Applied Machine Learning: Foundations

This is the repository for the LinkedIn Learning course Applied Machine Learning: Foundations. The full course is available from LinkedIn Learning.

AI models are transforming the workplace. Knowing what’s going behind those models can help you apply machine learning (ML) techniques more effectively. In this course, instructor Matt Harrison shows you how to get started mastering the essentials of machine learning using the power of the Python programming language.

Explore the fundamentals of an end-to-end machine learning application, as you gain hands-on experience of data exploration, data processing, model creation, model evaluation, model tuning, and model deployment with MLFlow. Along the way, test out your new coding skills in the practice challenges at the end of each section.

Getting Started

This project can be set up and run in two ways: using GitHub Codespaces for a cloud-based environment, or locally on your machine by installing the required dependencies. Follow the instructions below to get started with the method that best suits your needs.

Option 1: Using GitHub Codespaces

GitHub Codespaces provides a complete, configurable dev environment on top of a powerful VS Code interface. It's an excellent option for quickly starting development without the need to set up your local environment.

1. Open the project in Codespaces: Navigate to the GitHub page of the project and click the "Code" button. Select "Open with Codespaces" > "New codespace". This will set up a new cloud-based development environment pre-configured for this project.

2. Wait for installation: The installation takes a few minutes after the Codespace launches. The terminal at the bottom of VSCode will be spinning for a little bit getting all of the dependencies built and installed.

3. Open up ml-foundations.ipynb in VSCode: The video will walk you through this.

Option 2: Local Setup

If you prefer to work on your local machine, follow these steps to set up the project environment. You'll need Python installed on your system (refer to python.org for installation instructions).

1. Clone the repository:

   git clone https://github.com/your-username/your-project-name.git
   cd your-project-name

2. Create virtual environment: Using your favorite mechanism, create a virtual environment for Python.

3. Install dependencies: Ensure you have your virtual environment activated. Then, install the required packages using the following command:

   pip install -r requirements.txt

4. Launch Jupyter and open ml-foundations.ipynb: With the dependencies installed, you're ready to launch Juypter:

   jupyter lab

   Navigate and open the ml-foundations.ipynb notebook in Jupyter.

Instructor

Matt Harrison

Python and Data Science Corporate Trainer, Author, Speaker, Consultant

Check out my other courses on LinkedIn Learning.