Tag: AutoML

Course content

• Introduction
• The Three Types of Anomalies
• Anomaly Detection – Time Series
• Anomaly Detection –
• Unsupervised DBSCAN
• Anomaly Detection – Unsupervised Isolation Forest
• Anomaly Detection – Supervised
• Anomaly Detection – Images
• Anomaly Detection Using Deep Learning
• PyOD: A comparison of 10 algorithms
• Predicting High Impact Low Volume Events: Predictive Maintenance
• No Code (AutoML) approach to anomaly detection using PowerBl
• Machine Learning
• Bonus Lecture

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

Duration

14 hours (usually 2 days including breaks)

Requirements

• Experience working with machine learning models.
• Python programming experience is helpful but not necessary.

Audience

• Data analysts
• Subject matter experts (domain experts)
• Data scientists

Overview

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras 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.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning 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.
• To learn more about Auto-Keras, please visit: https://autokeras.com/

Course Outline

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

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-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

Duration

7 hours (usually 1 day including breaks)

Requirements

• Basic knowledge of data analytics
• Familiarity with machine learning

Audience

• Data scientists
• Data analysts
• Developers

Overview

Google Cloud AutoML is a machine learning (ML) platform that enables users to build, train, and deploy customized ML models specific to their business needs.

This instructor-led, live training (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

By the end of this training, participants will be able to:

• Explore the AutoML product line to implement different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

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

Overview of AutoML Features and Architecture

• Google’s ML ecosystem
• AutoML line of products

Working With Google’s Machine Learning Ecosystem

• Applications for AutoML products
• Challenges and limitations

Evaluating Content Using AutoML Natural Language

• Preparing datasets
• Creating and deploying models
• Text and document training (classification, extraction, analysis)

Classifying Images Using AutoML Vision

• Labeling images
• Training and evaluating models
• AutoML Vision Edge

Creating Translation Models Using AutoML Translation

• Preparing datasets (source and target language)
• Creating and managing models
• Testing models

Making Predictions from Trained Models

• Analyzing documents
• Image prediction
• Translating content

Exploring Other AutoML Products

• AutoML Tables for structured data
• AutoML Video Intelligence for videos

Troubleshooting

Summary and Conclusion

Duration

14 hours (usually 2 days including breaks)

Requirements

• Experience with machine learning algorithms.
• Python or R programming experience.

Audience

• Data analysts
• Data scientists
• Data engineers
• Developers

Overview

AutoML is user-friendly machine learning software that automates much of the work needed to select an ideal machine learning algorithm, its parameter settings, and pre-processing methods.

This instructor-led, live training (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.

By the end of this training, participants will be able to:

• Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
• Train high quality machine learning models.
• Efficiently solve different types of supervised machine learning problems.
• Write just the necessary code to initiate the automated machine learning process.

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.
• To learn more about AutoML, please visit: https://www.automl.org/

Course Outline

Introduction

Setting up a Working Environment

Overview of AutoML Features

How AutoML Explores Algorithms

• Gradient Boosting Machines (GBMs), Random Forests, GLMs, etc.

Solving Problems by Use-Case

Solving Problems by Training Data Type

Data Privacy Considerations

Cost Considerations

Preparing Data

Working with Numeric and Categorical Data

• IID tabular data (H2O AutoML, auto-sklearn, TPOT)

Working with Time Dependent Data (Time-Series Data)

Classifying Raw Text

Classifying Raw Image Data

• Deep Learning and Neural Architecture Search (TensorFlow, PyTorch, Auto-Keras, etc.)

Deploying an AutoML Method

A Look at the Algorithms Inside AutoML

Ensembling Different Models Together

Troubleshooting

Summary and Conclusion