Become a cloud data science expert. Develop and scale your data science projects into the cloud using Amazon SageMaker

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Antje Barth 另外 +3 位授課教師

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Practical Data Science on the AWS Cloud 專項課程deeplearning.ai

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

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

關於此專項課程

23,227 次近期查看

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.

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

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

高級

Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses

完成課程大約需要 3 個月

建議進度：5 小時/週

英語（English）

字幕：英語（English）

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

高級

Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses

完成課程大約需要 3 個月

建議進度：5 小時/週

英語（English）

字幕：英語（English）

專項課程的運作方式

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獲得證書

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此專項課程包含 3 門課程

課程1

課程 1

Analyze Datasets and Train ML Models using AutoML

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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.

課程2

課程 2

Build, Train, and Deploy ML Pipelines using BERT

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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.

課程3

課程 3

Optimize ML Models and Deploy Human-in-the-Loop Pipelines

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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.

授課教師

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.

Coursera and AWS have been partners since 2017 providing learners and enterprises globally, the skills they need to succeed. Coursera builds on AWS servers to scale with student demand with confidence around capacity and elasticity and in partnership with AWS. In 2019, Coursera achieved Advanced Tier Partner status and further extended the partnership with AWS Educate, AWS EdStart and AWS Academy collaborations. Coursera's been able to make cloud skills more accessible with 8 AWS courses on the Coursera platform featuring top subject matter experts and the portfolio continues to grow. To learn more about AWS, visit https://aws.amazon.com.

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What is the Practical Data Science Specialization about?
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 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. 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. In this Specialization, you will master how to remove obstacles to advance your projects from idea to deployment quickly and to scale to thousands of models or more. Learn how to prepare massive datasets originating from social media channels, mobile/web applications, or other public/private data sources that don’t fit in your local hardware and use them for training models. Understand how to tune models for the highest accuracy and then deploy and manage them over time.
What are the benefits of running data science projects in the cloud?
One of the biggest benefits of developing data science projects in the cloud is the agility to scale up and out at a minimum cost. The cloud brings together data, low-cost storage, security, and ML services along with high-performance CPUs and GPUs for model training and deployment. As a result, you can store as much data as you need and use high-performance compute elastically, so it’s much faster to realize the value of machine learning. This Specialization provides tools to analyze and clean the data, extract relevant features, and teach you how to use tools purpose-built for ML such as visual development environment, debuggers, profilers, and pipelines to scale models building, training, and deployment.
What will I learn in the Practical Data Science Specialization?
Prepare data, detect statistical data biases, and perform feature engineering at scale to train models
Automatically train, evaluate, and tune models with automated machine learning (AutoML)
Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
Build, deploy, monitor, and operationalize end-to-end machine learning pipelines.
Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence
What will I be able to do upon completing the Practical Data Science Specialization?
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
What background knowledge is necessary for the Practical Data Science Specialization?
Learners should have a working knowledge of ML algorithms and principles, be proficient in Python programming at an intermediate level, and be familiar with Jupyter notebooks and statistics. We recommend you complete the Deep Learning Specialization or an equivalent program.
Learners should also be familiar with the fundamentals of AWS and cloud computing. Completion of Coursera AWS Cloud Technical Essentials or similar is considered the prerequisite knowledge base.
Who is the Practical Data Science Specialization for?
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.
How long does it take to complete the Practical Data Science Specialization?
This Specialization consists of three courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete course 1, 3 weeks to complete course 2, and 3 weeks to complete course 3.
Who created the Practical Data Science Specialization?
This Specialization was created by Antje Barth, Sireesha Muppala, Shelbee Eigenbrode, and Chris Fregly.
Antje Barth is a Sr. Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS). She is co-author of the O'Reilly book - Data Science on AWS. Antje frequently speaks at AI / ML conferences, events, and meetups around the world. Previously, Antje worked in technical evangelism and solutions engineering at Cisco and MapR, focused on data center technologies, big data, and AI applications. Antje is also a co-founder of the Düsseldorf chapter of Women in Big Data.
Sireesha Muppala is an Enterprise Principal SA, AI/ML at Amazon Web Services (AWS) who guides customers on architecting and implementing machine learning solutions at scale. She received her Ph.D. in Computer Science from the University of Colorado, Colorado Springs, and has authored several research papers, white papers, blog articles. Sireesha frequently speaks at industry conferences, events, and meetups. She co-founded the Denver chapter of Women in Big Data.
Shelbee Eigenbrode is a Principal AI and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.
Chris Fregly is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS." Chris is also the founder of the global "Data Science on AWS" Meetup series. He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI, Open Data Science Conference (ODSC), and Nvidia GPU Technology Conference (GTC).
Is this a standalone course or a Specialization?
The Practical Data Science Specialization is made up of 3 courses.
How do I take the Specialization?
You can enroll in the Practical Data Science Specialization on Coursera. You will watch videos and complete assignments on Coursera as well.
Do I need to take the courses in a specific order?
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
How much does the Specialization cost?
A Coursera subscription costs $49 / month.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee.
Can I audit the Practical Data Science Specialization?
You can audit the courses in the Practical Data Science Specialization for free.
Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.
How do I get a receipt to get this reimbursed by my employer?
Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
You can read more about it here.
I want to purchase this Specialization for my employees. How can I do that?
Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.
Will I receive a certificate at the end of the Specialization?
You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.
If you complete all 4 courses and are subscribed to the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.
完成专项课程后我会获得大学学分吗？
No

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