Learn AWS machine learning services including SageMaker, Rekognition, Comprehend, and Bedrock. Build, train, and deploy ML models on Amazon Web Services.
Learn AWS Machine Learning and AI Complete Course
Learn Amazon Bedrock Foundations
Learn Algorithmic Trading with Machine Learning
Comprehensive course on Reinforcement Learning on AWS taught by AWS. Part of AWS ML curriculum.
Learn the fundamentals of robotics and how to leverage AWS services for developing and deploying robotics applications.
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.
Learn AWS Certified Machine Learning - Specialty
This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker 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 SageMaker and why it is requiredSageMaker Machine Learning lifecycleSageMaker ArchitectureSageMaker training techniques:Bring your own docker container from on premise to SageMakerBring your own algorithms from local machine to SageMakerSageMaker Pre built AlgorithmSageMaker Pipeline developmentSchedule the SageMaker Training notebookMore than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in 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 3-week course to grow generative AI expertise by focusing on customizing, optimizing, and automating AI solutions using Amazon Bedrock.
This course covers the GenAI revolution and its role in securing a career with AWS Certified AI Practitioner AIF-C01, focusing on AI services, machine learning, and cloud AI.
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
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 ApplicationsWelcome 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 OpenSearch 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 operationsRetrievers - 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 LearningChapter - Setup Environment - Installing Anaconda, how to use Spyder and Jupiter Notebook- Installing LibrariesChapter - Creating Environment on cloud (AWS)- Creating EC2, connecting to EC2- Installing libraries, transferring files to EC2 instance, executing python scriptsChapter - Data Preprocessing- Null Values- Correlated Feature check- Data Molding- Imputing- Scaling- Label Encoder- On-Hot EncoderChapter - 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 RegressionChapter - Supervised Learning: Classification- Logistic Regression- K-Nearest Neighbours- Naive Bayes- Saving and Loading ML Models- Classification Model Performance - Confusion MatrixChapter: UnSupervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring UnSupervised Clusters Performace - Silhouette IndexChapter: UnSupervised Learning: Association R
Do you want to become an AWS Machine Learning Engineer Using SageMaker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an
Welcome to this course on Machine Learning and Data Science with AWS. Amazon Web services or AWS is one of the biggest cloud computing platform where everything gets deployed to scale and action. Understanding the concepts and methods are vital, but being able to develop and deploy those concepts in forms of real life applications is something that is most weighted by the industry. Thus, here in this course, we are focused on ways you can use various cloud services on AWS to actually build and deploy you ideas into actions on multiple domains on Machine Learning and Data Science. You could be an IT professional looking for job change or upgrading your skillset or you could be a passionate learner or cloud certification aspirant, this course is for wider audience that if formed by the people who would like to learn any of these or a combination of these things-Create and Analyze dataset to find insights and spot outliers or trendsBuild Data visualization reports and dashboards by combining various visualization charts to represent data insightsDevelop machine learning models for Natural Language Processing for various applications on AWSAnd much more.Course StructureThis course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.After that in Machine Learning section, you will learn
Gain insights into AWS Bedrock and LLMs to position yourself at the forefront of AI innovation. You will build and deploy three AI projects using foundation models from a single API.
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