Solutions on Practical Data Science Specialization on Coursera (offered by deeplearning.ai)

@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.

Coursera Course

This repo contains materials from the Deeplearning.AI's Practical Data Science Specialization offered by Coursera.

This repository will contain all the material I used in this 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

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

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

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

Specialization in Data Science with MATLAB hosted on coursera

This course, which is the second course of the data science specialization from Johns Hopkins University on Coursera, covers practical issues in statistical computing which include programming in R, writing R functions and debugging. Each week, programming assignments were given.

@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.

This repository contains all materials, including Labs in the Practical Data Science Specialization offered by DeepLearning.AI and Amazon Web Services on Coursera

Final project for the Practical Machine Learning course on Coursera. This course is part of the Data Science Specialization track.

Coursera Practical Data Science Specialization

Practical Data Science on the AWS Cloud Specialization

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

Practical Machine Learning, eigth course of the data science specialization from Johns Hopkins University on Coursera

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Practical Data Science Specialization Coursera

Completing this specialization will give you the skills and confidence you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare, to the auto industry, to tech startups. This specialization assumes you have domain expertise in a technical field and some exposure to computational tools, such as spreadsheets. To be successful in completing the courses, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation). Throughout this specialization, you will be using MATLAB. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your data science tasks. You will be provided with free access to MATLAB for the duration of the specialization to complete your work.

Practical Data Science on the AWS Cloud Specialization

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Coursera Practical Data Science on AWS Specialization Courses Notebooks.

Assignment Practical Machine Learning course of the Data Science specialization

Part of "Practical Machine Learning" course from Data Science Specialization

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

✅ Course Project for Practical Machine Learning course, Data Science specialization, Coursera