This live online course is tailored for professionals aiming to break into the world of Artificial Intelligence or advance their current roles with AI expertise. It begins with foundational AI concepts and quickly progresses into practical applications using Python. Learners will explore supervised and unsupervised machine learning, deep learning fundamentals, and model evaluation techniques. The course also dives into real-world use cases involving computer vision, NLP, and AI ethics. Through projects and case studies, learners will build AI solutions such as chatbots, image classifiers, and recommendation systems—bridging theory and practice to prepare for roles in AI development, data science, and automation.

Key Benefits:

Taught by industry experts with real-world experience
Combines theory with hands-on coding and case studies
Covers end-to-end pipeline: data preprocessing to model deployment
Ideal for upskilling or transitioning into AI-focused roles
Access to career-oriented projects and certification

What You’ll Learn:

AI Foundations

What is AI? History, applications, types of AI
Overview of machine learning, deep learning, and neural networks
Setting up Python environment for AI development

Machine Learning Essentials

Supervised learning: regression, classification
Unsupervised learning: clustering, dimensionality reduction
Model training, testing, tuning (train/test split, cross-validation)
Feature engineering and selection

Deep Learning & Neural Networks

Basics of neural networks and backpropagation
Building deep learning models using TensorFlow/Keras
CNNs for image processing
RNNs and LSTMs for sequential data

AI Applications

Natural Language Processing (NLP): text classification, sentiment analysis
Computer Vision: object detection, facial recognition
Building AI-powered applications: chatbots, recommenders

Advanced Topics

Model deployment and APIs
Explainable AI (XAI)
AI ethics, bias in algorithms, responsible AI development