Introduction to Machine Learning with Python
If you're a programmer who wants to learn and apply machine learning techniques, this workshop is for you. We'll explore machine learning concepts and see the steps to process and prepare data for machine learning, train and deploy a machine learning model, and generate real-time predictions. We will explore, Scikit-Learn, a machine learning library that makes it easy to train custom models with just a few lines of code. You'll walk away understanding how to leverage machine learning to solve business problems and the libraries available to build machine learning solutions. No Ph.D. or machine learning experience is required!
- Machine learning techniques: regression, classification, clustering, and more
- Machine learning library: Scikit-Learn
- Machine learning algorithms: Linear Regression, Radom Forest Regressor, and XGBoost
- Data science libraries: NumPy, Pandas, Matplotlib, and more
- Python-based IDE: Jupyter Notebook
The workshop is intended for developers and software engineers looking to transition to machine learning.
Day 1: Machine learning and data
- Machine learning overview and benefits
- Real-world uses of machine learning
- Workshop project overview
- Machine learning lifecycle
- Collect and store training data
- Prepare and clean training data
- Visualize and analyze training data
Day 2: Model training and deployment
- Feature Engineering
- Built-in learning algorithms
- Train and tune the model
- Evaluate and qualify the model
- Deploy and host the model
- Make predictions using the model
- Monitor and debug the model
- Detect and prevent bias
The ability to read basic Python code is necessary, or familiarity with another programming language is strongly recommended. No prior experience with machine learning is required.
Attendees must bring their laptops with access to Jupyter Notebooks and Python.