Practical Automated Machine Learning (AutoML) Projects

Udemy
Deal Score+1
Deal Score+1

Practical Automated Machine Learning (AutoML) Projects 4.1 (16 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

What you’ll learn

  • Learn to build model with AutoSklearn
  • Learn to build model with AutoKeras
  • Learn to build model with TPOT (tree-based pipeline optimization tool)
  • Introduction to Automated Machine Learning
  • Learn to build model with AutoKeras-Pretrained

Requirements

  • Should Know Basics of Machine Learning
  • Should Know about Machine Learning Libraries
  • A passion to learn data science

Description

Automated Machine Learning(AutoML) is currently one of the explosive subfields within Data Science. It sounds great for those who are not fluent in machine learning and terrifying for current Data Scientists. In this course, we are going to provide students with knowledge of Automated Machine Learning (Auto ml). Students will learn to use Auto Sklearn, Auto Keras, TPOT in their real world problems.

In this course we are going to work on

  • Credit card fraud detection using Auto-Sklearn
  • Mobile price prediction using Auto-Sklearn
  • Medical insurance cost prediction using TPOT
  • Red wine quality classification using TPOT
  • Image classification using Auto-Keras
  • Image classification using ANN(Auto-Keras)
  • Image classification using CNN(Auto-Keras)
  • Text classification using Auto-Keras
  • Object detection using Auto-Keras
  • Topic classification using Auto-Keras

Who this course is for:

  • Intermediate in machine learning
  • Interest in AutoML

There is currently no uniformly optimal AutoML solution, and the Automated Machine Learning (AutoML) frameworks presently available are still far from being able to solve many of the real-world data science issues, where the projects are multifaceted and require complicated and subjective tasks that don’t allow for simple automation.

machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples,

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