AI & Machine Learning Concepts with Python

Course Description

This hands-on course introduces foundational AI and machine learning concepts using Python and key libraries such as scikit-learn and TensorFlow. Participants will learn how to prepare data, build and evaluate models using supervised and unsupervised techniques, explore neural networks, and deploy models via REST APIs. The course also covers model optimization, emerging trends, and ethical AI practices, culminating in a capstone project simulating a real-world machine learning workflow.

Course Objectives

Prepare and preprocess real-world datasets for machine learning using techniques such as normalization, encoding, and dimensionality reduction.

Build, train, and evaluate machine learning models using both supervised and unsupervised learning algorithms with scikit-learn.

Design and deploy deep learning models using TensorFlow/Keras and expose them via APIs built with Flask or FastAPI.

Optimize model performance through hyperparameter tuning and explore emerging trends and ethical considerations in AI, including fairness, explainability, and federated learning.

Modules

Python Essentials for AI/ML
Data Preprocessing & Feature Engineering
Supervised Learning
Unsupervised Learning
Neural Network Basics
Advanced Architectures
Model Deployment with Flask/FastAPI
Model Optimization & Scaling
Capstone Project
Future Trends & Ethics

Audience: Aspiring data scientists, AI/ML enthusiasts, and developers

Pre-requisites

Python programming basics
Basic statistics and math knowledge

Career Pathways: AI/ML Developer, Data Scientist (Junior), Python Automation Engineer

Assessment

Certification

Dates:

Module	Date	Time	Zone	Duration (Days)
Content	Content	Content	Content	Content

Course Includes:

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