AutoML with Auto-sklearn Training Course

Course Code

autosklearn

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 (onsite or remote) 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

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