Analyze Datasets and Train ML Models using AutoML (Coursera)

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.

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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.

Course 1 of 3 in the Practical Data Science Specialization.

What You Will Learn

Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.

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.

Practical Data Science Specialization

Datasets

ML Models

AutoML

Exploratory Data Analysis

EDA

Automated Machine Learning

Bias

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Course Auditing

Coursera