Exclusive Bundle (Machine Learning and Python Programming)

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About Bundle

The Special Combo Course in Python Programming and Machine Learning is designed to provide a strong foundation in both coding and intelligent system development. Starting with Python, learners will explore key programming concepts such as variables, loops, functions, data structures, and file handling. Python’s simplicity and versatility make it the perfect language for beginners and a powerful tool for professionals, especially in the fields of data science and AI. Through hands-on projects, students will gain practical experience that prepares them to tackle real-world coding challenges.

Building on this foundation, the course transitions into Machine Learning, where students learn how to develop models that can analyze data, recognize patterns, and make predictions. Topics include supervised and unsupervised learning, regression, classification, clustering, and model evaluation. Using libraries like Pandas, and Matplotlib, learners will apply machine learning techniques to real datasets. By the end of this combo course, participants will be equipped with the programming skills and analytical mindset needed to pursue careers in data science, AI, or software development.

Courses in the Bundle (2)

Cloud Computing With AWS

Demo Lecture

Course Curriculum

Module 1: Introduction to Cloud Computing

  • Overview of Cloud Computing
    • Definition, Characteristics, and Benefits
    • Types of Cloud Services: IaaS, PaaS, SaaS
    • Deployment Models: Public, Private, Hybrid, and Community Clouds
  • History and Evolution of Cloud Computing
    • From On-Premises to the Cloud
    • Major Milestones and Innovations
  • Why AWS for Cloud Computing?
    • AWS Overview and Market Leadership
    • Global Infrastructure: Regions and Availability Zones

Module 2: Core Concepts of Cloud Computing

  • Virtualization Basics
    • AWS EC2 Instances as an Example of Virtual Machines
    • Amazon ECS and EKS for Container Management
  • Networking in the Cloud
    • Amazon VPC: Subnets, Route Tables, and Gateways
    • Elastic Load Balancing (ELB) and AWS Direct Connect
  • Storage in the Cloud
    • AWS S3 for Object Storage: Buckets, Access Policies, and Lifecycle Management
    • Amazon EBS for Block Storage

Module 3: Cloud Infrastructure and Architecture

  • Scalability and Elasticity
    • Using AWS Auto Scaling for Elastic Workloads
    • Horizontal and Vertical Scaling with Amazon EC2
  • Cloud-Native Architecture
    • Serverless Computing with AWS Lambda
    • Orchestrating Containers with Amazon ECS and EKS
  • High Availability and Disaster Recovery
    • Multi-Region Deployments with AWS Route 53
    • Backup Strategies Using AWS Backup

Module 4: Security in the Cloud

  • Cloud Security Fundamentals
    • Shared Responsibility Model with AWS
    • Identity and Access Management (IAM) for User Roles and Permissions
  • Data Security
    • Encryption Using AWS Key Management Service (KMS)
    • Monitoring and Auditing with AWS CloudTrail
  • Compliance and Governance
    • Managing Compliance with AWS Config and Artifact

Module 5: Cloud Service Models

  • Infrastructure as a Service (IaaS)
    • Provisioning EC2 Instances
    • Managing Storage and Networking with Amazon VPC and Elastic IPs
  • Platform as a Service (PaaS)
    • Deploying Applications Using AWS Elastic Beanstalk
  • Software as a Service (SaaS)
    • Exploring AWS Marketplace for SaaS Solutions

Module 6: DevOps and Automation in the Cloud

  • Cloud and DevOps Integration
    • Building CI/CD Pipelines with AWS CodePipeline and CodeDeploy
  • Infrastructure as Code (IaC)
    • Automating Resource Provisioning Using AWS CloudFormation and Terraform
  • Monitoring and Logging
    • Application Monitoring with Amazon CloudWatch

Module 7: Cloud Economics and Optimization

  • Cost Management in AWS
    • Exploring AWS Pricing Models
    • Using AWS Budgets and Cost Explorer to Track and Optimize Costs
  • Resource Optimization
    • Reserved Instances and Savings Plans in EC2
    • Using Trusted Advisor for Recommendations
  • Sustainability in the Cloud
    • AWS’s Commitment to Renewable Energy and Carbon Footprint Reduction

Module 8: Emerging Trends in Cloud Computing

  • Edge Computing with AWS
    • AWS IoT Greengrass and AWS Wavelength
  • AI and Machine Learning in the Cloud
    • Using Amazon Sage Maker for Machine Learning Workflows
  • Hybrid and Multi-Cloud Strategies
    • Managing Hybrid Environments with AWS Outposts

Module 9: Projects

 

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