🚀 8-Week AI & Software Engineering Practical Training

​🚀 8-Week AI & Software Engineering Practical Training
Audience: Beginners / newcomers to tech (no prior coding required)
Format: 3 sessions per week × 2 hours (flexible: online or in-person)​
Goal: Build solid programming foundations and ship a simple but real AI-powered web application from scratch, focusing on practical skills used in modern software teams.​

​Week 1: Foundations of AI & Software Engineering

• ​What is Artificial Intelligence? Everyday use cases in streaming, e-commerce, social media, finance, and cybersecurity.
  
  

• ​What software engineers actually do: problem-solving, requirements, breaking problems into smaller tasks, and debugging.​
  
  

• ​Tooling setup: Python, VS Code, Git & GitHub, virtual environments, and browser-based notebooks.
  
  

• ​Hands-on: Write your first Python scripts and push your first “Hello AI World” repo to GitHub.

​Week 2: Python for Real-World Problem Solving

• ​Core Python: variables, data types, conditions, loops, functions, and modular code.​
  
  

• ​Data structures in practice: lists, dictionaries, and working with JSON for web and AI APIs.
  
  

• ​Error handling and debugging strategies for beginners.
  
  

• ​Hands-on: Build and refactor a small console app (e.g., expense tracker, quiz app, or to-do manager).
  
  

​Week 3: Data Skills for AI

• ​How AI “learns”: datasets, features, labels, training vs inference.
  
  

• ​Using Pandas to load, clean, and transform data from CSV and web sources.
  
  

• ​Visualizing data with Matplotlib/Seaborn to spot patterns and issues.​
  
  

• ​Responsible data use: bias, representativeness, and privacy basics.
  
  

• ​Hands-on: Clean and explore a real dataset and produce charts plus a short “insights” summary.
  
  

​Week 4: First Steps in Machine Learning

• ​Key ML concepts: train/test split, metrics, overfitting, and model lifecycle. Classic models with scikit-learn: regression and classification.
  
  

• ​Evaluating models with accuracy, MAE, confusion matrix, and simple tradeoffs.
  
  

• ​Hands-on: Train at least two models on your dataset, compare performance, and save the best model.
  
  

​Week 5: From Model to AI Feature (APIs & Integration)

• ​What is an API? Requests, responses, endpoints, and JSON in simple terms.
  
  

• ​Building a minimal backend using Flask or FastAPI to serve AI predictions.​
  
  

• ​Handling inputs safely, basic validation, and simple logging.
  
  

• ​Hands-on: Wrap your ML model in an HTTP endpoint and test it with sample inputs.​
  
  

​Week 6: Frontend, UX, and AI Experience Design

• ​Web basics: HTML forms, simple CSS, and how browsers talk to APIs.
  
  

• ​Connecting a web page to your AI API using fetch/AJAX.
  
  

• ​Designing for clarity: explaining predictions, showing confidence, and avoiding confusing outputs.
  
  

• ​Hands-on: Build a simple one-page app (e.g., “AI Movie Recommender” or “AI Study Helper”) and run it end-to-end with your API.
  
  

​Week 7: Professional AI Engineering Practices

• ​Real-world AI use cases across Finance, Healthcare, Marketing, and Cybersecurity.
  
  

• ​Software engineering habits: version control workflows, branching, pull requests, and documentation basics.​
  
  

• ​Intro to prompt engineering and using hosted models (e.g., language or vision APIs) as building blocks.
  
  

• ​Midterm Project Presentations: Each participant presents a working mini AI feature for feedback.
  
  

​Week 8: Capstone Delivery & Career Launch

• ​Capstone Project: Choose a use case (movies, sales, health, education, or cybersecurity-inspired examples) and design an AI-powered application.
  
  

• ​Build & polish: Clean → Analyze → Train → Wrap in API → Connect UI → Prepare demo with clear README and screenshots.
  
  

• ​Career & next steps: Overview of AI Engineer, ML Engineer, Software Developer, and related roles, plus how to present your work on GitHub and LinkedIn.​
  
  

​✅ Practical Training Deliverables & Outcomes

• ​Weekly Assignments: Short, focused tasks in Python, data cleaning, modeling, or UI/API integration.
  
  

• ​Midterm Project (Week 7): A functioning AI-powered mini app or notebook demo with a brief live or recorded presentation.​
  
  

• ​Capstone Project (Week 8): A portfolio-ready AI application (or robust notebook) with documentation and a walkthrough.​
  
  

• ​Outcome: Students complete the training with one substantial project, several smaller exercises, and a clear roadmap into junior software or AI roles.