A curated collection of tutorials, resources, and knowledge sharing for my lab.
I believe that everyone should have access to high-quality learning resources, regardless of their level of expertise. That's why I've organized this content into four levels, each designed to cater to different needs and skill sets.
🟩 Beginner : Foundational knowledge is essential for success. Learn the basics and get started with ease.
🟨 Intermediate : Build on what you know with more complex topics and applications.
🟧 Advanced : For experts looking to refine their skills and tackle challenging projects.
⬛ All Levels : Access all challenges and exercises.
Important
Please note that while I try to provide a comprehensive selection of resources, this repository may not be exhaustive, and omission does not imply exclusion. Inclusion in this collection does not constitute an endorsement of any particular tool or service.
If you'd like to suggest improvements or new categories, please submit a pull request (PR) to enhance the repository's contents. Your contributions are valued and appreciated !
All header images in this repository were generated using locally runned image generation models.
AI for Coding
- AIEnhancedWork : A collection of AI-driven tools designed to enhance productivity and make everyday work more manageable.
Command Line Interface (CLI)
- Awesome Cli : A simple command line tool to give you a fancy command line interface to dive into Awesome lists.
- Awesome Cli Apps : A curated list of command line apps.
- Awesome Cli Frameworks : Collection of tools to build beautiful command line interface in different languages.
- Awesome macOS Cli : A curated list of awesome command-line software for macOS.
- Awesome Shell : A curated list of awesome command-line frameworks, toolkits, guides and gizmos.
- Awesome Windows Cli : Use your Windows terminal to do awesome things.
Virtualization
- Awesome Compose : Awesome Docker Compose samples.
- Awesome Containers : A curated list of amazingly awesome open source container resources.
- Awesome Docker Compose Examples : Various Docker Compose examples of selfhosted FOSS and proprietary projects.
- Awesome Kubernetes : A curated list for awesome kubernetes sources.
- Docker Tutorials and Labs : a collection of tutorials for learning how to use Docker with various tools.
Version Control System (VCS)
- Awesome Git : A curated list of amazingly awesome Git tools, resources and shiny thing.
- Awesome VCS tools : A curated list of awesome Version control tools - clients, diffs.
- Git/GitHub Cheat Sheet : A list of cool features of Git and GitHub.
- Git and GitHub learning resources : Git and Github learning resources from Github.
- AI Assistance for Coding : Benefits, Challenges, and Best Practices 🟩 Beginner
- Basic Understanding of the Command Line 🟩 Beginner
- Use LLMs in your Integrated Development Environment (IDE) 🟩 Beginner
- Introduction to Virtualization 🟨 Intermediate
- Introduction to Version Control System (VCS) 🟨 Intermediate
- Introduction to Continuous Integration/Continuous Deployment (CI/CD) 🟨 Intermediate
🎮 Online Interactive courses
| Website | Audience | Format | Topics |
|---|---|---|---|
| Learn the Command Line by Codecademy | 🟩 Beginner | explanations + Online interpreter | Essential skills for working at the command line, covering navigation, file management, redirection, and environment configuration. |
| Git Tutorial by w3schools | 🟨 Intermediate | explanations + Online interpreter | Git Basics and more. |
| Learn Git & GitHub by Codecademy | 🟨 Intermediate | explanations + Online interpreter | Git and Github Basics and more. |
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Cheat Sheet: Unix/Mac Commands | Laurence Bradford | 🟩 Beginner | command Cheat Sheet | Basics of MacOS CLI |
| Command line crash course | Mozilla | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Command Line for Beginners | FreeCodeCamp | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Learning the Shell | Linuxcommand | 🟩 Beginner | Code examples with explanations. | Basics and more |
| The Art of Command Line | Joshua Levy | 🟩 Beginner | Code examples with explanations. | Basics and more |
| The Front-End Developer's Guide to the Terminal | Josh Comeau | 🟩 Beginner | Code examples with explanations. | Basics and more |
| The Linux command line for beginners | Ubuntu | 🟩 Beginner | Code examples with explanations. | Basics of Linux CLI |
| Windows Commands | Microsoft | 🟩 Beginner | Documentation with explanations. | Basics of Windows CLI |
| A Docker tutorial for beginners | Prakhar Srivastav | 🟨 Intermediate | Code examples with explanations. | Basics of Docker usage |
| A Step by Step Docker Tutorial for Beginners | Sana Afreen | 🟨 Intermediate | Code examples with explanations. | Basics and more |
| Docker Tutorial for Beginners | Programming with Mosh | 🟨 Intermediate | Youtube tutorial | Basics and more |
| Learn Docker in 2 Hours | KodeKloud | 🟨 Intermediate | Youtube tutorial | Basics of Docker usage |
| Docker Tutorial | geeksforgeeks | 🟨 Intermediate | Code examples with explanations. | Basics and more |
📖 Book References
| Book Name | Authors | Audience | Strengths | Topics |
|---|---|---|---|---|
| The Command Line Crash Course | Zed A. Shaw | 🟩 Beginner | Code examples with explanations. | Basics and more |
| The Linux Command Line | William Shotts | 🟩 Beginner | Code examples with explanations. | Basics and more |
| The Docker Handbook – Learn Docker for Beginners | Farhan Hasin Chowdhury | 🟨 Intermediate | Code examples with explanations. | Basics and more |
- Awesome Python : An opinionated list of awesome Python frameworks, libraries, software and resources.
- Awesome Python Applications : Free software that works great, and also happens to be open-source Python.
- Awesome Python Books : Directory of Python books.
- Awesome Python Resources : About Awesome Python Resources.
- Fucking Awesome Python : awesome-python ressources.
- A Method to Learn Python from scratch 🟩 Beginner
- Setting up a Python virtual environment 🟩 Beginner
- Getting Started With Jupyter Notebook for Python 🟨 Intermediate
📖 Official Python Documentation
🎮 Online Interactive courses
| Website | Audience | Format | Topics |
|---|---|---|---|
| Codédex | 🟩 Beginner | Funny environment with explanations + Online interpreter | Basics and more |
| Learnpython | 🟩 Beginner | explanations + Online interpreter | Basics and more |
| PyFlo | 🟩 Beginner | explanations + QCM/MCQ | Basics and more |
| Kaggle : Intro to Programming | 🟩 Beginner | explanations + Online interpreter | Basics and more |
| Kaggle : Python | 🟩 Beginner | explanations + Online interpreter | Build on Introduction to programming |
| Kaggle : Pandas | 🟨 Intermediate | explanations + Online interpreter | Data manipulation skills. |
| Hackinscience | ⬛ all Levels | Online interpreter | Extensive range of topics |
| W3school | ⬛ all Levels | explanations + Online interpreter | Extensive range of topics |
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Dive Into Python 3 | Mark Pilgrim | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Playground and Cheatsheet for Learning Python | Oleksii Trekhleb | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Python Programming Beginner Tutorials | Corey Schafer | 🟩 Beginner | Video Tutorials | Basics and more |
| Python Tutorials from PythonSpot | PythonSpot | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Python Tutorials from Tutorialspoint | Tutorialspoint | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Python Tutorials from studytonight | Study Tonight | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Python Tutorials from ThePythonGuru | ThePythonGuru | 🟩 Beginner | Code examples with explanations. | Basics and more |
| Python for you and me | Kushal Das | 🟩 Beginner | Code examples with explanations. | Basics and more |
| RealPython | Real Python | ⬛ all Levels | Video Tutorials | Extensive range of topics |
📖 Book References
| Book Name | Authors | Audience | Strengths | Topics |
|---|---|---|---|---|
| A Byte of Python | Swaroop C H | 🟩 Beginner | Easy to understand, gentle, thorough | Python fundamentals and problem solving |
| Automate the Boring Stuff with Python | Al Sweigart | 🟩 Beginner | Practical applications, easy to follow | Python basics, CSV, PDF, Excel, web scraping, images, email, debugging, and more. |
| How To Code in Python | Lisa Tagliaferri, Pankaj | 🟩 Beginner | Practical, digestable, pleasant | Python basics, installation, debugging logging, data types, hints and tips. |
| Learning Python | Mark Lutz | 🟩 Beginner | Broad and deep exploration of Python. | Python basics, into advanced Python features |
| Problem Solving with Algorithms and Data Structures using Python | Brad Miller, David Ranum | 🟩 Beginner | Classic concepts, topically diverse, smart. | Data structures, algorithms, fundamentals of Python |
| Python for you and me | Kushal Das | 🟩 Beginner | step-by-step pace, contains variety | Python fundamentals, editors, PEP8, testings, NeoPixels, command line interfaces. |
| The Hitchhiker’s Guide to Python! | Kenneth Reitz, Trey Hunner | 🟩 Beginner | Practical, enjoyable, broad. | Python basics, installation, virtual environments, project structure, coding style, documentation, packaging, GUI development, command line interface development, and much more. |
| Intermediate Python | Muhammad Yasoob Ullah Khalid | 🟨 Intermediate | dvanced yet understandable concepts, unique among Python programming books | Debugging, exception handling, functional programming, mutable/immutable types, and much more. |
| Python Data Science Handbook | Jake VanderPlas | 🟨 Intermediate | nerdy and practical | Numpy, Pandas, Matplotlib, machine learning, and other hip subject matter |
| Architecture Patterns with Python | Harry J.W. Percival, Bob Gregory | 🟧 Advanced | Explains deep concepts in thorough but understandable ways, introduces advanced design concepts | Test Drive Development, Domain Driven Design, microservices |
- Awesome Biological Image Analysis : A curated list of softwares, tools, pipelines, plugins etc. for image analysis related to biological questions.
- Awesome Image Quality Assessment (IQA) : A comprehensive collection of IQA papers.
- Awesome Image Distortion Correction : A curated list of resources on handling Rolling Shutter effects and Radial Distortions.
- Awesome Neuron Segmentation in EM Images : A curated list of resources for 3D segmentation of neurites in EM images.
- Bioimage.io : Website for the BioImage Model zoo -- a model zoo for bioimage analysis.
- Bio-image Analysis Notebooks : Python Jupyter notebooks for BioImageAnalysis, GPU-accelerated image processing, bio-image data science and more.
- Segmentation Models : Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
- Napari : a fast, interactive viewer for multi-dimensional images in Python.
- Scikit-Image : Image processing in Python.
- Automate Cellpose GUI Opening 🟩 Beginner
- Intersection over Union (IoU): Evaluating Segmentation Algorithm Performance 🟩 Beginner
- Mastering Cellpose: From Installation to Advanced Features 🟩 Beginner
Note
the Beta release of Cellpose-gradio, a user-friendly interface for using Cellpose, is now available on GitHub: https://github.com/LSeu-Open/Cellpose_Gradio.
To make it easy for everyone to get started, I've automated the installation and launching process with simple scripts.
- Automate Cellpose GUI Opening: Windows Users (.bat)
- Automate Cellpose GUI Opening: MacOS/Linux Users (.sh)
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Batch processing | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Basics on how to process multiple images. |
| Cell classification | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Feature extraction and afterwards machine learning algorithms for differentiating objects. |
| Colocalization | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Counting cells according to their signal expression in multiple channels. |
| Deep Learning based image segmentation | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | deep learning based algorithms for image segmentation. |
| Feature extraction | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Retrieving quantitative measurements from image data. |
| Image segmentation | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Subdividing an image into multiple groups of pixels having different characteristics. |
| Machine learning for image segmentation | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Classical machine learning for pixel classification, object segmentation and for generating probability maps. |
| Scikit-image: image processing | Emmanuelle Gouillart | 🟩 Beginner | Code examples with explanations. | Basics of Scikit-image and more |
| Scikit Image Tutorials | scikit-image | 🟩 Beginner | Code examples with explanations. | A collection of tutorials for the scikit-image package. |
| Segmentation post-processing | haesleinhuepf | 🟩 Beginner | Code examples with explanations. | Post-process segmentation results. |
| Graphical user interfaces | haesleinhuepf | 🟨 Intermediate | Code examples with explanations. | build custom user interfaces |
📷 Video Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Cellpose 2.0 tutorial: how to train your own cellular segmentation model | Carsen Stringer | 🟩 Beginner | Youtube tutorial | Human-in-the-loop pipeline for quickly prototyping new specialist models. |
| Cellpose GPU installation for QuPath and Fiji | Thierry Pécot | 🟩 Beginner | Youtube tutorial | Install Cellpose to be processed with the GPÜ within QuPath and Fiji |
| Feature extraction: Youtube Video | haesleinhuepf | 🟩 Beginner | Youtube tutorial | Retrieving quantitative measurements from image data. |
| FIJI for Quantification: Cell Segmentation | Melbourne Advanced Microscopy Facility | 🟩 Beginner | Youtube tutorial | Cell Segmentation in Fiji |
| Introduction to QuPath | Zbigniew Mikulski | 🟩 Beginner | Youtube tutorial | Major concepts and tools in QuPath |
| Nuclei segmentation based on stardist with QuPath | Thierry Pécot | 🟩 Beginner | Youtube tutorial | Segment nuclei via Stardist in a multiplexed image with QuPath |
- AI Expert Roadmap : Roadmap to becoming an Artificial Intelligence Expert.
- Awesome Data Science : An awesome Data Science repository to learn and apply for real world problems.
- Awesome Python Data Science : Probably the best curated list of data science software in Python.
- Data Analysis Script : Data Science Projects Using Python and a little R. Code and Notebooks for numerous data science projects.
- Data Science for Beginners : 10 Weeks, 20 Lessons, Data Science for All!
- Data Science IPython Notebooks : Data science Python notebooks on Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
- Python Data Science Handbook : Python Data Science Handbook: full text in Jupyter Notebooks.
- Python Data Science Tutorials : Common data analysis and machine learning tasks using python.
🎮 Online Interactive courses
| Website | Audience | Format | Topics |
|---|---|---|---|
| w3schools : Data Science Tutorials | 🟩 Beginner | explanations + Online interpreter | Basics and more |
| Kaggle : Feature Engineering | 🟨 Intermediate | explanations + Online interpreter | Mutual information, Clustering and Principal Component Analysis and more |
| Kaggle : Data Cleaning | 🟨 Intermediate | explanations + Online interpreter | Missing Values, Scaling, Normalization and more |
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Data Science Tutorial for Beginners | DATAI Team | 🟩 Beginner | Notebook + explainations | Basics and more |
| Data Science Full Course For Beginners | codebasics | 🟩 Beginner | Youtube videos tutorials | Everything, from basics to advanced topics |
| Learn Data Science from Scratch | DataFlair | ⬛ all Levels | Code examples with explanations | Everything, from basics to advanced topics |
📖 Book References
| Book Name | Authors | Audience | Topics |
|---|---|---|---|
| Foundations of Data Science | Avrim Blum, John Hopcroft, and Ravindran Kannan | 🟩 Beginner | basics from mathematical perspective |
| Feature Engineering and Selection: A Practical Approach for Predictive Models | Max Kuhn and Kjell Johnson | 🟨 Intermediate | measuring performance, tuning parameters, model optimization, exploratory visualization, and more |
- Awesome Dataviz : A curated list of data science, analysis and visualization tools.
- Awesome Data Science viz : A curated list of awesome data visualization libraries and resources.
- Exploration and Visualization of Data With Python : Methods of data exploration and visualization using Python.
- Python Data Viz workshop : A workshop on data visualization in Python with notebooks and exercises for following along.
- Awesome ggplot2 : ggplot2 is a popular open-source plotting system for the statistical programming language R. A curated list of awesome ggplot2 tutorials, packages etc.
- Matplotlib Tutorials : Matplotlib is a popular Python library used to create static, animated, and interactive 2D and 3D visualizations of data. 🟩 Beginner
- Plotly Tutorials : Plotly is an open-source graphing library that enables users to create high-quality, interactive plots, charts, and graphs in Python, R, and MATLAB. 🟩 Beginner
- Seaborn Tutorials : Seaborn is a Python library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. 🟩 Beginner
- Visualization using Pandas : Pandas is a powerful open-source library in Python for data manipulation and analysis. Learn how to visualize data with Pandas. 🟩 Beginner
- Applied ML : Papers & tech blogs by companies sharing their work on data science & machine learning in production.
- Awesome Machine Learning : A curated list of awesome Machine Learning frameworks, libraries and software.
- Best-of Machine Learning with Python : A ranked list of awesome machine learning Python libraries. Updated weekly.
- Machine Learning & Deep Learning Tutorials : machine learning and deep learning tutorials, articles and other resources.
- Machine Learning for Beginners : 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all.
- Machine Learning from Scratch : Implements and explains core machine learning algorithms and neural networkds from scratch, assuming you know absolutly nothing.
- Caret : R equivalent of the "Scikit-Learn" package.
- cuML : Enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming.
- mlpack : An intuitive, fast, and flexible header-only C++ machine learning library with bindings to other languages.
- scikit-learn : Python module for machine learning built on top of SciPy.
🎮 Online Interactive courses
| Website | Audience | Format | Topics |
|---|---|---|---|
| DataCamp : Machine Learning in R for beginners | 🟩 Beginner | explanations + Online interpreter | introduction to the basics of machine learning in R |
| Kaggle : Introduction to Machine Learning | 🟩 Beginner | explanations + Online interpreter | Basics and more |
| Kaggle : Machine Learning Explainability | 🟩 Beginner | explanations + Online interpreter | Extract human-understandable insights from any model |
| w3schools : Machine Learning Tutorials | 🟩 Beginner | explanations + Online interpreter | Basics and more |
| Kaggle : Intermediate Machine Learning | 🟨 Intermediate | explanations + Online interpreter | Handle missing values, non-numeric values, data leakage, and more... |
| Kaggle : Time Series | 🟨 Intermediate | explanations + Online interpreter | Apply machine learning to real-world forecasting tasks. |
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Machine Learning Crash Course | 🟩 Beginner | videos tutorials + QCM/MCQ | Basics and more | |
| Machine Learning Tutorial for Beginners | DATAI Team | 🟨 Intermediate | Notebook + explainations | Basics and more |
| Testing and Debugging in Machine Learning | 🟨 Intermediate | videos tutorials + QCM/MCQ | Validate data, debug and optimize a machine learning model, and monitor its performance during development, launch, and production. |
📖 Book References
| Book Name | Authors | Audience | Topics |
|---|---|---|---|
| An Introduction to Machine Learning Interpretability | Patrick Hall and Navdeep Gill | 🟩 Beginner | Learn how to explain your model |
| Machine Learning for Humans | Vishal Maini Samer Sabri | 🟩 Beginner | Supervised Learning, Unsupervised Learning, Neural Networks and more |
| Python Machine Learning Projects | Brian Bocheron and Lisa Tagliaferri | 🟨 Intermediate | Create machine-learning projects to test your skills and build a portfolio |
| Hands-On Machine Learning with R | Bradley Boehmke & Brandon Greenwell | ⬛ all Levels | Generalized low-rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines and more |
| The Hundred-Page Machine Learning Book | Andriy Burkov | ⬛ all Levels | Fundamental Algorithms plus in-depth material, Anatomy of a Learning Algorithm, Basic Practice, Neural Networks and more |
| Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz and Shai Ben-David | ⬛ all Levels | Foundations, Theoretical to Algorithmic Applications, Additional Learning Models, and Advanced Theory |
- Awesome Deep Learning : A curated list of awesome Deep Learning tutorials, projects and communities.
- Awesome - Most Cited Deep Learning Papers : The most cited deep learning papers.
- Awesome Deep Vision : A curated list of deep learning resources for computer vision.
- Awesome Deep learning papers and other resources : Deep Learning and deep reinforcement learning research papers and some codes.
- Cvat : Annotate better with CVAT, the industry-leading data engine for machine learning.
- Deep Learning Drizzle : Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from exciting lectures.
- Deep Learning Examples : State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance.
- Supervision : Reusable computer vision tools.
- Catalyst : a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop.
- Keras : A multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch. Effortlessly build and train models for computer vision, natural language processing, audio processing...
- PyTorch : Tensors and Dynamic neural networks in Python with strong GPU acceleration.
- Tensorflow : An Open Source Machine Learning Framework for Everyone.
- Torchvision : torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
🎮 Online Interactive courses
| Website | Audience | Format | Topics |
|---|---|---|---|
| Kaggle : Intro to Deep Learning | 🟨 Intermediate | explanations + Online interpreter | Use TensorFlow and Keras to build and train neural networks for structured data |
| Kaggle : Computer Vision | 🟨 Intermediate | explanations + Online interpreter | Apply machine learning to real-world forecasting tasks. |
💻 Tutorials
| Title | Authors | Audience | Format | Topics |
|---|---|---|---|---|
| Basics of Pytorch | DATAI Team | 🟨 Intermediate | Notebook + explainations | Basics and more |
| Coding TensorFlow | TensorFlow | 🟨 Intermediate | Youtube videos tutorials | Large panel of topics |
| Deep Learning Tutorial for Beginners | DATAI Team | 🟨 Intermediate | Notebook + explainations | Basics and more |
| DL Zero to Hero | TensorFlow | 🟨 Intermediate | Youtube videos tutorials | Few basics on Tensorflow coding |
| Roboflow Notebooks | Roboflow | 🟨 Intermediate | Notebook + explainations | SOTA computer vision models and techniques |
📖 Book References
| Book Name | Authors | Audience | Topics |
|---|---|---|---|
| Dive into Deep Learning | Aston zhang, zachary c. Lipton, mu li, and alexander j. Smola | 🟨 Intermediate | implementations with PyTorch, NumPy/MXNet, JAX, and TensorFlow. |
(TO BE DISCUSSED)