Automated Machine Learning With Autogluon Library In Python

Discover how to easily automate entire machine learning pipelines with the extremely powerful Autogluon library from AWS

What you'll 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.

Requirements

Some Python experience required.

Previous machine learning experience helpful, but no required.

Description

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.

Overview

Section 1: Course Overview and Introduction

Lecture 1 Course Downloads and Files

Lecture 2 Course Welcome

Lecture 3 Course Curriculum Overview

Lecture 4 AutoGluon Overview

Section 2: Tabular Data - Classification and Regression

Lecture 5 Introduction to Tabular Data Section

Lecture 6 OPTIONAL: Supervised Learning Overview

Lecture 7 AutoGluon Classification Part One: Data and Split

Lecture 8 AutoGluon Classification Part Two: Training the Model

Lecture 9 OPTIONAL: Train Test Splits and Cross-Validation

Lecture 10 AutoGluon Classification Part Three: Validation

Lecture 11 AutoGluon Classification Part Four: Interpretability

Lecture 12 OPTIONAL: Classification Metrics

Lecture 13 AutoGluon Regression: Data, Split, Training, and Validation

Lecture 14 OPTIONAL: Regression Metrics

Lecture 15 AutoGluon Fit Parameters: Inference Constraints and Manual Hyperparameters

Lecture 16 Advanced AutoGluon: Presets and Deployment

Lecture 17 Advanced AutoGluon: Custom Feature Engineering Pipeline

Section 3: Multi-Modal Datasets

Lecture 18 Introduction to Multi-Modal Data Problems

Lecture 19 Optional: Download Trained Book Rating Prediction Model Here

Lecture 20 Natural Language - MultiClass Problem - Part One

Lecture 21 Natural Language - MultiClass Problem - Part Two

Lecture 22 Optional: Download Trained Sentiment Analysis Model

Lecture 23 MultiModalPredictor on Binary Class with Natural Language Text

Section 4: Time Series Forecasting

Lecture 24 Introduction to Time Series

Lecture 25 Overview of Time Series in AutoGluon

Lecture 26 Single Variate Time Series Forecasting in AutoGluon - Part One

Lecture 27 Single Variate Time Series Forecasting in AutoGluon - Part Two

Lecture 28 Single Variate Time Series Forecasting in AutoGluon - Part Three

Lecture 29 Known Covariate Time Series Forecasting in AutoGluon - Part One

Lecture 30 Known Covariate Time Series Forecasting in AutoGluon - Part Two

Lecture 31 Past Covariate Time Series Forecasting

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