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.


Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Software developers looking to increase machine learning applications' speed

Who should attend & recommended skills

  • This course is geared for software developers who want to automate data and model pipelines for faster machine learning applications.
  • Skill-level: Foundation-level Automated Machine Learning for Intermediate skilled team members. This is not a basic class.
  • Software developing: Intermediate (3-5 years’ experience)
  • Programming language (any): Intermediate (3-5 years’ experience)
  • IP networking and HTTP protocol: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • AI: Not required

About this course

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 will 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 will 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Automated Machine Learning expert instructor, participants will learn about and explore:
  • Building automated modules for different machine learning components
  • Understanding each component of a machine learning pipeline in depth
  • Learning to use different open-source AutoML and feature engineering platforms
  • Understanding the fundamentals of Automated Machine Learning systems
  • Exploring auto-sklearn and MLBox for AutoML tasks
  • Automating your preprocessing methods along with feature transformation
  • Enhancing feature selection and generation using the Python stack
  • Assembling individual components of ML into a complete AutoML framework
  • Demystifying hyperparameter tuning to optimize your ML models
  • Diving into Machine Learning concepts such as neural networks and autoencoders
  • Understanding the information costs and trade-offs associated with AutoML

Course breakdown / modules

  • 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

  • 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

  • Technical requirements
  • Data transformation
  • Feature selection
  • Feature generation

  • Technical requirements
  • Computational complexity
  • Differences in training and scoring time
  • Linearity versus non-linearity
  • Necessary feature transformations
  • Supervised ML
  • Unsupervised AutoML

  • Technical requirements
  • Hyperparameters
  • Warm start
  • Bayesian-based hyperparameter tuning
  • An example system

  • Technical requirements
  • An introduction to machine learning pipelines
  • A simple pipeline
  • FunctionTransformer
  • A complex pipeline

  • Technical requirements
  • Overview of neural networks
  • A feed-forward neural network using Keras
  • Autoencoders
  • Convolutional Neural Networks

  • Machine learning as a search
  • Trade-offs in machine learning
  • Engagement model for a typical data science project
  • The phases of an engagement model