Full Stack AI Training

Introduction

Anyone who wants to learn more about artificial intelligence (AI) can take the AI & Machine Learning Diploma Course, which is a 3-month practical training program. It covers everything from Python programming and data preprocessing to machine learning, natural language processing (NLP), and neural networks. 

The course is designed to help students gain real-world skills step by step. With 48 hands-on training hours, live projects, and instructor-led guidance, you'll not only learn how AI works, but you'll also build, train, and deploy intelligent systems yourself. 

This course gives you the technical confidence to enter the fast-growing field of AI and Machine Learning, whether you want to start a career in data science or just want to improve your current skills.

AI & Machine Learning - 3 Months Diploma Course

• Duration: 3 Months (12 Weeks)
• Schedule: 2 Days/Week, 2 Hours/Class
• Total Hours: 48 Hours
• Level: Beginner to Intermediate

MONTH 1: Python & OOP for AI (Weeks 1-4)

Week 1: Python Basics for AI

• Class 1: Python Setup & Fundamentals
  • Installing Python, Jupyter Notebook
  • Variables, Data Types, Operators
  • Input/Output functions
• Class 2: Control Structures
  • Conditional statements (if, elif, else)
  • For loops and While loops
  • Loop control statements

Week 2: Python Data Structures for AI

• Class 1: Lists, Tuples & Arrays
  • List methods and comprehensions
  • NumPy arrays introduction
  • Array operations for data
• Class 2: Dictionaries & Sets
  • Key-value pairs for data storage
  • Set operations for data cleaning
  • Data structure selection for AI

Week 3: Functions & Libraries

• Class 1: Python Functions
  • Function definition and parameters
  • Lambda functions for data processing
  • *args and **kwargs
• Class 2: AI Libraries Setup
  • Installing NumPy, Pandas, Matplotlib
  • Importing and using libraries
  • Basic data visualization

Week 4: OOP for AI Projects

• Class 1: OOP Basics
  • Classes and objects
  • init method, attributes
  • Methods in classes
• Class 2: OOP Advanced for AI
  • Inheritance in ML models
  • Method overriding
  • Building AI model classes

Month 1 Project: Data Analysis with Python

MONTH 2: AI Fundamentals & Machine Learning (Weeks 5-8)

Week 5: Introduction to AI & ML

• Class 1: AI Basics
  • What is Artificial Intelligence?
  • Types of AI (Narrow AI, General AI)
  • AI applications in real world
• Class 2: Machine Learning Overview
  • What is Machine Learning?
  • Supervised vs Unsupervised Learning
  • ML workflow and process

Week 6: Data Preprocessing

• Class 1: Data Collection & Cleaning
  • Loading datasets (CSV, Excel)
  • Handling missing values
  • Data normalization
• Class 2: Data Visualization
  • Matplotlib for basic plots
  • Seaborn for statistical plots
  • Data analysis and insights

Week 7: Supervised Learning

• Class 1: Regression Models
  • Linear Regression
  • Model training and prediction
  • Evaluation metrics
• Class 2: Classification Models
  • Logistic Regression
  • K-Nearest Neighbors
  • Model accuracy evaluation

Week 8: Unsupervised Learning

• Class 1: Clustering
  • K-Means Clustering
  • Customer segmentation
  • Cluster evaluation
• Class 2: Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • Data visualization with PCA
  • Feature selection

Month 2 Project: Customer Segmentation Model

MONTH 3: Advanced AI & Real Projects (Weeks 9-12)

Week 9: Natural Language Processing (NLP)

• Class 1: NLP Basics
  • Text preprocessing
  • Tokenization, Stemming, Lemmatization
  • Bag of Words model
• Class 2: Text Classification
  • Sentiment Analysis
  • Spam detection model
  • NLP with scikit-learn

Week 10: Introduction to Deep Learning

• Class 1: Neural Networks Basics
  • What are Neural Networks?
  • Activation functions
  • Basic architecture
• Class 2: TensorFlow/Keras Introduction
  • Installing TensorFlow
  • Building first neural network
  • Model training and evaluation

Week 11: AI Model Deployment

• Class 1: Model Saving & Loading
  • Saving trained models
  • Model serialization
  • Loading models for prediction
• Class 2: Building AI Applications
  • Creating prediction APIs
  • Web interface for AI models
  • User input handling

Week 12: Final AI Project

• Class 1: Project Development
  • End-to-end AI project
  • Data collection to deployment
  • Model optimization
• Class 2: Project Presentation
  • Demo of AI applications
  • Code review
  • Future learning path

Month 3 Project: Choose One:

1. Sales Prediction System
2. Customer Churn Prediction
3. Spam Email Classifier
4. Sentiment Analysis Tool

Technologies Covered:

Programming:

• Python 3.x
• Jupyter Notebook
• OOP Concepts

AI/ML Libraries:

• NumPy (Numerical computing)
• Pandas (Data manipulation)
• Matplotlib & Seaborn (Data visualization)
• Scikit-learn (Machine Learning)
• TensorFlow/Keras (Deep Learning)

Tools:

• VS Code / Jupyter
• Git & GitHub
• Dataset handling

Learning Outcomes:

After this 3-month course, students will be able to:

• ✅ Write Python code for AI applications
• ✅ Understand and apply OOP concepts in AI
• ✅ Preprocess and visualize data
• ✅ Build and train Machine Learning models
• ✅ Work with NLP for text data
• ✅ Create basic Neural Networks
• ✅ Deploy AI models for predictions
• ✅ Build complete AI projects from scratch

Conclusion

Students who finish IPEI Fullstack AI Training course will have a deep understanding of AI ideas, machine learning algorithms, and how to use AI models in real-life situations. This class not only covers the basics of AI, but it also gives students real-world practice in making AI solutions. Graduates will be ready to take on tasks in the AI field, which is always changing, and they will know a lot about how to build and use AI-driven apps.

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