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DeepLearning.AI

Analyze Datasets and Train ML Models using AutoML

DeepLearning.AI and Amazon Web Services via Coursera

Overview

In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code.

Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost.

The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

Syllabus

  • Week 1: Explore the Use Case and Analyze the Dataset
    • Ingest, explore, and visualize a product review data set for multi-class text classification.
  • Week 2: Data Bias and Feature Importance
    • Determine the most important features in a data set and detect statistical biases.
  • Week 3: Use Automated Machine Learning to train a Text Classifier
    • Inspect and compare models generated with automated machine learning (AutoML).
  • Week 4: Built-in algorithms
    • Train a text classifier with BlazingText and deploy the classifier as a real-time inference endpoint to serve predictions.

Taught by

Antje Barth, Shelbee Eigenbrode, Sireesha Muppala and Chris Fregly

Reviews

4.0 rating, based on 1 Class Central review

4.5 rating at Coursera based on 272 ratings

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  • AutoML helps you automating the tedious tasks so you can focus on the interesting stuff.

    In this course, you learn how to apply autoML using Amazon SageMaker Studio and autopilot.

    It covers the whole life cycle from data ingestion to deployment. At every stage you have access to intermediate data, scripts & notebooks, so you can finetune your pipeline.

    Introductory explanations, reference documents, quizzes and practical exercises bring you up to speed with this fascinating technology

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