1. Introduction

In this tutorial, we will introduce you to Automated Machine Learning (AutoML) and hyperparameter optimization. Our goal is to simplify the model development process and improve model performance by utilizing these techniques.

You will learn:
- How to use AutoML for model optimization
- The basics of hyperparameter tuning
- How to apply these techniques in your machine learning projects

Prerequisites:
- Basic understanding of Python programming
- Familiarity with Machine Learning concepts
- Installed Python 3, scikit-learn, and auto-sklearn libraries

2. Step-by-Step Guide

2.1 Automated Machine Learning (AutoML)

AutoML is a process of automating the tasks associated with Machine Learning model development. It helps in selecting the right algorithm, feature selection, and hyperparameter tuning, thus simplifying the model development process.

2.2 Hyperparameter Optimization

Hyperparameters are parameters that are not learned from the data but are set prior to the training process. Hyperparameter tuning or optimization means finding the combination of hyperparameters that gives the best performance for a machine learning model.

3. Code Examples

We will use the Auto-sklearn library, which is an extension of the popular Scikit-learn library in Python. We'll use the digits dataset from scikit-learn for our examples.

3.1 Installing auto-sklearn

pip install auto-sklearn

3.2 Importing necessary libraries

from sklearn import datasets
from autosklearn import classification

3.3 Loading the dataset

X, y = datasets.load_digits(return_X_y=True)

3.4 Creating an AutoML object

automl = classification.AutoSklearnClassifier()

3.5 Training the model

automl.fit(X, y)

3.6 Making predictions

predictions = automl.predict(X)

4. Summary

In this tutorial, we covered the basics of Automated Machine Learning (AutoML) and hyperparameter optimization. We also learned how to use auto-sklearn for model optimization.

To continue your learning journey, you can explore more advanced topics in AutoML and try out different datasets and ML tasks.

5. Practice Exercises

5.1 Exercise 1

Use AutoML to train a model on the Iris dataset from scikit-learn and make predictions.

5.2 Exercise 2

Perform hyperparameter optimization on a Random Forest Classifier using the digits dataset. Compare the performance of the optimized model with a model using default parameters.

5.3 Exercise 3

Use AutoML to perform regression on the Boston Housing dataset from scikit-learn. Compare the performance of the AutoML model with a manually created Linear Regression model.

Note: Always make sure to divide your data into training and testing sets before training your model. This allows you to test your model's performance on unseen data.