10 Best AutoML Tutorials

Learn AutoML

Are you looking to learn AutoML? AutoML is a process of automating the machine learning process. It has been designed to make it easier for data scientists and developers to build and deploy machine learning models. AutoML can be used to automate the selection of algorithms, pre-processing of data, feature engineering, model tuning, and more.

In this article, we will explore the ten best AutoML tutorials to help you learn AutoML in no time.

Table of Contents

  1. Coursera AutoML Tutorial.
  2. Auto-Keras AutoML Tutorial.
  3. AutoML Vision API Tutorial.
  4. Azure Machine Learning studio.
  5. H2O AutoML Tutorial.
  6. Kaggle AutoML Tutorial.
  7. AutoGluon AutoML Tutorial.
  8. Databricks AutoML Tutorial.
  9. Javatpoint AutoML Tutorial.
  10. Streamlit AutoML Tutorial.

Why Learn AutoML?

There are several reasons why you should learn AutoML. Below are just a few:

  • AutoML can be used to automate the selection of algorithms, pre-processing of data, feature engineering, model tuning
  • It has been designed to make it easier for data scientists and developers to build and deploy machine learning models
  • No more need for a Ph.D. to get into AI or Machine Learning; all you need is to learn AutoML
  • AutoML can be used in a wide range of industries, including automotive, healthcare, finance, retail, and more
  • It is an essential skill for data scientists and developers who want to get into machine learning
  • AutoML is the future of machine learning.

Now let’s explore the ten best AutoML tutorials.

Coursera AutoML Tutorial.

26,000 students are currently enrolled in this course, and it has a rating of 4.6 out of 5 stars. This course explains how to apply machine learning in Google Cloud data pipelines at various levels of customization, as well as basic principles. This training covers AutoML for little or no customization. This course introduces Notebooks and BigQuery Machine Learning (BigQuery ML) for more specialized machine learning capabilities. This course also teaches you how to use Kubeflow to productionalize machine learning solutions. Finally, learners use Qwiklabs to build machine learning models on Google Cloud.

Auto-Keras AutoML Tutorial.

This tutorial teaches students how to automate machine learning and deep learning using Auto-Keras. Auto-Keras is an open-source alternative to Google’s AutoML. Over 3000 students are enrolled, and the course has a rating of 4.84 out of 5 stars. The tutorial covers:

  • Project structure
  • Installing Auto-Keras
  • How to implement a training script with Auto-Keras
  • How to train a neural network with Auto-Keras
  • How to modify the neural network architecture
  • Tuning hyperparameters

AutoML Vision API Tutorial

This AutoML tutorial is for developers who want to use the Vision API to label and rank images automatically. You will learn to use the Vision API to detect objects in images, recognize landmarks, and read text from images. The tutorial also shows you how to train a custom vision model with your data.

Azure Machine Learning studio

This AutoML tutorial is for developers who want to use the Azure Machine Learning Studio to build and deploy machine learning models. The tutorial covers how to use Azure Machine Learning studio to:

  • Choose an algorithm
  • Pre-process data
  • Engineer features
  • Tune the model
  • Deploy the model

H2O AutoML Tutorial

This AutoML tutorial is for developers who want to use the H2O AutoML platform to build and deploy machine learning models. The tutorial covers how to:

  • Upload data
  • Select an algorithm
  • Train a model
  • Evaluate a model
  • Deploy a model

Kaggle AutoML Tutorial

This AutoML tutorial is for data scientists who want to use the Kaggle AutoML platform to build and deploy machine learning models. The tutorial covers how to:

  • Upload data
  • Select an algorithm
  • Train a model
  • Evaluate a model
  • Deploy a model

AutoGluon AutoML Tutorial

This AutoML tutorial is for developers who want to use the AutoGluon platform to build and deploy machine learning models. The tutorial covers how to:

  • Upload data
  • Select an algorithm
  • Train a model
  • Evaluate a model

Databricks AutoML Tutorial

This AutoML tutorial is for developers who want to use the Databricks platform to build and deploy machine learning models. The tutorial:

  • AutoML algorithms
  • AutoML UI
  • AutoML Python API
  • Python API specification
  • API examples
  • databricks-automl-runtime package
  • Limitations

Javatpoint AutoML Tutorial

This AutoML tutorial is for developers who want to learn all the ins and outs of building and deploying machine learning models. The tutorial covers:

  • Data Cleaning
  • Feature Selection/Feature Engineering
  • Model Selection
  • Parameter Optimization
  • Model Validation.

Streamlit AutoML Tutorial.

This step-by-step AutoML tutorial teaches machine learning developers how to deploy an AutoML model using Streamlit. The tutorial covers:

  • Project Overview
  • Development
  • Deployment
  • References

Final thoughts

When it comes to AutoML tutorials, there are a variety of resources to choose from. Whether you are a data scientist, developer, or just getting started with machine learning, there is an AutoML tutorial for you. The ten tutorials listed above cover various topics and platforms, so you can find the one that best fits your needs. And with so many different AutoML tutorials available, you can be sure to find one that is easy to follow and helps you build machine learning models that are ready for production.

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