# practicalAI **Repository Path**: Cloud-Rambler/practicalAI ## Basic Information - **Project Name**: practicalAI - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-08-14 - **Last Updated**: 2021-10-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

 Made With ML

Applied ML Β· MLOps Β· Production
Join 30K+ developers in learning how to responsibly deliver value with ML.

     
πŸ”₯  Among the top ML repositories on GitHub

## Foundations Learn the foundations of ML through intuitive explanations, clean code and visuals. - Lessons: https://madewithml.com/#foundations - Code: [GokuMohandas/MadeWithML/tree/master/notebooks](https://github.com/GokuMohandas/MadeWithML/tree/master/notebooks)
πŸ›   Toolkit πŸ”₯  Machine Learning πŸ€–  Deep Learning
Notebooks Linear Regression CNNs
Python Logistic Regression Embeddings
NumPy Neural Network RNNs
Pandas Data Quality Transformers
PyTorch Utilities
πŸ“†  More topics coming soon!
Subscribe for our monthly updates on new content.
## MLOps Learn how to apply ML to build a production grade product to deliver value. - Lessons: https://madewithml.com/courses/mlops/ - Code: [GokuMohandas/MLOps](https://github.com/GokuMohandas/MLOps)
πŸ“¦  Product πŸ“  Scripting ♻️  Reproducibility
Objective Organization Git
Solution Packaging Pre-commit
Iteration Documentation Versioning
πŸ”’  Data Styling Docker
Labeling Makefile πŸš€  Production
Preprocessing Logging Dashboard
Exploratory data analysis πŸ“¦  Interfaces CI/CD workflows
Splitting Command-line Infrastructure
Augmentation RESTful API Monitoring
πŸ“ˆ  Modeling βœ…  Testing Feature store
Evaluation Code Pipelines
Experiment tracking Data Continual learning
Optimization Models
πŸ“†  New lessons every month!
Subscribe for our monthly updates on new content.
## FAQ ### Who is this content for? - `Software engineers` looking to learn ML and become even better software engineers. - `Data scientists` who want to learn how to responsibly deliver value with ML. - `College graduates` looking to learn the practical skills they'll need for the industry. - `Product Managers` who want to develop a technical foundation for ML applications. ### What is the structure? Lessons will be released weekly and each one will include: - `intuition`: high level overview of the concepts that will be covered and how it all fits together. - `code`: simple code examples to illustrate the concept. - `application`: applying the concept to our specific task. - `extensions`: brief look at other tools and techniques that will be useful for difference situations. ### What makes this content unique? - `hands-on`: If you search production ML or MLOps online, you'll find great blog posts and tweets. But in order to really understand these concepts, you need to implement them. Unfortunately, you don’t see a lot of the inner workings of running production ML because of scale, proprietary content & expensive tools. However, Made With ML is free, open and live which makes it a perfect learning opportunity for the community. - `intuition-first`: We will never jump straight to code. In every lesson, we will develop intuition for the concepts and think about it from a product perspective. - `software engineering`: This course isn't just about ML. In fact, it's mostly about clean software engineering! We'll cover important concepts like versioning, testing, logging, etc. that really makes something production-grade product. - `focused yet holistic`: For every concept, we'll not only cover what's most important for our specific task (this is the case study aspect) but we'll also cover related methods (this is the guide aspect) which may prove to be useful in other situations. ### Who is the author? - I've deployed large scale ML systems at Apple as well as smaller systems with constraints at startups and want to share the common principles I've learned. - Connect with me on Twitter and LinkedIn ### Why is this free? While this content is for everyone, it's especially targeted towards people who don't have as much opportunity to learn. I believe that creativity and intelligence are randomly distributed while opportunities are siloed. I want to enable more people to create and contribute to innovation.
To cite this content, please use: ```bibtex @misc{madewithml, author = {Goku Mohandas}, title = {Made With ML}, howpublished = {\url{https://madewithml.com/}}, year = {2021} } ```