Hands-On Machine Learning with Auto-Keras

Video description

Develop state-of-the-art machine learning models with just a few lines of code!

About This Video

  • Learn how to leverage the power of auto machine learning by applying Auto-Keras to real-world problems and data sets
  • Find out how Auto-Keras can help you obtain close to state-of-the-art performance on ML tasks with only a few lines of code
  • Explore time, resource, and development quality benefits that Auto-Keras can bring to your organization

In Detail

If you want to build efficient models using the open-source Auto-Keras library, then this course is perfect for you. It will teach you how to use Auto-Keras to build custom machine learning and AI models effectively, even with limited machine Learning knowledge.

You will learn how to train a network automatically and evaluate it using Auto-Keras. You will begin by installing Auto-Keras and using it to implement basic algorithms. You will then train more advanced models as you progress to state-of-the-art techniques.

By the end of the course, you will be confident about using Auto-Keras to build custom machine learning models for your organization.

Publisher resources

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Table of contents

  1. Chapter 1 : Getting Started with Auto-Keras
    1. The Course Overview 00:05:59
    2. The Need for Auto-Keras 00:02:30
    3. Installing Auto-Keras 00:04:13
    4. The MNIST Data Set 00:07:52
    5. An Auto-Keras Classifier for MNIST 00:05:58
    6. Making Predictions on Our Own Data 00:06:29
  2. Chapter 2 : Artificial Neural Network Models
    1. ANN Generation 00:05:07
    2. ANN Classifier for Identifying Handwritten Digits 00:09:22
    3. ANN Model for Predicting House Prices 00:06:53
    4. Visualizing the Best ANN 00:04:50
    5. Exploring More Data Sets 00:07:27
  3. Chapter 3 : Convolutional Neural Network Models
    1. CNN Generation 00:03:32
    2. CNN Classifiers for Identifying Handwritten Digits 00:05:51
    3. CNN Classifiers for Identifying Other Objects 00:04:26
    4. CNN Regressor for MNIST 00:04:08
    5. Visualizing the Best CNN 00:03:14
  4. Chapter 4 : Text Classification and Regression
    1. Text-Based Tasks 00:03:02
    2. Text Classification for Reuters News 00:08:15
    3. Text Classification for Spam Filtering 00:04:21
    4. Text Regression on a Real-World Data Set 00:04:48
    5. Generating Our Own Data Set 00:04:12
  5. Chapter 5 : Sentiment Analysis
    1. Sentiment Analysis Basics 00:02:48
    2. Auto-Keras’ Pretrained Models for Sentiment Analysis on a Real-World Data Set 00:04:57
    3. The Pretrained Models on Some of Our Own Data 00:04:19
    4. Auto-Keras Classifier for Sentiment Analysis 00:02:08
    5. Auto-Keras Regressor for Sentiment Analysis 00:04:05
  6. Chapter 6 : Object Detection
    1. Object Detection Basics 00:02:10
    2. Using Auto-Keras’ Pretrained Models for Object Detection 00:04:40
    3. Building Our Own Data Set for Use with the Pretrained Model 00:07:40
    4. Deploying a Model 00:02:45
  7. Chapter 7 : Topic Classification
    1. Basics of Topic Classification 00:01:35
    2. Using Auto-Keras’ Pretrained Models for Topic Classification 00:03:03
    3. Building Our Own Dataset for Use with the Pretrained Model 00:04:20
    4. Our Own Auto-Keras Model for Topic Classification 00:03:32

Product information

  • Title: Hands-On Machine Learning with Auto-Keras
  • Author(s): Vlad Sebastian Ionescu
  • Release date: November 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838646738