Artificial Intelligence is an innovative and versatile field that enables machines to mimic human intelligence.
It's applicable in numerous sectors like healthcare, finance, and entertainment, significantly impacting how we live and work.
Discover the fundamentals of Machine Learning, one of the most innovative and widely-used applications of Artificial Intelligence. Learn about what it really means, and optionally explore advanced topics such as K-Nearest Neighbors to build a robust foundation for your AI journey.
Understand the significance of Python and Scikit-learn in AI programming. Learn how to set up and practice with Python and Jupyter to effectively manage your Machine Learning workflows.
Dive into the world of predictive models in AI. Learn how to create predictions with Python, from salary forecasting to customer churn and even Apple stock predictions, enhancing the practicality and versatility of your AI applications.
Get hands-on experience with AI through a series of practical exercises. Learn how to apply Machine Learning concepts in real-life scenarios and solve tangible problems, bolstered by supportive resources like cheat sheets and further learning suggestions.
All notebooks are available in /lab repository.
If you want to go further and do not know what course to take next, this great blog post from @lewagon might answer all your question, just as it did mines.
Dataset
- Stock Predictions: IEX Cloud
- Stock data can also be collected by scraping finance web sites (ex: finance.yahoo.com ) using Python API package such as Beautiful Soup.
- Here's an hands-on notebook with Beautiful Soup & Pandas DataFrame
Tools
- setosa.io (blog)
- ml-playground.com (model playground)
Virtual environment:
ML API:
- Facebook Prophet: https://facebook.github.io/prophet/docs/quick_start.html
Papers: