Overview

DescriptionTake the next step in your cloud-powered AI and machine learning journey! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, this course will equip you with the skills to harness AWS for scalable, real-world data science and machine learning solutions. Learn how services like SageMaker, Glue, Redshift, and QuickSight are transforming industries through data-driven intelligence, automation, and predictive analytics.Guided by hands-on projects and real-world use cases, you will:• Master foundational data science workflows and machine learning principles using AWS cloud services.• Gain hands-on experience managing data with S3, Redshift, Glue, and building models with AWS SageMaker.• Learn to train, optimize, and deploy ML models at scale using advanced tools like AutoML, hyperparameter tuning, and deep learning frameworks.• Explore industry applications in e-commerce, finance, healthcare, and manufacturing using AWS AI/ML solutions.• Understand best practices for cost management, security, and automation in cloud-based data science projects.• Position yourself for a competitive advantage by building in-demand skills at the intersection of cloud computing, AI, and machine learning.The Frameworks of the Course· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises- designed to help you deeply understand how to leverage AWS for data science and machine learning applications.· The course includes industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using AWS services.· In the first part of the course, you'll learn the basics of data science, machine learning, and how AWS enables scalable cloud-based solutions.· In the middle part of the course, you will gain hands-on experience using AWS tools like SageMaker, Glue, Redshift, and QuickSight to train, tune, and visualize ML workflows across different stages of a data science project.· In the final part of the course, you will explore deployment strategies, automation pipelines, cost and security best practices, and real-world applications across industries. All your queries will be addressed within 48 hours with full support throughout your learning journey.Course Content:Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Data Science and AWS1.1. Basics of Data Science: Definitions, Workflows, and Tools1.2. Overview of Machine Learning: Types, Algorithms, and Use Cases1.3. Introduction to AWS Cloud and Its Benefits for ML and Data Science1.4. Overview of Key AWS Services for Data Science and ML1.5. Hands-On Activity: Set up an AWS account and explore the AWS Management Console.1.6. Conclusion of Introduction to Data Science and AWSModule 2. Data Management on AWS2.1. Data Storage Solutions on AWS: S3, DynamoDB, and RDS2.2. Data Warehousing with Amazon Redshift2.3. Data Integration and ETL Processes with AWS Glue2.4. Data Lake Architecture on AWS2.5. Hands-On Activity: Create an S3 bucket and manage datasets.Perform basic ETL using AWS Glue.2.6. Conclusion of Data Management on AWSModule 3. Introduction to AWS SageMaker3.1. Overview of SageMaker Capabilities3.2. Data Preparation and Labeling with SageMaker Data Wrangler3.3. Building ML Models with SageMaker Studio3.4. Pre-built Models and SageMaker JumpStart3.5. Hands-On Activity: Load a dataset into SageMaker and explore it using Data Wrangler.3.6. Conclusion of Introduction to AWS SageMaker.Module 4. Building Machine Learning Models on AWS4.1. Model Training and Tuning with SageMaker4.2. Feature Engineering and Model Optimization4.3. Hyper-parameter Tuning and AutoML with SageMaker4.4. Managing Model Artifacts with SageMaker Model Registry4.5 Hands-On Activity: Train a supervised learning model using SageMaker.Perform hyperparameter tuning on the model.4.6. Conclusion of Building Machine Learning Models on AWSModule 5. Deploying and Scaling ML Models on AWS5.1. Model Deployment with SageMaker Endpoints5.2. Batch Transform for Large-Scale Inference5.3. Real-Time Inference and Monitoring Deployed Models5.4. Scaling Models with Elastic Inference and Multi-Model Endpoints5.5. Hands-On Activity: Deploy an ML model on SageMaker and test it with sample inputs.5.6. Conclusion of Deploying and Scaling ML Models on AWSModule 6. Advanced Machine Learning on AWS6.1. Deep Learning with AWS and SageMaker6.2. Custom Training with TensorFlow and PyTorch in SageMaker6.3. Reinforcement Learning with AWS DeepRacer6.4. ML Pipelines for Automation and Workflow Management6.5. Hands-On Activity: Build a simple deep learning model using SageMaker.Explore reinforcement learning using AWS DeepRacer.6.6. Conclusion of Advanced Machine Learning on AWSModule 7. Analytics and Visualization on AWS7.1. Data Analytics with AWS QuickSight7.2. Log and Metric Analysis with CloudWatch and Athena7.3. Integrating ML Models with Visualization Dashboards7.4. Advanced Analytics Workflows with AWS Data Pipeline7.5. Hands-On Activity: Create a visualization dashboard with AWS QuickSight.7.6. Conclusion of Analytics and Visualization on AWSModule 8. Security, Cost Management, and Best Practices8.1. Ensuring Data Security with AWS IAM and Encryption8.2. Managing Costs for Data Science and ML Projects on AWS8.3. Best Practices for ML and Data Science Workflows on AWS8.4. Common Pitfalls and How to Avoid Them8.5.Hands-On Activity: Set up IAM roles and policies for secure ML workflows.Monitor and optimize AWS costs using AWS Billing Dashboard.8.6. Conclusion of Security, Cost Management, and Best PracticesModule 9. Real-World Use Cases and Applications9.1. E-commerce: Customer Segmentation and Recommendation Systems9.2. Finance: Fraud Detection and Risk Analysis9.3. Healthcare: Predictive Analytics and Diagnostics9.4. Manufacturing: Predictive Maintenance and Quality Control9.5. Media and Entertainment9.6. Education and Training9.7. Hands-On Activity: Work on a domain-specific case study using AWS services9.8. Conclusion of Real-World Use Cases and Applications.Part 2Capstone Project.

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