github.com/mxagar/image_captioning) | Images :city_sunrise:, Text :page_facing_up: | CNN, RNN, Image Captioning | Pytorch |\n| Facial Keypoint Detection with Deep Convolutional Neural Networks (CNNs) | [Github](https://github.com/mxagar/P1_Facial_Keypoints) | Images :city_sunrise: | CNN, Regression | Pytorch |\n| Skin Cancer Detection with Convolutional Neural Networks (CNNs) and T-SNE Visualization of Compressed Image Representations | [Github](https://github.com/mxagar/dermatologist-ai) | Images :city_sunrise: | CNN, Classification, Autoencoders, Manifold Learning | Pytorch, Scikit-Learn |\n| Dog Breed Classification with Convolutional Neural Networks (CNNs) and Transfer Learning | [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-dog-classification) | Images :city_sunrise: | CNN, Classification, Transfer Learning | Pytorch |\n| American Sign Language (ASL) Image Analysis and Classification with Convolutional Neural Networks (CNNs) | [Github](https://github.com/mxagar/asl_alphabet_image_classification) | Images :city_sunrise: | CNN, Classification, Transfer Learning, Autoencoders | Tensorflow/Keras |\n| A Satellite Image Processing Toolkit to Vectorize Water Bodies | [Github](https://github.com/mxagar/satellite_image_water_vectorizer) | Images :city_sunrise:, Geo-Spatial :satellite: :earth_africa: | Image Processing | Rasterio, GeoPandas, EarthPy, Matplotlib |\n| Analysis and Modelling of the AirBnB Dataset from the Basque Country | [Blog](https://mikelsagardia.io/blog/airbnb-spain-basque-data-analysis.html), [Github](https://github.com/mxagar/airbnb_data_analysis) | Tabular :bar_chart:, Text :page_facing_up: | Regression, Classification, Hypothesis Testing | Scikit-Learn |\n| A Template Package to Transform Machine Learning Research Notebooks into *Production-Level* Code and Its Application to Predicting Customer Churn | [Blog](https://mikelsagardia.io/blog/machine-learning-production-level.html), [Github](https://github.com/mxagar/customer_churn_production) | Tabular :bar_chart: | MLOps, Classification, Clean Code | Python Packaging, Scikit-Learn |\n| A Boilerplate for Reproducible Machine Learning Pipelines with MLflow and Weights \u0026 Biases and Its Application to Song Genre Classification | [Github](https://github.com/mxagar/music_genre_classification) | Tabular :bar_chart: | MLOps, Classification, Random Forests | Scikit-Learn, MLflow, Weights \u0026 Biases |\n| A Disaster Response Classification Web App with ETL and Machine Learning (ML) Pipelines | [Github](https://github.com/mxagar/disaster_response_pipeline) | Text :page_facing_up:, Tabular :bar_chart: | MLOps, Classification, Random Forests, CI | Scikit-Learn, NLTK, Flask, Pytest, Docker |\n| A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City | [Github](https://github.com/mxagar/ml_pipeline_rental_prices) | Tabular :bar_chart: | MLOps, Regression | Scikit-Learn, MLflow, Weights \u0026 Biases |\n| Deployment of a Sentiment Analysis Recurrent Neural Network (RNN) Using AWS SageMaker | [Github](https://github.com/mxagar/sentiment_rnn_aws_deployment) | Text :page_facing_up: | MLOps, RNN, Classification, Sentiment Analysis | AWS SageMaker, API Gateway, Lambda, Pytorch |\n| Deployment of a Census Salary Classification Model Using FastAPI | [Github](https://github.com/mxagar/census_model_deployment_fastapi) | Tabular :bar_chart: | MLOps, Classification, CI/CD, Deployment | Scikit-Learn, Python Packaging, FastAPI, Heroku, Pytest, Docker, AWS |\n| A Dynamic Risk Assessment System: Monitoring of a Customer Churn Model | [Github](https://github.com/mxagar/churn_model_monitoring) | Tabular :bar_chart: | MLOps, Classification, Monitoring, Automation | Scikit-Learn, Flask, SQLite, SQLAlchemy |\n| Deployment of a Personalized Online Course Recommender System Using Streamlit | [Github](https://github.com/mxagar/course_recommender_streamlit) | Tabular :bar_chart: | Recommender Systems, Unsupervised Learning, Regression, Classification, CI/CD, Deployment | Scikit-Learn, Tensorflow/Keras, Streamlit, Heroku, Pytest |\n| Text Generation: TV Script Creation with a Recurrent Neural Network (RNN) | [Blog](https://mikelsagardia.io/blog/text-generation-rnn.html), [Github](https://github.com/mxagar/text_generator) | Text :page_facing_up: | RNN, Text Generation | Pytorch |\n| Simultaneous Localization and Mapping (SLAM) in 2D Using a Graph-Based Approach | [Github](https://github.com/mxagar/slam_2d) | Tabular :bar_chart:, Spatio-Temporal  :robot: | SLAM | Numpy |\n| Predicting Bike Sharing Patterns with Neural Networks Written from Scratch with Numpy | [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-bikesharing) | Tabular :bar_chart: | MLP, Regression | Numpy |\n| Analysis and Modelling of an Expert and Project Matching Dataset | [Github](https://github.com/mxagar/expert_chase) | Tabular :bar_chart: | EDA, Hypothesis Testing, Classification, Regression | Pandas, Scipy, Scikit-Learn, Matplotlib, etc. |\n| A 80/20 Guide for Exploratory Data Analysis, Data Cleaning and Feature Engineering | [Blog](https://mikelsagardia.io/blog/data-processing-guide.html), [Github](https://github.com/mxagar/eda_fe_summary) | Tabular :bar_chart:, Text :page_facing_up: | *Guide*: EDA, Regression, Classification, Unsupervised Learning, Pipelines | Scikit-Learn, Pandas, Matplotlib, etc. |\n| Beyond Image Classification: Object Detection and Semantic Segmentation Examples with Pytorch | [Github](https://github.com/mxagar/detection_segmentation_pytorch) | Images :city_sunrise: | *Compilation*: Object Detection \u0026 Segmentation | Pytorch |\n| Text Sentiment Analysis: A Collection of Notes and Examples | [Github](https://github.com/mxagar/text_sentiment) | Text :page_facing_up: | *Compilation*: Sentiment Analysis | Pytorch |\n\n\u003c!-- \nText :page_facing_up: :page_facing_up:\nTabular :bar_chart: :bar_chart:\nImages :city_sunrise: :city_sunrise: :city_sunrise:\nSpatio-Temporal :robot:\n--\u003e\n\u003c!-- |  |  |  |  |  | --\u003e\n\u003c!--\n- Facial Keypoint Detection with Deep Convolutional Neural Networks (CNNs): [Github](https://github.com/mxagar/P1_Facial_Keypoints).\n- Predicting Bike Sharing Patterns with Neural Networks Written from Scratch with Numpy: [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-bikesharing).\n- Analysis and Modelling of the AirBnB Dataset from the Basque Country: [Blog](https://mikelsagardia.io/blog/airbnb-spain-basque-data-analysis.html) | [Github](https://github.com/mxagar/airbnb_data_analysis).\n- A 80/20 Guide for Exploratory Data Analysis, Data Cleaning and Feature Engineering: [Blog](https://mikelsagardia.io/blog/data-processing-guide.html) | [Github](https://github.com/mxagar/eda_fe_summary).\n- A Template Package to Transform Machine Learning Research Notebooks into *Production-Level* Code and Its Application to Predicting Customer Churn: [Blog](https://mikelsagardia.io/blog/machine-learning-production-level.html) | [Github](https://github.com/mxagar/customer_churn_production).\n- Dog Breed Classification with Convolutional Neural Networks (CNNs) and Transfer Learning: [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-dog-classification).\n- Skin Cancer Detection with Convolutional Neural Networks (CNNs) and T-SNE Visualization of Compressed Image Representations: [Github](https://github.com/mxagar/dermatologist-ai).\n- A Boilerplate for Reproducible Machine Learning Pipelines with MLflow and Weights \u0026 Biases and Its Application to Song Genre Classification: [Github](https://github.com/mxagar/music_genre_classification).\n- Beyond Image Classification: Object Detection and Semantic Segmentation Examples with Pytorch: [Github](https://github.com/mxagar/detection_segmentation_pytorch).\n- Text Sentiment Analysis: A Collection of Notes and Examples: [Github](https://github.com/mxagar/text_sentiment).\n- Text Generation: TV Script Creation with a Recurrent Neural Network (RNN): [Blog](https://mikelsagardia.io/blog/text-generation-rnn.html) | [Github](https://github.com/mxagar/text_generator).\n- Image Captioning: Image Description Text Generator Combining CNNs and RNNs: [Github](https://github.com/mxagar/image_captioning).\n- A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City: [Github](https://github.com/mxagar/ml_pipeline_rental_prices).\n- Face Generation with a Convolutional Generative Adversarial Network (GAN): [Github](https://github.com/mxagar/face_generator_gan).\n- Simultaneous Localization and Mapping (SLAM) in 2D Using a Graph-Based Approach: [Github](https://github.com/mxagar/slam_2d).\n- Deployment of a Sentiment Analysis Recurrent Neural Network (RNN) Using AWS SageMaker: [Github](https://github.com/mxagar/sentiment_rnn_aws_deployment).\n- American Sign Language (ASL) Image Analysis and Classification with Convolutional Neural Networks (CNNs): [Github](https://github.com/mxagar/asl_alphabet_image_classification)\n--\u003e\n\n## Some of My Guides on AI MOOCs and Books\n\nThis is a list of some repositories in which I collected notes for my future self while following courses/books. Note that in many cases the text is perfectly legible, but not edited; additionally, the original notes on a course/book might have been extended with other sources. For a complete list of the courses I have followed (with or without public notes), visit [my course compilation](https://github.com/mxagar/course_compilation).\n\n| \u003cdiv align=\"left\"\u003eCourse / Book + Link\u003c/div\u003e  | \u003cdiv style=\"width:150px\" align=\"left\"\u003eMy Notes | \u003cdiv align=\"left\" style=\"width:120px\"\u003eMy Personal Rating |\n| --- | --- | --- |\n[Generative AI Nanodegree, Udacity](https://www.udacity.com/course/generative-ai--nd608) | [Guide \u0026 Code](https://github.com/mxagar/generative_ai_udacity) | :star: :star: :star: :star: __ |\n| [Natural Language Processing with Transformers, O'Reilly Book by Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/) | [Guide \u0026 Code](https://github.com/mxagar/nlp_with_transformers_nbs) | :star: :star: :star: :star: :star: |\n| [Deep Learning Nanodegree, Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101) | [Guide \u0026 Code](https://github.com/mxagar/deep_learning_udacity) | :star: :star: :star: :star: __ |\n| [Computer Vision Nanodegree, Udacity](https://www.udacity.com/course/computer-vision-nanodegree--nd891) | [Guide \u0026 Code](https://github.com/mxagar/computer_vision_udacity) | :star: :star: :star: __ __ |\n| [Machine Learning DevOps Engineer Nanodegree, Udacity](https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821) | [Guide \u0026 Code](https://github.com/mxagar/mlops_udacity) | :star: :star: :star: :star: __ |\n| [IBM Machine Learning Professional Certificate, Coursera / IBM](https://www.coursera.org/professional-certificates/ibm-machine-learning) | [Guide \u0026 Code](https://github.com/mxagar/machine_learning_ibm) | :star: :star: :star: :star: __ |\n| [Machine Learning, Coursera / Univ. Standford](https://www.coursera.org/learn/machine-learning) | [Guide \u0026 Code](https://github.com/mxagar/machine_learning_coursera) | :star: :star: :star: :star: :star: |\n| [Statistics with Python Specialization, Coursera / Uni. Michigan](https://www.coursera.org/specializations/statistics-with-python) | [Guide \u0026 Code](https://github.com/mxagar/statistics_with_python_coursera)  | :star: :star: :star: __ __ |\n| [Data Science Nanodegree, Udacity](https://www.udacity.com/course/data-scientist-nanodegree--nd025) | [Guide \u0026 Code](https://github.com/mxagar/data_science_udacity) | :star: :star: :star: :star: __ |\n| [Accelerated Computer Science Fundamentals Specialization, Coursera / Univ. Illinois](https://www.coursera.org/specializations/cs-fundamentals) | [Guide \u0026 Code](https://github.com/mxagar/accelerated_computer_science_coursera) | :star: :star: :star: __ __ |\n| [Machine Vision: Theory and Applications, Book by Steger et al.](https://www.amazon.com/Machine-Vision-Algorithms-Applications-Carsten/dp/3527413650) | [Guide \u0026 Code](https://github.com/mxagar/machine_vision_notes) | :star: :star: :star: :star: :star: |\n| [Notes on Manipulation Robotics, Craig](https://www.amazon.com/Introduction-Robotics-Mechanics-Control-4th/dp/0133489795/ref=sr_1_1?crid=2Q39F2ZXG1D2W\u0026keywords=Introduction+to+Robotics\u0026qid=1664796958\u0026qu=eyJxc2MiOiIyLjYyIiwicXNhIjoiMi42OCIsInFzcCI6IjMuMDkifQ%3D%3D\u0026sprefix=introduction+to+robotics%2Caps%2C144\u0026sr=8-1) | [Guide](https://github.com/mxagar/robotics_notes) | :star: :star: :star: :star: __ |\n| [Natural Language Processing (NLP) Guide, Udemy](https://www.udemy.com/course/nlp-natural-language-processing-with-python/) | [Guide \u0026 Code](https://github.com/mxagar/nlp_guide) | :star: :star: :star: :star: __ |\n| [Hyperparameter Optimization Guide, Udemy](https://www.udemy.com/course/hyperparameter-optimization-for-machine-learning/) | [Guide \u0026 Code](https://github.com/mxagar/hyperparameter-optimization) | :star: :star: :star: :star: __ |\n| [Object Detection and Semantic Segmentation Guide, PyImageSearch](https://pyimagesearch.com/pyimagesearch-university/) | [Guide \u0026 Code](https://github.com/mxagar/detection_segmentation_pytorch) | :star: :star: :star: :star: __ |\n| [Notes on Reinforcement Learning, Udemy](https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/) | [Guide \u0026 Code](https://github.com/mxagar/data_science_python_tools/blob/main/24_ReinforcementLearning/ReinforcementLearning_Guide.md) | :star: :star: :star: __ __ |\n| [SQL Guide, Udemy](https://www.udemy.com/course/the-complete-sql-bootcamp/) | [Guide \u0026 Code](https://github.com/mxagar/sql_guide) | :star: :star: :star: :star: __ |\n| Big Data and Spark Guide, [Udacity](https://www.udacity.com/course/learn-spark-at-udacity--ud2002) and [Datacamp](https://app.datacamp.com/learn/skill-tracks/big-data-with-pyspark) | [Guide \u0026 Code](https://github.com/mxagar/spark_big_data_guide) | :star: :star: :star: :star: __ |\n| [Docker Guide, Udemy](https://www.udemy.com/course/docker-mastery/) | [Guide \u0026 Code](https://github.com/mxagar/templates/blob/master/docker_swarm_kubernetes/docker_swarm_kubernetes_howto.md) | :star: :star: :star: :star: __ |\n| [Azure Guide (AZ900), Udemy](https://www.udemy.com/course/az900-azure/) | [Guide \u0026 Code](https://github.com/mxagar/azure_guide) | :star: :star: :star: :star: __ |\n| [MLflow Guide, Udemy](https://www.udemy.com/course/mlflow-course) | [Guide \u0026 Code](https://github.com/mxagar/mlflow_guide) | :star: :star: :star: :star: __ |\n| [Elasticsearch Guide, Udemy](https://www.udemy.com/course/elasticsearch-complete-guide) | [Guide \u0026 Code](https://github.com/mxagar/elastic_search_guide) | :star: :star: :star: :star: __ |\n| [Operationalizing LLMs on Azure, Coursera / Univ. Duke](https://www.coursera.org/learn/llmops-azure) | [Guide \u0026 Code](https://github.com/mxagar/generative_ai_udacity/tree/main/06_RAGs_DeepDive/02_Azure_LLMs) | :star: :star: :star: :star: __ |\n\n\n\u003c!--\n- Deep Learning Nanodegree, Udacity: [Guide \u0026 Code](https://github.com/mxagar/deep_learning_udacity) | [Course Link](https://www.udacity.com/course/deep-learning-nanodegree--nd101) | :star: :star: :star: :star: __ \n- Computer Vision Nanodegree, Udacity: [Guide \u0026 Code](https://github.com/mxagar/computer_vision_udacity) | [Course Link](https://www.udacity.com/course/computer-vision-nanodegree--nd891) | :star: :star: :star: __ __\n- Data Science Nanodegree, Udacity: [Guide \u0026 Code](https://github.com/mxagar/data_science_udacity) | [Course Link](https://www.udacity.com/course/data-scientist-nanodegree--nd025) | :star: :star: :star: :star: __\n- Machine Learning DevOps Engineer Nanodegree, Udacity: [Guide \u0026 Code](https://github.com/mxagar/mlops_udacity) | [Course Link](https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821) | :star: :star: :star: :star: :star:\n- IBM Machine Learning Professional Certificate, Coursera / IBM: [Guide \u0026 Code](https://github.com/mxagar/machine_learning_ibm) | [Course Link](https://www.coursera.org/professional-certificates/ibm-machine-learning) | :star: :star: :star: :star: __\n- Machine Learning, Coursera / Univ. Standford: [Guide \u0026 Code](https://github.com/mxagar/machine_learning_coursera) | [Course Link](https://www.coursera.org/learn/machine-learning) | :star: :star: :star: :star: :star:\n- Statistics with Python Specialization, Coursera / Uni. Michigan: [Guide \u0026 Code](https://github.com/mxagar/statistics_with_python_coursera) | [Course Link](https://www.coursera.org/specializations/statistics-with-python) | :star: :star: :star: __ __\n- Accelerated Computer Science Fundamentals Specialization, Coursera / Univ. Illinois: [Guide \u0026 Code](https://github.com/mxagar/accelerated_computer_science_coursera) | [Course Link](https://www.coursera.org/specializations/cs-fundamentals) | :star: :star: :star: __ __\n- Machine Vision: Theory and Applications, Steger et al.: [Guide \u0026 Code](https://github.com/mxagar/machine_vision_notes) | [Book Link](https://www.amazon.com/Machine-Vision-Algorithms-Applications-Carsten/dp/3527413650) | :star: :star: :star: :star: :star:\n- Notes on Manipulation Robotics, Craig: [Guide](https://github.com/mxagar/robotics_notes) | [Book Link](https://www.amazon.com/Introduction-Robotics-Mechanics-Control-4th/dp/0133489795/ref=sr_1_1?crid=2Q39F2ZXG1D2W\u0026keywords=Introduction+to+Robotics\u0026qid=1664796958\u0026qu=eyJxc2MiOiIyLjYyIiwicXNhIjoiMi42OCIsInFzcCI6IjMuMDkifQ%3D%3D\u0026sprefix=introduction+to+robotics%2Caps%2C144\u0026sr=8-1) | :star: :star: :star: :star: __ \n- Natural Language Processing (NLP) Guide, Udemy: [Guide \u0026 Code](https://github.com/mxagar/nlp_guide) | [Course Link](https://www.udemy.com/course/nlp-natural-language-processing-with-python/) | :star: :star: :star: :star: __\n- Notes on Reinforcement Learning, Udemy: [Guide \u0026 Code](https://github.com/mxagar/data_science_python_tools/blob/main/24_ReinforcementLearning/ReinforcementLearning_Guide.md) | [Course Link](https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/) | :star: :star: :star: __ __\n- SQL Guide, Udemy: [Guide \u0026 Code](https://github.com/mxagar/sql_guide) | [Course Link](https://www.udemy.com/course/the-complete-sql-bootcamp/) | :star: :star: :star: :star: __\n- Docker Guide, Udemy: [Guide \u0026 Code](https://github.com/mxagar/templates/blob/master/docker_swarm_kubernetes/docker_swarm_kubernetes_howto.md) | [Course Link](https://www.udemy.com/course/docker-mastery/) | :star: :star: :star: :star: __\n--\u003e\n\n\u003c!--\n## Other Projects\n\n- [datamix.ai](https://datamix.ai)\n- [machinevision.academy](https://machinevision.academy)\n--\u003e\n\n## Contact and Other Information\n\nFor **professional collaboration**, you can find me at: [sagardia.mikel@gmail.com](mailto:sagardia.mikel@gmail.com).\n\nFor more information, you can visit:\n\n- My **website/blog**: [https://mikelsagardia.io](https://mikelsagardia.io)\n- The list of my **research papers**: [https://mikelsagardia.io/publications](https://mikelsagardia.io/publications)\n- My **Curriculum Vitae (CV)**: [https://mikelsagardia.io/assets/MikelSagardia_CV.pdf](https://mikelsagardia.io/assets/MikelSagardia_CV.pdf)\n- A list of **repositories on books and courses I have followed**: [https://github.com/mxagar/course_compilation](https://github.com/mxagar/course_compilation)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fproject_compilation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fproject_compilation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fproject_compilation/lists"}