Introduction to ReactJS & modern frontend architecture

JSX, Components, Props & State

React Hooks (useState, useEffect, useContext, useReducer)

React Router & Client-side Routing

State Management with Context API & Redux Toolkit

Consuming REST APIs with Axios & Fetch

Component Libraries (MUI / Tailwind CSS / Joy UI)

Project: Build a full-featured frontend UI

Go fundamentals: syntax, variables, data types, control flow

Functions: Variadic, Anonymous, Deferred, Closures, Higher-order

Structs, Interfaces, and Composition

Error Handling: panic, recover, custom & sentinel errors

Pointers, Linked Lists & Tree implementation

Generics: Functions, Structs & Collections

Go Modules, Dependency Management

Unit Testing & Benchmarking

Building RESTful APIs with net/http

Router libraries (Chi / Gin / Echo)

Middleware implementation

File Handling: read, write, encode, encrypt files

Concurrency in Go: Goroutines, Channels, Mutex, WaitGroups

Context package: Timeout, Deadlines, Cancellation

Concurrency patterns: Worker Pool, Fan-in, Fan-out

Custom Authentication & JWT

Database integration: PostgreSQL / MongoDB using ORM libraries (GORM / Mongo-driver)

Caching with Redis

Dockerizing Golang apps

Multi-stage Docker builds for lean images

Docker Compose for local development

Build, tag, and push images to Docker Hub

Deployment on DigitalOcean / AWS / Render

Build a full-stack web app using ReactJS + Golang

Implement REST APIs, Authentication, and Database

Deploy the application with Docker

Performance optimization & benchmarking

Introduction to AI Agents and their applications

Understanding agent architectures: Tools, Thoughts, Actions, Observations

Exploring AI Agent frameworks (e.g., LangChain, Semantic Kernel)

Design patterns: ReAct (Reasoning and Acting), Planning, Metacognition

Building and deploying AI Agents using LangChain and LCEL

Implementing multi-agent systems and communication strategies

Security, ethical considerations, and observability in AI Agents

Project: Develop and deploy a functional AI Agent

Machine Learning fundamentals: supervised, unsupervised, and reinforcement learning

Deep Learning concepts: neural networks, CNNs, RNNs

Natural Language Processing (NLP) techniques and applications

Model training, evaluation, and optimization

Data preprocessing and feature engineering

Model deployment strategies and serving architectures

Monitoring, maintenance, and scalability of AI models

Ethical considerations, bias mitigation, and Explainable AI (XAI)

Project: Build and deploy an AI-powered application