Artificial Intelligence and Machine Learning

Artificial Intelligence

📌 Course Overview

This course provides a complete foundation in Artificial Intelligence (AI) and Machine Learning (ML), starting from basic concepts to practical implementation. It is designed for beginners, students, and professionals who want to build a career in AI-driven technologies.

🎯 Course Objectives

  • Understand the fundamentals of AI and ML
  • Learn how machines learn from data
  • Build real-world machine learning models
  • Gain hands-on experience using Python
  • Explore modern AI tools and applications

🧠 Module 1: Introduction to AI & ML

  • What is Artificial Intelligence?
  • What is Machine Learning?
  • Difference between AI, ML, and Deep Learning
  • Real-life applications of AI

💻 Module 2: Python for AI/ML

  • Introduction to Python
  • Data types, variables, loops
  • Functions and libraries
  • Introduction to NumPy and Pandas

📊 Module 3: Data Handling & Preprocessing

  • Understanding datasets
  • Data cleaning techniques
  • Handling missing values
  • Data visualization using Matplotlib

🤖 Module 4: Machine Learning Basics

  • Types of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
  • Training and testing data
  • Overfitting & Underfitting

📈 Module 5: ML Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • K-Nearest Neighbors (KNN)
  • Clustering (K-Means)

🧪 Module 6: Model Evaluation

  • Accuracy, Precision, Recall
  • Confusion Matrix
  • Cross Validation

🧠 Module 7: Introduction to Deep Learning

  • Neural Networks basics
  • Introduction to TensorFlow / Keras
  • Simple AI model creation

🚀 Module 8: Real-World Projects

  • Chatbot Development
  • Image Classification
  • AI-based Prediction System

🛠️ Tools Covered

  • Python
  • Jupyter Notebook
  • Google Colab
  • TensorFlow / Keras