Introduction to Artificial Intelligence with Python

Course Overview

This hands-on training course provides a practical introduction to Artificial Intelligence (AI) using Python, covering fundamental concepts, machine learning algorithms, and real-world applications. Participants will gain experience with key Python libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch while building AI models from scratch.

Course Objectives

By the end of this course, participants will be able to:

1. Understand core AI and machine learning (ML) concepts and their differences.
2. Use Python for AI development (data preprocessing, model training, evaluation).
3. Implement supervised & unsupervised learning (regression, classification, clustering).
4. Build neural networks for deep learning tasks (using TensorFlow/Keras).
5. Apply Natural Language Processing (NLP) and Computer Vision techniques.
6. Deploy a simple AI model and understand ethical considerations in AI.

Who Should Attend?

• Python Developers transitioning into AI/ML roles.
• Data Analysts & Scientists expanding into AI applications.
• Software Engineers interested in AI integration.
• Tech Enthusiasts & Students exploring AI with Python.

(Basic Python knowledge is required; no prior AI experience needed.)

Course Modules

Module 1: Python for AI & Data Science

• Python Basics Recap (NumPy, Pandas, Matplotlib)
• Data Handling & Preprocessing (Cleaning, Normalization, Feature Engineering)
• Exploratory Data Analysis (EDA) with Python
• Hands-on: Data Manipulation & Visualization

Module 2: Machine Learning Fundamentals

• Introduction to ML: Supervised vs. Unsupervised Learning
• Regression (Linear, Polynomial) & Classification (Logistic Regression, Decision Trees)
• Model Evaluation (Accuracy, Precision, Recall, ROC Curve)
• Hands-on: Building an ML Model with Scikit-learn

Module 3: Deep Learning with Python

• Neural Networks Basics (Perceptrons, Activation Functions)
• Introduction to TensorFlow & Keras
• Building & Training a Deep Learning Model (CNNs for Image Recognition)
• Hands-on: MNIST Digit Classification

Module 4: Natural Language Processing (NLP) & Computer Vision

• NLP Basics (Tokenization, TF-IDF, Word Embeddings)
• Sentiment Analysis & Text Classification
• Computer Vision with OpenCV (Image Processing, Object Detection)
• Hands-on: Building a Simple Chatbot or Image Classifier

Module 5: AI Deployment & Ethics

• Saving & Loading Models (Pickle, HDF5)
• Introduction to AI Deployment (Flask/Django API)
• Bias, Fairness & Ethical AI Considerations
• Final Project: End-to-End AI Solution