AWS AI & SageMaker

An introductory course to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). Students will be able to select and apply ML services to resolve business problems and will be able to label, build, train, and deploy a custom ML model.

A course focused on managing AWS costs effectively. It teaches techniques to reduce spending on AWS resources, covering cost management tools and best practices for cost optimization in the cloud.

A 3-week course to grow generative AI expertise by focusing on customizing, optimizing, and automating AI solutions using Amazon Bedrock.

Amazon Bedrock Foundations

AWS Certified Machine Learning - Specialty

This course is complete guide of AWS Sage Maker wherein student will learn how to build, deploy Sage Maker models by brining on-premises docker container and integrate it to Sage Maker. Course will also do deep drive on how to bring your own algorithms in AWS Sage Maker Environment. Course will also explain how to use pre-built optimized Sage Maker Algorithm.Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.This course offers:AWS Certified Machine Learning Specialty (MLS-C01)What is Sage Maker and why it is required Sage Maker Machine Learning lifecycle Sage Maker Architecture Sage Maker training techniques:Bring your own docker container from on premise to Sage Maker Bring your own algorithms from local machine to Sage Maker Sage Maker Pre built Algorithm Sage Maker Pipeline development Schedule the Sage Maker Training notebook More than 5 hour course are provided which helps beginners to excel in Sage Maker and will be well versed with build, train and deploy the models in Sage Maker

Description Take 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 Sage Maker, Glue, Redshift, and Quick Sight 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 Sage Maker.• 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

Updated videos with new and improved slides. Fixed all the voice issues. Hope you like the course and please give feedback!Unlock the Power of Amazon Bedrock to Build AI-Powered Applications Welcome to Mastering Amazon Bedrock, a comprehensive course designed to help you harness the power of AWS Bedrock’s tools and services. Whether you're a beginner or an experienced developer, this course will take you step-by-step through concepts, configurations, and hands-on exercises that showcase the potential of AWS Bedrock in building intelligent applications.What You’ll Learn:Knowledge Bases (KB): Dive deep into the concept of vector embeddings and retrieval-augmented generation (RAG), essential for optimizing large-scale AI applications. Learn how to configure Knowledge Bases and integrate them seamlessly with other AWS Bedrock tools using practical examples to solidify your understanding.RAG with Amazon Bedrock - We will use Anthropic Claude Model with Open Search Serverless as vector storage to perform the RAG operationsRAG with Open Source - We will also use OpenAI's ChatGPT model with in memory vector storage to perform RAG operations Retrievers - RAG pattern relies heavily on retrieval. There are many ways to retrieve data for summarization. We will learn and explore about different ways to retrieve the contents. Followed by a hands-on activity   AI Agents: Master the configuration of AWS Bedrock agents to streamline AI workflows. Gain hands-on experience in implementing action groups, handling parameters, and orchestrating requests effectively to Knowledge Bases. Understand how agents serve as the backbone of dynamic and intelligent AI interactions. We will cover 2 use cases of AI Agents. Multimodal Nutritional AI Agent - We

This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.Bonus introductions include Natural Language Processing and Deep Learning.Below Topics are covered  Chapter - Introduction to Machine Learning- Machine Learning?- Types of Machine Learning Chapter - Setup Environment - Installing Anaconda, how to use Spyder and Jupiter Notebook- Installing Libraries Chapter - Creating Environment on cloud (AWS)- Creating EC2, connecting to EC2- Installing libraries, transferring files to EC2 instance, executing python scripts Chapter - Data Preprocessing- Null Values- Correlated Feature check- Data Molding- Imputing- Scaling- Label Encoder- On-Hot Encoder Chapter - Supervised Learning: Regression- Simple Linear Regression- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent- Assumptions of Linear Regression, Dummy Variable- Multiple Linear Regression- Regression Model Performance - R-Square- Polynomial Linear Regression Chapter - Supervised Learning: Classification- Logistic Regression- K-Nearest Neighbours- Naive Bayes- Saving and Loading ML Models- Classification Model Performance - Confusion Matrix Chapter: Un Supervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring Un Supervised Clusters Performace - Silhouette Index Chapter: Un Supervised Learning: Association R