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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/practical-data-science
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课程名称：实用数据科学 课程概述： 在本课程中，您将学习如何准备数据、检测统计数据偏差、在规模上执行特征工程以训练模型，并通过AutoML进行模型训练、评估和调优。您将学习如何存储和管理机器学习特征，调试、分析、调优和评估模型，同时跟踪数据沿袭和模型文档。您还将掌握构建、部署、监控和运营化端到端机器学习管道的技能，以及通过人机协作管道提升模型性能。 您将获得的技能： - 使用BERT进行自然语言处理 - 机器学习管道和机器学习运维（MLOps） - A/B测试与模型部署 - 大规模数据标注 - 自动化机器学习（AutoML） - 统计数据偏差检测 - 使用FastText和BlazingText的多类分类 - 数据摄取 - 探索性数据分析 - 模型训练与BERT模型部署 - 模型调试与评估 课程介绍： 本专业化课程结合了多个学科，采用专用的机器学习工具在AWS云上，使您掌握有效部署数据科学项目的实用技能。课程适合熟悉Python和SQL编程语言的数据开发者、科学家和分析师，学习如何在AWS云中构建、训练和部署可扩展的端到端机器学习管道，包括自动化和人机协作的方式。 为期10周的课程，每周将有专门开发的实验室，让您有机会实践最新的自然语言处理（NLP）和自然语言理解（NLU）算法，如BERT和FastText，利用Amazon SageMaker进行实践学习。 应用学习项目： 通过本专业化课程的学习，您将能够： - 摄取、注册和探索数据集 - 检测数据集中的统计偏差 - 使用AutoML自动训练和选择模型 - 从原始数据创建机器学习特征 - 在特征存储中保存和管理特征 - 使用内置算法和自定义BERT模型训练和评估模型 - 调试、分析和比较模型以提升性能 - 构建并运行完整的端到端机器学习管道 - 通过超参数调优优化模型性能 - 部署和监控模型 - 大规模进行数据标注 - 构建人机协作管道以提升模型性能 - 降低成本并提升数据产品性能 证书与学习方式： 课程为100%在线，灵活安排学习时间，完成后可获得证书。适合具有机器学习和Python工作知识，并熟悉Jupyter Notebook和统计学的人士。课程预计需要3个月的时间，每周建议学习5小时。 更多信息及课程链接：[实用数据科学课程](https://www.coursera.org/learn/automl-datasets-ml-models)。
Course Link: https://www.coursera.org/learn/automl-datasets-ml-models
Name:Analyze Datasets and Train ML Models using AutoML
Description:Offered by Amazon Web Services and DeepLearning.AI. In the first course of the Practical Data Science Specialization, you will learn ... Enroll for free.
Course Link: https://www.coursera.org/learn/ml-pipelines-bert
Name: Build, Train, and Deploy ML Pipelines using BERT
Description:Offered by Amazon Web Services and DeepLearning.AI. In the second course of the Practical Data Science Specialization, you will learn to ... Enroll for free.
Course Link: https://www.coursera.org/learn/ml-models-human-in-the-loop-pipelines
Name:Optimize ML Models and Deploy Human-in-the-Loop Pipelines
Description:Offered by Amazon Web Services and DeepLearning.AI. In the third course of the Practical Data Science Specialization, you will learn a ... Enroll for free.
What you will learn
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
Skills you will gain
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
About this Specialization
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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.
Applied Learning Project
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
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Hours to complete
Approximately 3 months to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Hours to complete
Approximately 3 months to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
