Advance Industry-Ready Java, Cloud & AI Engineering
Duration – 12 Days
Program Summary
Designed to strengthen programming fundamentals and enhance interview confidence for IT and product-based companies.
Java Programming & Coding Interview Preparation
- Build Strong Object-Oriented Programming Fundamentals
- Master Java Collection Framework
- Crack Coding Rounds with Confidence
Python, Data Science & Artificial Intelligence
- Build Strong Python Programming Fundamentals
- Master Data Analysis & Machine Learning Techniques
- Crack AI & Coding Interviews with Confidence
- Gain Hands-On Expertise in Deep Learning & Generative AI
Cloud Computing & Deployment Essentials
- Build Strong Cloud Computing Fundamentals
- Master Core Cloud Services & Deployment Models
- Crack Cloud Interviews with Confidence
Tools/Software/Hardware
- Java JDK, Eclipse / IntelliJ IDEA
- Python 3.x, NumPy, Pandas
- AWS Free Tier / Sandbox Account, AWS CLI
- Laptop/Desktop with a minimum 8 GB RAM and internet connectivity
Pre-requisites
- Basic computer and operating system knowledge
- Logical thinking and problem-solving skills
- Familiarity with any programming language is an added advantage
Take away
- Strong Programming & Problem-Solving skills for interviews
- Hands-On Expertise in AI & Data Technologies
- Cloud Deployment & Industry Readiness
- LMS Access with Short notes and Coding practice platform
- 1000+ Sample interview questions
- Digital Videos for reference
- Improved technical and interview readiness
Day 1:
Embedded Systems
Java Basics & Environment Setup
Topics
- Introduction to Core Java
- Features of Java
- Platform independence
- Object-oriented nature
- Robustness, security, portability
- C++ vs Java
- Memory management
- Pointers vs references
- Multiple inheritance differences
- Java Program Structure
- Class structure
- main() method
- Compilation and execution flow
- Environment Setup
- Installing JDK
- Understanding JDK, JRE, JVM
- Java execution process (source → bytecode → JVM)
- Hands-on
Hands-on
- Install JDK and configure environment variables
- Write and execute first Java “Hello World” program
- Modify program to understand class and main method behavior
Day 2:
Variables, Data Types & Control Statements
<p?Topics
- Variables in Java
- Local, instance, and static variables
- Data Types
- Primitive data types
- Non-primitive data types (String, arrays – intro)
- Operators
- Arithmetic, relational, logical, assignment
- Increment/decrement operators
- Control Statements
- Conditional statements: if, if-else, switch
- Looping statements: for, while, do-while
- break and continue
<pHands-on
- Write programs using different data types
- Implement decision-making programs
- Loop-based programs (number patterns, sum, factorial, etc.)
- Practice operator precedence examples
Day 3:
OOP Concepts & Arrays
Topics
- Object-Oriented Programming Concepts
- Class and Object
- Object creation and memory allocation
- Object Class (overview)
- toString(), equals(), hashCode()
- Arrays
- Single-dimensional arrays
- Multi-dimensional arrays
- Array initialization and traversal
- Real-world examples using OOP concepts
Hands-on
- Create classes and objects
- Use object reference variables
- Write programs using arrays
- Implement simple OOP-based applications
Day 4:
Keywords & Inheritance Concepts
Topics
- Static Keyword
- Static variables
- Static methods
- Static blocks
- this Keyword
- Referring to current object
- Constructor chaining
- super Keyword
- Accessing parent class members
- Constructor calling
- Inheritance
- Types of inheritance in Java
- Method overriding
- Abstraction
- Abstract classes
- Abstract methods
- Encapsulation
- Access modifiers
- Getters and setters
Hands-on
- Programs using static members
- Demonstrate this and super keywords
- Implement inheritance examples
- Create abstract classes and methods
- Apply encapsulation using access modifiers
Day 5:
Cloud – AWS
AWS fundamentals
Topics
- Introduction to the Course
- What is Cloud Computing and AWS?
Hands-On Practice
- Free Tier vs Sandbox
- AWS Account Overview
- Create your AWS Account
- [HOL] Configure Account and Create a Budget
- [HOL] Install Tools and AWS CLI
- Introduction
- The AWS Global Infrastructure
- The AWS Shared Responsibility Model
- Application Programming Interfaces (APIs)
- AWS Pricing Fundamentals
- The 6 Advantages of Cloud Computing
Day 6:
AWS Authentication and Access Control
Topics
- AWS Identity and Access Management (IAM)
- [HOL] Creating IAM Users and Groups
- IAM Roles and Policies
- [HOL] Switching IAM Roles
- IAM Identity Center
- [HOL] IAM Identity Center in Action
Day 7:
Amazon EC2, Auto Scaling and Load Balancin
Topics
- Server Virtualization
- Scaling Up vs Scaling Out
- High Availability and Fault Tolerance
- Amazon EC2 Overview
- [HOL] Launching Amazon EC2 Instances
Day 8:
Amazon VPC
Topics
- The OSI Model
- Routers, Switches and Firewalls
- IP Addressing
- Amazon Virtual Private Cloud (VPC)
- [HOL] Create a Custom VPC
- Security Groups and Network ACLs
- Block vs File vs Object Storage
- Amazon EBS and Instance Stores
- [HOL] Create and Attach an EBS Volume
- [HOL] EBS Snapshots and AMIs
- Amazon Elastic File System (EFS)
Day 9:
Artificial Intelligence & Machine Learning (AI/ML)
AI & Machine Learning Fundamentals
Topics
- AI vs ML vs Deep Learning
- Types of machine learning
- ML workflow
- Role of AI in Embedded & IoT
- Industry use cases
Hands-on
- Analyse datasets for ML suitability
- Identify ML type for given problem statements
- Map IoT problems to ML solutions
Day 10:
Core Machine Learning Algorithms
Topics
- Linear regression
- Logistic regression
- Decision trees
- KNN algorithm
- Model evaluation
Hands-on
- Train a regression model
- Perform classification on sample dataset
- Evaluate model accuracy
- Compare algorithm performance
Day:11
Python for AI/ML & Data Processing
Topics
- Python basics for ML
- Working with CSV and log files
- Data cleaning and preprocessing
- NumPy and Pandas basics
- Exploratory data analysis
Day 12:
AI/ML Integration, Edge AI & Use Cases
Topics
- ML model deployment concepts
- Edge AI fundamentals
- Predictive maintenance
- AI integration with IoT
- Industry expectations
Hands-on
- End-to-end ML case study
- Integrate ML output with IoT data
- Predict anomalies using ML
- Final review & interview discussion
