Certification in Machine Learning and Data Science with AWS

Master AWS for Data Science & Machine Learning

Take your data science and machine learning skills to the next level with this comprehensive course! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, you'll gain the practical expertise to build scalable and real-world data science and machine learning solutions using Amazon Web Services (AWS).

Discover how powerful AWS services like SageMaker, Glue, Redshift, and QuickSight are revolutionizing industries through data-driven intelligence, automation, and predictive analytics.

What You'll Learn:

• Foundational Data Science Workflows: Master core data science principles and machine learning techniques leveraging AWS cloud services.
• Data Management on AWS: Gain hands-on experience with data storage (S3, DynamoDB, RDS), data warehousing (Redshift), and ETL processes (Glue) to build robust data lakes on AWS.
• Machine Learning Model Building: Learn to train, tune, and deploy ML models at scale using SageMaker, including AutoML, hyperparameter tuning, and deep learning frameworks.
• Advanced ML Techniques: Explore deep learning with AWS and SageMaker, custom training with TensorFlow and PyTorch, and reinforcement learning with AWS DeepRacer.
• Model Deployment & Scaling: Deploy and scale your machine learning models with SageMaker Endpoints, Batch Transform, and Real-Time Inference.
• Data Analytics & Visualization: Utilize QuickSight, CloudWatch, and Athena for powerful data analytics and creating insightful visualization dashboards.
• Security & Cost Optimization: Implement best practices for data security with AWS IAM and encryption, and effectively manage costs for your data science and ML projects.
• Real-World Applications: Explore practical use cases in e-commerce, finance, healthcare, manufacturing, and more.

Course Framework:

• Engaging Content: Benefit from video lectures, case studies, hands-on projects, downloadable resources, and interactive exercises.
• Practical Labs: Strengthen your skills with industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs.
• Structured Learning: The course is divided into three parts:
  • Part 1: Introduction to Data Science & AWS, Data Management, and SageMaker basics.
  • Part 2: Building & Deploying ML Models on AWS, Advanced ML, Analytics & Visualization.
  • Capstone Project: Apply your knowledge to a real-world scenario.
• Dedicated Support: Receive support for all your queries within 48 hours throughout your learning journey.

Course Content Breakdown:

Part 1: Foundations

• Introduction and Study Plan: Course overview, instructor introduction, and structured learning path.
• Module 1: Introduction to Data Science and AWS:
  • Data Science Fundamentals: Definitions, workflows, and essential tools.
  • Machine Learning Overview: Types, algorithms, and common applications.
  • AWS Cloud for ML/Data Science: Benefits and key services.
  • Key AWS Services: A comprehensive overview.
  • Hands-On: AWS account setup and console exploration.
• Module 2: Data Management on AWS:
  • AWS Data Storage: S3, DynamoDB, and RDS.
  • Amazon Redshift: Data warehousing solutions.
  • AWS Glue: Data integration and ETL processes.
  • Data Lake Architecture on AWS.
  • Hands-On: S3 bucket creation, basic ETL with Glue.
• Module 3: Introduction to AWS SageMaker:
  • SageMaker Capabilities: A detailed overview.
  • SageMaker Data Wrangler: Data preparation and labeling.
  • SageMaker Studio: Building ML models.
  • SageMaker JumpStart: Pre-built models and solutions.
  • Hands-On: Dataset exploration with SageMaker Data Wrangler.
• Module 4: Building Machine Learning Models on AWS:
  • SageMaker Model Training and Tuning.
  • Feature Engineering and Model Optimization.
  • Hyperparameter Tuning and AutoML with SageMaker.
  • SageMaker Model Registry: Managing model artifacts.
  • Hands-On: Supervised learning model training and hyperparameter tuning.
• Module 5: Deploying and Scaling ML Models on AWS:
  • SageMaker