This is the repository for the LinkedIn Learning course Artificial Intelligence Foundations: Machine Learning. The full course is available from LinkedIn Learning.
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
- To use these exercise files, you must have the following installed:
- Jupyter Notebook environment in the cloud or locally, with the necessary libraries installed
- Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
Before running the code, make sure to install the following dependencies in your environment.
- pandas - %pip install pandas
- matplotlib - %pip install matplotlib
- seaborn - %pip install seaborn
- scikit-learn - %pip install scikit-learn
- numpy - %pip install numpy
- xgboost - %pip install xgboost
Kesha Williams
Software Engineer and Speaker
Check out my other courses on LinkedIn Learning.
