Automated Machine Learning with AutoGluon Library in Python

Automated Machine Learning with AutoGluon Library in Python, available at $89.99, has an average rating of 4.9, with 37 lectures, based on 122 reviews, and has 1517 subscribers.

You will learn about Understand the basics of the Autogluon Python library and its capabilities for automating machine learning tasks. Learn how to install and set up the Autogluon Python library in your local environment. Develop skills in data preparation and cleaning processes that are critical for successful machine learning outcomes using Autogluon. Discover best practices for selecting and configuring machine learning models to achieve optimal results with minimal effort. Explore how to use Autogluon to create high-accuracy models for image classification tasks, including object detection, segmentation, and classification. Understand how to use Autogluon to perform natural language processing (NLP) tasks such as sentiment analysis. Learn how to train and deploy time series models using Autogluon to make accurate predictions for future events or trends. Gain hands-on experience in using Autogluon to analyze tabular data and build predictive models for business applications and financial forecasting. This course is ideal for individuals who are Data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks It is particularly useful for Data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks.

Enroll now: Automated Machine Learning with AutoGluon Library in Python

Summary

Title: Automated Machine Learning with AutoGluon Library in Python

Price: $89.99

Average Rating: 4.9

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics of the Autogluon Python library and its capabilities for automating machine learning tasks.
Learn how to install and set up the Autogluon Python library in your local environment.
Develop skills in data preparation and cleaning processes that are critical for successful machine learning outcomes using Autogluon.
Discover best practices for selecting and configuring machine learning models to achieve optimal results with minimal effort.
Explore how to use Autogluon to create high-accuracy models for image classification tasks, including object detection, segmentation, and classification.
Understand how to use Autogluon to perform natural language processing (NLP) tasks such as sentiment analysis.
Learn how to train and deploy time series models using Autogluon to make accurate predictions for future events or trends.
Gain hands-on experience in using Autogluon to analyze tabular data and build predictive models for business applications and financial forecasting.

Who Should Attend

Data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks

Target Audiences

Data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks

Welcome to our online course on Autogluon!

Are you tired of spending countless hours performing repetitive and time-consuming tasks when it comes to machine learning? Do you want to automate your machine learning tasks and achieve strong predictive performance in your applications with minimal effort? Look no further than Autogluon.

Our comprehensive online course is designed to provide you with the skills and knowledge necessary to use the Autogluon Python library for automating machine learning tasks. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

Throughout the course, you will learn how to install and set up the Autogluon Python library in your local or cloud-based environment. You will also develop skills in data preparation and cleaning processes that are critical for successful machine learning outcomes using Autogluon. Additionally, we will cover best practices for selecting and configuring machine learning models to achieve optimal results with minimal effort.

Our course will also take a deep dive into using Autogluon to create high-accuracy models for image classification tasks, including object detection, segmentation, and classification. You will also learn how to use Autogluon to perform natural language processing (NLP) tasks such as sentiment analysis, language translation, and named entity recognition.

But that’s not all! We will also cover how to train and deploy time series models using Autogluon to make accurate predictions for future events or trends. You’ll gain hands-on experience in using Autogluon to analyze tabular data and build predictive models for business applications and financial forecasting.

By the end of this course, you will have developed skills in model interpretation and evaluation techniques to assess the accuracy and reliability of machine learning models created using Autogluon. You’ll be able to apply the knowledge gained from this course to real-world scenarios, such as developing predictive models for customer churn, fraud detection, or personalized recommendations.

Our course is designed for data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks and achieve strong predictive performance in their applications. Prior experience with Python programming and machine learning concepts is recommended but not required.

Enroll today in our comprehensive online course and learn how to use Autogluon to automate your machine learning tasks and achieve strong predictive performance in your applications with minimal effort.

Course Curriculum

Chapter 1: Course Overview and Introduction

Lecture 1: Course Downloads and Files

Lecture 2: Course Welcome

Lecture 3: Course Curriculum Overview

Lecture 4: AutoGluon Overview

Chapter 2: Tabular Data – Classification and Regression

Lecture 1: Introduction to Tabular Data Section

Lecture 2: OPTIONAL: Supervised Learning Overview

Lecture 3: AutoGluon Classification Part One: Data and Split

Lecture 4: AutoGluon Classification Part Two: Training the Model

Lecture 5: OPTIONAL: Train Test Splits and Cross-Validation

Lecture 6: AutoGluon Classification Part Three: Validation

Lecture 7: OPTIONAL: Understanding Classification Metrics

Lecture 8: AutoGluon Classification Part Four: Interpretability

Lecture 9: AutoGluon Regression: Data, Split, Training, and Validation

Lecture 10: OPTIONAL: Regression Metrics

Lecture 11: AutoGluon Fit Parameters: Inference Constraints and Manual Hyperparameters

Lecture 12: Advanced AutoGluon: Presets and Deployment

Lecture 13: Advanced AutoGluon: Custom Feature Engineering Pipeline

Chapter 3: Multi-Modal Datasets

Lecture 1: Introduction to Multi-Modal Data Problems

Lecture 2: Optional: Download Trained Book Rating Prediction Model Here

Lecture 3: Natural Language – MultiClass Problem – Part One

Lecture 4: Natural Language – MultiClass Problem – Part Two

Lecture 5: Optional: Download Trained Sentiment Analysis Model

Lecture 6: MultiModalPredictor on Binary Class with Natural Language Text

Lecture 7: Optional: Download Trained MultiModal Image, Natural Language, and Tabular Model

Lecture 8: MultiModalPredictor with Image, Natural Text, and Tabular Data

Chapter 4: Time Series Forecasting

Lecture 1: Introduction to Time Series

Lecture 2: Overview of Time Series in AutoGluon

Lecture 3: Single Variate Time Series Forecasting in AutoGluon – Part One

Lecture 4: Single Variate Time Series Forecasting in AutoGluon – Part Two

Lecture 5: Single Variate Time Series Forecasting in AutoGluon – Part Three

Lecture 6: Known Covariate Time Series Forecasting in AutoGluon – Part One

Lecture 7: Known Covariate Time Series Forecasting in AutoGluon – Part Two

Lecture 8: Past Covariate Time Series Forecasting

Chapter 5: Image Classification and Object Detection

Lecture 1: Introduction to Images

Lecture 2: Google Colab Notebook Online Set-Up

Lecture 3: Image Classification

Lecture 4: OpenAI CLIP with AutoGluon

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Machine Learning Training

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3 stars: 2 votes
4 stars: 20 votes
5 stars: 100 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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