@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

Develop and scaling data science projects into the cloud 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.

This repository contains my code solution to DeepLearning.AIs Practical Data Science On AWS Cloud 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. 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

1. Analyze Datasets and Train ML Models using AutoML . 2. Build, Train, and Deploy ML Pipelines using BERT 3.Optimize ML Models and Deploy Human-in-the-Loop Pipelines

This repo contains my lab submissions to complete the Practical Data Science on AWS Specialization.

@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

Practical Data Science in AWS

Coursera Course with a lot of practices resources and examples to deploy

Practical Data Science on the AWS Cloud Specialization

Codes from the Practical Data Science course from Deeplearning.ai website.

Coursera files from the completion of course "Practical Data Science On AWS Cloud"

Specialization course offered by Deeplearning.ai and AWS

Coursera Kursu : Amazon SageMaker kullanarak veri bilimi projelerini buluta taşıma ve ölçeklendirme

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Practical Data Science on the AWS Cloud

Practical Data Science on the AWS Cloud Specialization

Coursera Practical Data Science on AWS Specialization Courses Notebooks.

For the deeplearning.ai, AWS & Coursera offered specialization: Practical Data Science

A repository for learning Practical Data Science and Machine Learning on AWS.

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Practical Data Science on AWS course on DeepLearning.ai

Practical Data Science Specialization

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Amazon SageMaker and Human-in-the-Loop Pipelines using AWS

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