- Natural Language Processing with BERT
- ML Pipelines and ML Operations (MLOps)
- A/B Testing and Model Deployment
- Data Labeling at Scale
- Automated Machine Learning (AutoML)
- Statistical Data Bias Detection
- Multi-class Classification with FastText and BlazingText
- Data ingestion
- Exploratory Data Analysis
- ML Pipelines and MLOps
- Model Training and Deployment with BERT
- Model Debugging and Evaluation
Practical Data Science on the AWS Cloud 專項課程
Become a cloud data science expert. Develop and scale your data science projects into the cloud using Amazon SageMaker
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您將學到的內容有
Prepare data, detect statistical data biases, perform feature engineering at scale to train models, & train, evaluate, & tune models with AutoML
Store & manage ML features using a feature store, & debug, profile, tune, & evaluate models while tracking data lineage and model artifacts
Build, deploy, monitor, & operationalize end-to-end machine learning pipelines
Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence
您將獲得的技能
關於此 專項課程
應用的學習項目
By the end of this Specialization, you will be ready to:
• Ingest, register, and explore datasets
• Detect statistical bias in a dataset
• Automatically train and select models with AutoML
• Create machine learning features from raw data
• Save and manage features in a feature store
• Train and evaluate models using built-in algorithms and custom BERT models
• Debug, profile, and compare models to improve performance
• Build and run a complete ML pipeline end-to-end
• Optimize model performance using hyperparameter tuning
• Deploy and monitor models
• Perform data labeling at scale
• Build a human-in-the-loop pipeline to improve model performance
• Reduce cost and improve performance of data products
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
專項課程的運作方式
加入課程
Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。
實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

此專項課程包含 3 門課程
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. 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.
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 using Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm with Amazon SageMaker Pipelines. Your pipeline will first transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. It will then fine-tune a text classification model to the dataset using a Hugging Face pre-trained model, which has learned to understand the human language from millions of Wikipedia documents. Finally, your pipeline will evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold.
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth.
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常見問題
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我可以只注册一门课程吗?
有助学金吗?
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此课程是 100% 在线学习吗?是否需要现场参加课程?
What is the Practical Data Science Specialization about?
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完成专项课程后我会获得大学学分吗?
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