Practical Data Science

Antje Barth, Shelbee Eigenbrode, Sireesha Muppala, and Chris Fregly

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale...

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It 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 who 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. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.

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What's inside

Three courses

Analyze Datasets and Train ML Models using AutoML

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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Optimize ML Models and Deploy Human-in-the-Loop Pipelines

In this course, you will learn techniques to improve model accuracy, compare prediction performance, and generate new training data with human intelligence. You will also deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data.

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Build, Train, and Deploy ML Pipelines using BERT

In the second course of the Practical Data Science Specialization, you will learn to automate a natural language processing task by building an end-to-end machine learning pipeline. Your pipeline will transform the dataset into BERT-readable features, fine-tune a text classification model, and evaluate its accuracy. Finally, your pipeline will only deploy the model if it meets a given threshold.

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What's inside

Three courses

Analyze Datasets and Train ML Models using AutoML

Optimize ML Models and Deploy Human-in-the-Loop Pipelines

Build, Train, and Deploy ML Pipelines using BERT

Save this collection