@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.

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

@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

Repository with all the material developed during the specialization in practical data science offered by AWS and Coursera.

The IT industry is evolving at lightning speed. Companies are on the lookout for professionals with practical knowledge in AWS, Data Science, and Artificial Intelligence.

A collection of comprehensive data science projects that demonstrate practical skills in data analysis, machine learning, deep learning, NLP, SQL & NoSQL databases, big data frameworks, AWS tools, and real-world business problem solving. This portfolio highlights the full workflow of data science.

A hands-on repo for learning Data Science, Machine Learning, and AWS in one place. It has practical exercises and small projects to help you understand and apply concepts in real-world scenarios.

B.Tech student specializing in Artificial Intelligence and Data Science with practical experience in Python programming, machine learning, web scraping, and AWS cloud services. Passionate about developing data-driven solutions and automating processes to enhance efficiency and insights.

The Practical Data Science Specialization teaches you how to build and deploy machine learning projects using Amazon SageMaker. It's for developers, scientists, and analysts who already know Python and SQL and want to learn how to create scalable ML pipelines in the AWS cloud.

<h1 align="center">Hi 👋, I'm Maged Mahmoud</h1> <h3 align="center">Junior Data Scientist and Machine Learning Engineer with high command and hands-on experience in implementing Machine Learning, Data Science and Deep Learning models using Scikit Learn, TensorFlow and Keras. Highly motivated to solve real life challenges using AI Solutions. Seeking a challenging opportunity where acquired technical, analytical, organizational and communication skills would be employed and further enhanced.</h3> - 🔭 I’m currently working on **Root cause of drilling bit failure based on computer vision** - 🌱 I’m currently learning **Modern computer vision techniques and practical data science on AWS** - 👨‍💻 All of my projects are available at [Kaggle and GitHub](Kaggle and GitHub) - 📝 I regularly write articles on [LinkedIn](LinkedIn) - 💬 Ask me about **Machine learning algorithms, computer vision techniques, data science tools** - 📫 How to reach me **magedmahmoud665@gmail.com** - 📄 Know about my experiences [https://www.linkedin.com/in/maged-mahmoud-413746131](https://www.linkedin.com/in/maged-mahmoud-413746131) - ⚡ Fun fact **I believe I will do it.** <h3 align="left">Connect with me:</h3> <p align="left"> <a href="https://linkedin.com/in/https://www.linkedin.com/in/maged-mahmoud-413746131" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/linked-in-alt.svg" alt="https://www.linkedin.com/in/maged-mahmoud-413746131" height="30" width="40" /></a> <a href="https://kaggle.com/https://www.kaggle.com/magedmahmoud" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/kaggle.svg" alt="https://www.kaggle.com/magedmahmoud" height="30" width="40" /></a> </p> <h3 align="left">Languages and Tools:</h3> <p align="left"> <a href="https://www.docker.com/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original-wordmark.svg" alt="docker" width="40" height="40"/> </a> <a href="https://opencv.org/" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/opencv/opencv-icon.svg" alt="opencv" width="40" height="40"/> </a> <a href="https://pandas.pydata.org/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/2ae2a900d2f041da66e950e4d48052658d850630/icons/pandas/pandas-original.svg" alt="pandas" width="40" height="40"/> </a> <a href="https://www.python.org" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg" alt="python" width="40" height="40"/> </a> <a href="https://scikit-learn.org/" target="_blank" rel="noreferrer"> <img src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg" alt="scikit_learn" width="40" height="40"/> </a> <a href="https://seaborn.pydata.org/" target="_blank" rel="noreferrer"> <img src="https://seaborn.pydata.org/_images/logo-mark-lightbg.svg" alt="seaborn" width="40" height="40"/> </a> <a href="https://www.tensorflow.org" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/tensorflow/tensorflow-icon.svg" alt="tensorflow" width="40" height="40"/> </a> </p>