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Analyze Datasets and Train ML Models using AutoML
  • Level Expert
  • المدة 14 ساعات hours
  • Offered by

عن

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.

Auto Summary

Discover the exciting world of data science and machine learning with the course "Analyze Datasets and Train ML Models using AutoML." This expert-level course, offered by Coursera, is the first in the Practical Data Science Specialization, designed to equip you with essential skills in exploratory data analysis (EDA), automated machine learning (AutoML), and text classification. Led by seasoned instructors, this course focuses on leveraging Amazon SageMaker tools for comprehensive data analysis and model training. You will delve into sophisticated techniques using Amazon SageMaker Clarify and Data Wrangler to detect statistical biases, transform datasets, and identify key features. Additionally, you will utilize Amazon SageMaker Autopilot to streamline the process of training, tuning, and deploying optimal text-classification algorithms. The course also covers training text classifiers with minimal coding using Amazon SageMaker BlazingText, an optimized implementation of the FastText algorithm. Emphasizing practical data science, this program prepares you to manage large datasets from various sources and harness the cloud's scalability and cost-efficiency for your data science projects. Spanning approximately 840 hours, this comprehensive course is ideal for data-focused developers, scientists, and analysts proficient in Python and SQL. It aims to enhance your ability to create, train, and deploy scalable ML pipelines in the AWS cloud. Whether you are looking to refine your skills or tackle new data science challenges, this course offers a robust foundation in modern, cloud-based data science practices. Available through a Starter subscription, this course promises a valuable learning experience for those ready to advance their expertise in data science and machine learning. Join us and transform your approach to data analysis and model training with cutting-edge AutoML techniques.