Duration
14 hours (usually 2 days including breaks)
Requirements
- Experience with machine learning algorithms.
- Python programming experience.
Audience
- Data scientists
- Data analysts with a technical background
Overview
Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters.
This instructor-led, live training (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
- Automate the process of training highly efficient machine learning models.
- Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
- Use the power of machine learning to solve real-world business problems.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
Setting up a Working Environment
Installing Auto-sklearn
Anatomy of a Standard Machine Learning Workflow
How Auto-sklearn Automates the Machine Learning Workflow
Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)
Case Study: AutoML with Auto-sklearn
Downloading a Dataset
Building a Machine Learning Model
Training and Testing the Model
Tuning the Hyperparameters
Building, Training, and Testing Additional Models
Tweaking the Hyperparameters to Improve Accuracy
Configuring Auto-sklearn for Deep Learning Models
Troubleshooting
Summary and Conclusion