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.
Ce cours fait partie de la Spécialisation Practical Data Science on the AWS Cloud
Offert par
Spécialisation Practical Data Science on the AWS Clouddeeplearning.aiÀ propos de ce cours
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Ce que vous allez apprendre
Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.
Compétences que vous acquerrez
- Statistical Data Bias Detection
- Multi-class Classification with FastText and BlazingText
- Data ingestion
- Exploratory Data Analysis
- Automated Machine Learning (AutoML)
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Offert par
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 90 fully featured services for compute, storage, networking, database, analytics, application services, deployment, management, developer, mobile, Internet of Things (IoT), Artificial Intelligence, security, hybrid and enterprise applications, from 44 Availability Zones across 16 geographic regions. AWS services are trusted by millions of active customers around the world — including the fastest-growing startups, largest enterprises, and leading government agencies — to power their infrastructure, make them more agile, and lower costs.
Programme de cours : ce que vous apprendrez dans ce cours
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.
Avis
- 5 stars69,28 %
- 4 stars22,18 %
- 3 stars5,11 %
- 2 stars2,38 %
- 1 star1,02 %
Meilleurs avis pour ANALYZE DATASETS AND TRAIN ML MODELS USING AUTOML
Great course! The only thing that's not specified is the cost of the tools we learn how to use. Is SageMaker free, or is there a cost?
Excellent, Thank you Coursera for giving me this wornderfull opportunity.
Gives a very good and quick introduction to the different features available in AWS.
As always, I am overwhelmed with the course structure. Simple to learn and had enough practice to get started with cloud services.
À propos du Spécialisation Practical Data Science on the AWS Cloud
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.
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