Automated Machine Learning with AutoGluon Library in Python
1: 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
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.
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.
Who this course is for:
- Data scientists, machine learning engineers, and software developers who are looking to automate their machine learning tasks
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming.
He has publications and patents in various fields such as microfluidics, materials science, and data science.
Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations.
Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.
NumPy is regarded as being one of the most widely used and best Python libraries for Machine Learning. Other libraries, such as TensorFlow and Keras, use NumPy to implement various operations on tensors.
INSTRUCTOR
Jose Portilla
