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.
提供方
Practical Data Science 專項課程deeplearning.ai課程信息
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
您將學到的內容有
Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.
您將獲得的技能
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
提供方
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
亚马逊网络服务系统
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.
授課大綱 - 您將從這門課程中學到什麼
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.
審閱
來自ANALYZE DATASETS AND TRAIN ML MODELS USING AUTOML的熱門評論
Excellent introductory course for Aws sagemaker. Justifies the specialization title as it is indeed practical oriented. Labs are of good quality as well.
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?
As always, I am overwhelmed with the course structure. Simple to learn and had enough practice to get started with cloud services.
The videos and links were good. The labs were a bit too easy, mostly about copying variable names from the previous section.
關於 Practical Data Science 專項課程
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.
常見問題
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
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