Practical Data Science Specialization

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.
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

Coursera

DeepLearning.AI,AWS

Analyze Datasets and Train ML Models using AutoML (Coursera)

Data Science

Dive into the world of Practical Data Science with our specialized course designed to equip you with essential skills in analyzing datasets, employing automated machine learning techniques, and training sophisticated ML models. This course focuses on foundational concepts such as exploratory data analysis (EDA), AutoML, and text classification algorithms, providing hands-on experience with Amazon SageMaker Clarify and Data Wrangler tools.

Mar 25th 2024

4 Weeks

EDA Exploratory Data Analysis Bias

Coursera

DeepLearning.AI,AWS

Build, Train, and Deploy ML Pipelines using BERT (Coursera)

Data Science

Dive into the world of Natural Language Processing (NLP) with our specialized course on Building, Training, and Deploying ML Pipelines using BERT. This course is designed for data scientists and enthusiasts looking to harness the capabilities of Amazon SageMaker and Hugging Face's BERT algorithm to automate complex tasks and create efficient machine learning pipelines.

Mar 25th 2024

3 Weeks

Debugging Machine Learning Artifacts

Coursera

DeepLearning.AI,AWS

Optimize ML Models and Deploy Human-in-the-Loop Pipelines (Coursera)

Data Science

Dive into the world of advanced machine learning optimization and deployment with our 'Optimize ML Models and Deploy Human-in-the-Loop Pipelines' course. This course is designed for data scientists and practitioners looking to refine their models, improve prediction performance, and streamline the deployment process. By leveraging Amazon SageMaker's powerful tools, you'll learn how to automate model tuning, conduct A/B testing of model candidates, and scale successful models in real-time.

Mar 18th 2024

3 Weeks

A/B Testing Coursera Plus Model Deployment

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