Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Automated Machine Learning

  • Course Code: DevOps - Automated Machine Learning
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for software developers who wants to Automate data and model pipelines for faster machine learning applications

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Automated Machine Learning for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for software developers who wants to Automate data and model pipelines for faster machine learning applications 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this course, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this course, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this course to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. 

Working in a hands-on learning environment, led by our Automated Machine Learning expert instructor, students will learn about and explore: 

  • Build automated modules for different machine learning components 
  • Understand each component of a machine learning pipeline in depth 
  • Learn to use different open source AutoML and feature engineering platforms 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the fundamentals of Automated Machine Learning systems 
  • Explore auto-sklearn and MLBox for AutoML tasks  
  • Automate your preprocessing methods along with feature transformation 
  • Enhance feature selection and generation using the Python stack 
  • Assemble individual components of ML into a complete AutoML framework 
  • Demystify hyperparameter tuning to optimize your ML models 
  • Dive into Machine Learning concepts such as neural networks and autoencoders 
  • Understand the information costs and trade-offs associated with AutoML 

Audience & Pre-Requisites 

This course is geared for those who wants to Automate data and model pipelines for faster machine learning applications 

Pre-Requisites:  Students should have  

  • For software developers with intermediate skills in at least one programming language and a basic understanding of IP networking and HTTP protocol.  
  • No prior knowledge of AI is necessary. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to AutoML 
  • Introduction to AutoML 
  • Scope of machine learning 
  • What is AutoML? 
  • Why use AutoML and how does it help? 
  • When do you automate ML? 
  • What will you learn? 
  • Overview of AutoML libraries 
  1. Introduction to Machine Learning Using Python 
  • Introduction to Machine Learning Using Python 
  • Technical requirements 
  • Machine learning 
  • Linear regression 
  • Important evaluation metrics – regression algorithms 
  • Logistic regression 
  • Important evaluation metrics – classification algorithms 
  • Decision trees 
  • Support Vector Machines 
  • k-Nearest Neighbors 
  • Ensemble methods 
  • Comparing the results of classifiers 
  • Cross-validation 
  • Clustering 
  1. Data Preprocessing 
  • Data Preprocessing 
  • Technical requirements 
  • Data transformation 
  • Feature selection 
  • Feature generation 
  1. Automated Algorithm Selection 
  • Automated Algorithm Selection 
  • Technical requirements 
  • Computational complexity 
  • Differences in training and scoring time 
  • Linearity versus non-linearity 
  • Necessary feature transformations 
  • Supervised ML 
  • Unsupervised AutoML 
  1. Hyperparameter Optimization 
  • Hyperparameter Optimization 
  • Technical requirements 
  • Hyperparameters 
  • Warm start 
  • Bayesian-based hyperparameter tuning 
  • An example system 
  1. Creating AutoML Pipelines 
  • Creating AutoML Pipelines 
  • Technical requirements 
  • An introduction to machine learning pipelines 
  • A simple pipeline 
  • FunctionTransformer 
  • A complex pipeline 
  1. Dive into Deep Learning 
  • Dive into Deep Learning 
  • Technical requirements 
  • Overview of neural networks 
  • A feed-forward neural network using Keras 
  • Autoencoders 
  • Convolutional Neural Networks 
  1. Critical Aspects of ML and Data Science Projects 
  • Critical Aspects of ML and Data Science Projects 
  • Machine learning as a search 
  • Trade-offs in machine learning 
  • Engagement model for a typical data science project 
  • The phases of an engagement model 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?