Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Certification in Machine Learning and Data Science with AWS

Name: Certification in Machine Learning and Data Science with AWS
Rating: 4.0 (35 reviews)

Learn Data Management on AWS, ML models on AWS, Advanced ML on AWS, Analytics and Visualization on AWS and Use Cases

Created byHuman and Emotion: CHRMI

Last updated 7/2025

English

English [Auto],

What you'll learn

You will learn about the Introduction to Data Science and AWS, including the fundamentals of data science workflows, tools, and techniques
You will also explore the benefits of using AWS Cloud for ML and data science and become familiar with key AWS services that support these functions.
As part of the hands-on activity, you will set up your AWS account and explore the AWS Management Console.
You will explore Data Management on AWS, including how to store, organize, and manage data using services like Amazon S3, DynamoDB, and RDS
You will understand how data warehousing works with Amazon Redshift and how to implement ETL pipelines using AWS Glue.
You will also study the architecture and use cases of data lakes on AWS. Hands-on tasks include creating and managing an S3 bucket and performing ETL operations
You will get introduced to AWS SageMaker, Amazon's integrated ML platform, and explore its full capabilities.
Learn preparing and labeling data with SageMaker Data Wrangler, using SageMaker Studio to build and analyze ML models, and leveraging pre-trained models
You will learn how to Build Machine Learning Models on AWS using SageMaker, focusing on model training, tuning, and optimization
You will also learn to manage model versions using SageMaker Model Registry. In the hands-on exercise, you will train a supervised model
You will understand how to Deploy and Scale ML Models on AWS, including strategies for deploying models using SageMaker Endpoints
Learn handling large-scale inference with Batch Transform, and enabling real-time predictions. You will explore scaling techniques such as Elastic Inference
You will explore Advanced Machine Learning on AWS, including building deep learning models using TensorFlow and PyTorch in SageMaker
You will also study how to automate workflows using ML pipelines. As part of your hands-on work, you will build a simple deep learning model
You will learn how to perform Analytics and Visualization on AWS using services like QuickSight for dashboards, CloudWatch for monitoring
You will also learn to integrate ML models with visualization tools and set up advanced analytics workflows using AWS Data Pipeline
You will study Security, Cost Management, and Best Practices, learning how to secure data science workflows using IAM roles, encryption
You will also review best practices and common pitfalls in AWS-based ML workflows. Hands-on tasks include setting up IAM policies and monitoring project cost
You will analyze Real-World Use Cases and Applications of AWS Data Science across domains such as e-commerce, finance, healthcare, manufacturing etc
By the end of this course, you will apply all your skills to a capstone project, where you will solve a real-world problem or build a complete machine learning

Course content

10 sections • 70 lectures • 7h 39m total length

Introduction10:38
Explore machine learning and data science on AWS by building and deploying scalable models with SageMaker, Glue, S3, and Redshift, while mastering data pipelines and analytics.
Module 1. Introduction to Data Science and AWS3:14
Explore the introduction to data science and AWS, clarifying how data science blends statistics, mathematics, computer science, and domain expertise to extract insights from data and enable data-driven decision making.
1.1. Basics of Data Science: Definitions, Workflows, and Tools9:42
Explore the basics of data science as an interdisciplinary blend of statistics and computer science, covering data collection, cleaning, exploratory data analysis, feature engineering, modeling, and deployment tools.
1.2. Overview of Machine Learning: Types, Algorithms, and Use Cases7:48
Outline three machine learning types—supervised, unsupervised, and reinforcement—and key algorithms like linear regression, SVM, decision trees, K-means, PCA, and Q-learning, with use cases in healthcare, finance, e-commerce, and transportation.
1.3. Introduction to AWS Cloud and Its Benefits for ML and Data Science10:21
Explore how AWS cloud enables scalable, secure data science and ML workflows with SageMaker, S3, EC2, Redshift, and Kinesis for end-to-end analytics and AI applications.
1.4. Overview of Key AWS Services for Data Science and ML7:12
Discover key AWS services for data science and ML, from secure storage with S3 and Redshift to processing with Glue and Kinesis, and SageMaker for end-to-end models.
1.5. Hands-On Activity: Set up an AWS account and explore AWS Management console3:20
Guide beginners through creating an AWS account, signing into the AWS Management Console, and using the free tier with strong passwords and MFA.
Hands-On Activity: Set up an AWS account and explore AWS Management console 23:39
Set up an AWS account and explore the AWS Management Console, using the search bar to find EC2, S3, RDS, DynamoDB, and SageMaker, and practice creating a sample S3 bucket.
1.6. Conclusion of Introduction to Data Science and AWS2:32
The conclusion highlights how integrating data science with AWS creates a powerful, scalable framework for end-to-end data workflows, from ingestion to deployment, using SageMaker, S3, Redshift, Lambda, and QuickSight.

Module 2. Data Management on AWS4:02
Learn to manage data on AWS end-to-end, from ingestion and storage to insights, within a seven-layer architecture that ensures accuracy, security, and governance across a scalable data lifecycle.
2.1. Data Storage Solutions on AWS: S3, DynamoDB, and RDS7:06
Explore aws data storage: s3 for unstructured data with lifecycle policies, versioning, encryption; rds for managed relational databases with backups and replication; and dynamodb for high-throughput NoSQL workloads.
2.2. Data Warehousing with Amazon Redshift10:49
Master AWS data warehousing with Redshift, Spectrum, Athena, and QuickSight to turn raw data into insights via scalable pipelines from S3 to analytics and visualization using Glue and Kinesis.
2.3. Data Integration and ETL Processes with AWS Glue4:07
Master data integration and etl with aws glue alongside emr, kinesis, and data sync to build end-to-end pipelines from s3 to analytics with athena and quicksight.
2.4. Data Lake Architecture on AWS6:31
Describe a secure, governed data lake architecture on AWS, using IAM, KMS, CloudTrail, and Lake Formation, with ETL via Glue and Lambda, and analysis in Redshift and Athena.
2.5. Hands-On Activity: Create an S3 bucket and manage datasets and ETL6:17
Create an S3 bucket, upload datasets into raw and processed folders, then use AWS Glue to crawl and transform data for an ETL pipeline and load into processed.
2.6. Conclusion of Data Management on AWS2:44
Leverage the AWS data management platform to collect, store, process, secure, and analyze data across its lifecycle for informed, data-driven decisions, cost optimization, and compliance.

Module 3. Introduction to AWS SageMaker2:48
Discover SageMaker, a fully managed machine learning service that simplifies building, training, and deploying models at scale, with data preparation, tuning, endpoints, and monitoring.
3.1. Overview of SageMaker Capabilities6:58
Explore the AWS SageMaker capabilities, from end-to-end machine learning workflow to deployment and monitoring, using the IDE with built-in Jupyter notebooks, Data Wrangler, and Autopilot.
3.2. Data Preparation and Labeling with SageMaker Data Wrangler7:15
Learn data preparation and labeling with SageMaker Data Wrangler, notebook instances, feature store, distributed training, and pre-built and custom algorithms, plus real-time endpoints, batch transform, edge deployment, and pipelines.
3.3. Building ML Models with SageMaker Studio3:49
Learn to build ML models with AWS SageMaker Studio by setting up SageMaker, creating notebook, preparing data in S3 with Data Wrangler, training, tuning, and deploying endpoints for real-time predictions.
3.4. Pre-built Models and SageMaker JumpStart10:18
Learn to use AWS SageMaker JumpStart to prepare data in S3, train with prebuilt algorithms or AutoML, deploy to real-time endpoints or batch, and monitor model performance.
3.5. Hands-On Activity: Load a dataset into SageMaker and explore it6:08
Load a data set into Amazon SageMaker and explore it with Data Wrangler, visually cleaning, transforming, and exporting the flow for pipelines or notebooks.
3.6. Conclusion of Introduction to AWS SageMaker.2:23
AWS SageMaker revolutionizes machine learning by providing an integrated, scalable platform that simplifies data preparation, training, deployment, and monitoring with SageMaker Studio, Data Wrangler, Autopilot, and Jumpstart.

Module 4. Building Machine Learning Models on AWS3:51
Build end-to-end machine learning models on AWS, from data preparation with AWS Glue to training with SageMaker Experiments, then deploy and monitor via hosted endpoints.
4.1. Model Training and Tuning with SageMaker3:45
Discover SageMaker, a fully managed platform covering the end-to-end machine learning lifecycle from data preparation to training, tuning, and deployment, with built-in algorithms and TensorFlow, PyTorch, and scikit-learn support.
4.2. Feature Engineering and Model Optimization6:44
Learn feature engineering and model optimization in AWS, using AWS Glue, S3, and Redshift ML to streamline data prep, storage, and predictive modeling.
4.3. Hyper-parameter Tuning and AutoML with SageMaker8:50
Explore hyperparameter tuning and AutoML with SageMaker through an AWS workflow: define problems, collect and prepare data, select models, train, evaluate with metrics, and deploy with monitoring and scaling.
4.4. Managing Model Artifacts with SageMaker Model Registry9:56
Manage model artifacts with SageMaker model registry for governance and artifact tracking, then deploy ML models on AWS from data in S3 and Glue to training, evaluation, and deployment.
4.5 Hands-On Activity: Train a supervised learning model using SageMaker7:28
Train a supervised model with SageMaker using built-in algorithms like XGBoost or custom scripts, prepare data from S3 or Data Wrangler, and tune hyperparameters to improve accuracy before deployment.
4.6. Conclusion of Building Machine Learning Models on AWS1:57
Explore how AWS streamlines the machine learning lifecycle from data preparation to deployment and monitoring with SageMaker, Glue, and Lambda. Build scalable, cost-effective ML solutions using these tools.

Module 5. Deploying and Scaling ML Models on AWS4:04
Deploying and scaling machine learning models on AWS using SageMaker endpoints, auto scaling, real-time predictions, model monitor, and automated retraining pipelines.
5.1. Model Deployment with SageMaker Endpoints7:38
Deploy machine learning models with SageMaker endpoints and Batch Transform for real-time and offline inference. Explore Amazon web services such as lambda, api gateway, cloudwatch, and auto scaling.
5.2. Batch Transform for Large-Scale Inference3:47
Compare SageMaker batch transform for large scale inference with AWS Lambda serverless deployment, uploading input data to S3, and producing outputs in S3 for non real-time, periodic tasks.
5.3. Real-Time Inference and Monitoring Deployed Models6:21
Deploy real-time inferences with SageMaker endpoints and expose predictions via REST APIs and API gateway. Monitor performance, drift, and retrain with pipelines to keep models accurate.
5.4. Scaling Models with Elastic Inference and Multi-Model Endpoints13:35
Explore how to scale machine learning models on AWS using elastic inference and multi-model endpoints, with auto scaling in SageMaker, ELB, Fargate, ECS/EKS, and optimization techniques.
5.5. Hands-On Activity: Deploy an ML model on SageMaker and test it with sample6:55
Deploy a trained machine learning model on SageMaker using a model artifact stored in S3 and expose real-time endpoint for inference; test it with JSON inputs via Python Boto3 SDK.
5.6. Conclusion of Deploying and Scaling ML Models on AWS2:31
Deploy and scale ML models on AWS with SageMaker, Lambda, and ECS EKS to achieve high performance and cost efficiency in production, with autoscaling, monitoring, and pipelines.

Module 6. Advanced Machine Learning on AWS4:28
Explore advanced machine learning on AWS with SageMaker for deep learning, transfer learning, and end-to-end pipelines using TensorFlow, PyTorch, and MXNet.
6.1. Deep Learning with AWS and SageMaker7:45
Explore AWS SageMaker for end-to-end machine learning, from data preparation to training, deployment, and monitoring. Use SageMaker Studio, debugger, autopilot, and preconfigured environments for scalable, cost-efficient ML workflows.
6.2. Custom Training with TensorFlow and PyTorch in SageMaker8:07
Explore training with TensorFlow and PyTorch in SageMaker, with distributed training, hyperparameter optimization, and explainability. Learn transfer learning with Jumpstart and pipelines for computer vision, NLP, and speech recognition.
6.3. Reinforcement Learning with AWS DeepRacer4:12
Explore reinforcement learning with AWS DeepRacer, using SageMaker RL with OpenAI Gym environments to train autonomous vehicles and optimize real-time and batch inferences, via a ROS-based pipeline with OpenVINO.
6.4. ML Pipelines for Automation and Workflow Management14:14
Explore ml pipelines for automation on aws, from data cleaning with glue and storage in dynamodb or redshift to sage maker training, hyperparameter tuning, deployment, and continuous monitoring.
6.5. Hands-On Activity: Build a simple deep learning model using SageMaker.6:53
Build and train a simple deep learning model on AWS SageMaker using the mNIST data, then deploy an endpoint. Explore reinforcement learning by training a virtual car with AWS DeepRacer.
6.6. Conclusion of Advanced Machine Learning on AWS2:19
Leverage the AWS platform for advanced machine learning across the full lifecycle, including distributed training, hyperparameter tuning, and real-time inference. Enable SageMaker for managed model development, explainability, and scalable deployment.

Module 7. Analytics and Visualization on AWS3:09
Explore analytics and visualization on AWS across the data pipeline. Learn how Kinesis, Glue, Athena, and QuickSight enable serverless, real-time analytics and interactive dashboards for data-driven decisions.
7.1. Data Analytics with AWS QuickSight4:17
Explore data analytics with AWS QuickSight and Redshift, building dashboards and visualizations with templates or custom designs, and leverage Spice, anomaly detection, forecasting, embedding, and Redshift ML.
7.2. Log and Metric Analysis with CloudWatch and Athena7:59
Explore serverless log and metric analysis using CloudWatch and Athena on S3, integrating AWS Glue, Databrew, and QuickSight for end-to-end etl, data discovery, and visualization.
7.3. Integrating ML Models with Visualization Dashboards3:55
Integrate ML models with AWS visualization dashboards through Amazon QuickSight, enabling ML insights, automated anomaly detection, forecasting, real-time visuals with Kinesis, and geospatial and custom visualizations.
7.4. Advanced Analytics Workflows with AWS Data Pipeline18:11
Explore advanced analytics workflows with AWS Data Pipeline, automating end-to-end data movement, transformation, and processing across S3, DynamoDB, Redshift, and OpenSearch. Visualize insights with Kibana and QuickSight.
7.5. Hands-On Activity: Create a visualization dashboard with AWS QuickSight.6:24
Prepare and upload datasets to AWS QuickSight from CSV or JSON, connect to S3, Redshift, or RDS, clean data, create calculated fields, and build interactive visualization dashboards.
7.6. Conclusion of Analytics and Visualization on AWS2:33
Leverage AWS analytics and visualization to transform raw data into actionable insights. Build scalable analytics pipelines with QuickSight, Redshift, Athena, and Kinesis for real-time, serverless data access.

Module 8. Security, Cost Management, and Best Practices4:35
Secure your AWS environment, optimize costs, and boost efficiency with IAM, CloudTrail, config, AWS Trusted Advisor, rightsizing, reserved and spot instances, budgets, and automation with Lambda, CloudFormation, and Auto Scaling.
8.1. Ensuring Data Security with AWS IAM and Encryption15:07
Discover how AWS secures data through IAM for fine-grained access, KMS encryption, and threat detection with GuardDuty, Macie, and CloudTrail, plus WAF and VPC isolation for resilient protection.
8.2. Managing Costs for Data Science and ML Projects on AWS10:02
Manage data science and ML costs on AWS using Cost Explorer, budgets, and Trusted Advisor, while right sizing, spot and reserved instances, and tagging for optimized spending.
8.2. Managing Costs for Data Science and ML Projects on AWS 26:04
Learn AWS cost optimization for data science and ML, including rightsizing with trusted advisor, spot instances, S3 lifecycle policies, and consolidated billing via AWS organizations.
8.3. Best Practices for ML and Data Science Workflows on AWS6:47
Build scalable ML and data science workflows on AWS by using auto scaling, elastic load balancing, serverless options, and SageMaker pipelines for end-to-end model development.
8.4. Common Pitfalls and How to Avoid Them4:22
Identify common security and cost management pitfalls in AWS, then apply best practices like least privilege, encryption, auto stop, rightsizing, spot instances, and budgets to avoid them.
8.5. Hands-On Activity: Set up IAM roles and policies for secure ML workflows.7:03
Configure IAM roles and policies for secure ML workflows with least privilege and IAM Access Analyzer, then monitor and optimize AWS costs using the AWS Billing Dashboard and Cost Explorer.
8.6. Conclusion of Security, Cost Management, and Best Practices2:14
Apply AWS security best practices and cost management to build a secure, scalable cloud infrastructure using IAM, CloudTrail, Cost Explorer, tagging, and automation with Lambda, CloudFormation, and autoscaling.

Module 9. Real-World Use Cases and Applications3:30
Discover real-world AWS use cases across healthcare, finance, media, and e-commerce, showcasing secure storage, real-time processing, scalable analytics, and content delivery with services like S3, EC2, SageMaker, Redshift, and Lambda.
9.1. E-commerce: Customer Segmentation and Recommendation Systems7:03
Discover aws-driven e-commerce: customer segmentation and recommendations, secure payments, real-time inventory, and data analytics, enabled by scalable hosting and omnichannel retail using ec2, rds, dynamodb, redshift, and SageMaker.
9.2. Finance: Fraud Detection and Risk Analysis7:13
Explore AWS financial services use cases for real time fraud detection and risk analysis, leveraging Amazon Fraud Detector, SageMaker, Kinesis, Redshift, Macie, and Config, with end-to-end pipelines and regulatory compliance.
9.3. Healthcare: Predictive Analytics and Diagnostics3:32
Explore healthcare predictive analytics and diagnostics across electronic health records, genomics, telemedicine, and drug discovery using AWS services like RDS, HealthLake, Batch, SageMaker, AppSync, Chime, and ParallelCluster.
9.4. Manufacturing: Predictive Maintenance and Quality Control8:21
Explore how manufacturing and supply chains use industrial IoT, predictive maintenance with SageMaker, and digital twins on AWS to enable real-time decision making and quality control.
9.4. Manufacturing: Predictive Maintenance and Quality Control 22:19
Explore manufacturing, predictive maintenance, and quality control with AWS IoT Core, Amazon Forecast, and AWS IoT SiteWise for real-time monitoring, energy forecasting, and operational efficiency in smart grids.
9.5. Media and Entertainment5:52
Explore AWS media solutions for content delivery, live streaming, AI-driven recommendations with SageMaker, digital asset management with S3 and Glacier, and gaming analytics with GameLift, Kinesis, and Redshift.
9.6. Education and Training2:39
Build scalable online learning platforms with AWS EC2 and RDS, stream lectures via AWS Elemental MediaLive, and use Rekognition, Lambda, and Chime SDK for AI assessments and virtual classrooms.
9.7. Hands-On Activity: Work on a domain-specific case study using AWS services5:46
Define a domain-specific problem and map AWS services—SageMaker, S3, IoT core, Lambda—to build an end-to-end ML solution, then ingest, prepare, train, deploy, and visualize results.
9.8. Conclusion of Real-World Use Cases and Applications.2:20
Aws offers a flexible, secure, and scalable cloud platform that powers applications across retail, media, gaming, education, healthcare, and manufacturing, enabling end-to-end machine learning pipelines and internet of things integrations.

Requirements

You should have an interest in data science, machine learning, and understanding how cloud platforms like AWS support modern data workflows and AI applications.
A desire to learn how to manage, analyze, and visualize data using AWS services and tools such as SageMaker, S3, Glue, and QuickSight.
Interest in applying ML models to real-world scenarios using AWS infrastructure and exploring end-to-end ML pipelines on the cloud.
Willingness to build, train, deploy, and scale machine learning models using Python and AWS-based frameworks and APIs.
Familiarity with basic programming in Python and foundational knowledge of machine learning concepts is recommended.

Description

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 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 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Data Science and AWS

1.1. Basics of Data Science: Definitions, Workflows, and Tools

1.2. Overview of Machine Learning: Types, Algorithms, and Use Cases

1.3. Introduction to AWS Cloud and Its Benefits for ML and Data Science

1.4. Overview of Key AWS Services for Data Science and ML

1.5. Hands-On Activity: Set up an AWS account and explore the AWS Management Console.

1.6. Conclusion of Introduction to Data Science and AWS

Module 2. Data Management on AWS

2.1. Data Storage Solutions on AWS: S3, DynamoDB, and RDS

2.2. Data Warehousing with Amazon Redshift

2.3. Data Integration and ETL Processes with AWS Glue

2.4. Data Lake Architecture on AWS

2.5. Hands-On Activity: Create an S3 bucket and manage datasets.

Perform basic ETL using AWS Glue.

2.6. Conclusion of Data Management on AWS

Module 3. Introduction to AWS SageMaker

3.1. Overview of SageMaker Capabilities

3.2. Data Preparation and Labeling with SageMaker Data Wrangler

3.3. Building ML Models with SageMaker Studio

3.4. Pre-built Models and SageMaker JumpStart

3.5. 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 AWS

4.1. Model Training and Tuning with SageMaker

4.2. Feature Engineering and Model Optimization

4.3. Hyper-parameter Tuning and AutoML with SageMaker

4.4. Managing Model Artifacts with SageMaker Model Registry

4.5 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 AWS

Module 5. Deploying and Scaling ML Models on AWS

5.1. Model Deployment with SageMaker Endpoints

5.2. Batch Transform for Large-Scale Inference

5.3. Real-Time Inference and Monitoring Deployed Models

5.4. Scaling Models with Elastic Inference and Multi-Model Endpoints

5.5. 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 AWS

Module 6. Advanced Machine Learning on AWS

6.1. Deep Learning with AWS and SageMaker

6.2. Custom Training with TensorFlow and PyTorch in SageMaker

6.3. Reinforcement Learning with AWS DeepRacer

6.4. ML Pipelines for Automation and Workflow Management

6.5. 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 AWS

Module 7. Analytics and Visualization on AWS

7.1. Data Analytics with AWS QuickSight

7.2. Log and Metric Analysis with CloudWatch and Athena

7.3. Integrating ML Models with Visualization Dashboards

7.4. Advanced Analytics Workflows with AWS Data Pipeline

7.5. Hands-On Activity: Create a visualization dashboard with AWS QuickSight.

7.6. Conclusion of Analytics and Visualization on AWS

Module 8. Security, Cost Management, and Best Practices

8.1. Ensuring Data Security with AWS IAM and Encryption

8.2. Managing Costs for Data Science and ML Projects on AWS

8.3. Best Practices for ML and Data Science Workflows on AWS

8.4. Common Pitfalls and How to Avoid Them

8.5.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 Practices

Module 9. Real-World Use Cases and Applications

9.1. E-commerce: Customer Segmentation and Recommendation Systems

9.2. Finance: Fraud Detection and Risk Analysis

9.3. Healthcare: Predictive Analytics and Diagnostics

9.4. Manufacturing: Predictive Maintenance and Quality Control

9.5. Media and Entertainment

9.6. Education and Training

9.7. Hands-On Activity: Work on a domain-specific case study using AWS services

9.8. Conclusion of Real-World Use Cases and Applications.

Part 2

Capstone Project.

Who this course is for:

Aspiring data scientists, ML engineers, and AI practitioners who want to build real-world machine learning solutions using AWS.
Developers and software engineers looking to integrate scalable data pipelines and ML models into cloud-native applications.
Analysts, business intelligence professionals, and visualization experts aiming to leverage AWS tools like QuickSight and Athena for data-driven insights.
IT professionals, system architects, and cloud engineers interested in managing secure, cost-effective, and optimized AI/ML workflows on AWS.
Educators, researchers, and students who want hands-on experience with industry-standard AWS tools for machine learning and analytics.

Certification in Machine Learning and Data Science with AWS

What you'll learn

Explore related topics

Course content

Introduction to Data Science and AWS9 lectures • 58min

Module 2. Data Management on AWS7 lectures • 42min

Module 3. Introduction to AWS SageMaker7 lectures • 40min

Module 4. Building Machine Learning Models on AWS7 lectures • 43min

Module 5. Deploying and Scaling ML Models on AWS7 lectures • 45min

Module 6. Advanced Machine Learning on AWS7 lectures • 48min

Module 7. Analytics and Visualization on AWS7 lectures • 46min

Module 8. Security, Cost Management, and Best Practices8 lectures • 56min

Module 9. Real-World Use Cases and Applications10 lectures • 49min

Capstone Project1 lecture • 33min

Requirements

Description

Who this course is for: