Module 1: Foundations of Computer Science

Before diving into AI, you need a strong foundation in computer science.

Topics Covered:

• Basics of programming with Python or C++

• Data Structures and Algorithms

• Logic building and problem-solving techniques

Outcome: You will be able to code efficiently and understand the backbone of AI systems.

Module 2: Mathematics for AI

Mathematics is the language of AI. Every algorithm is powered by mathematical principles.

Topics Covered:

• Linear Algebra (Matrices, Vectors, Eigenvalues)

• Probability and Statistics

• Calculus for optimization

• Graph theory fundamentals

Outcome: You will build the mathematical intuition required to design and understand machine learning models.

Module 3: Machine Learning

Machine Learning (ML) is the heart of AI. In this module, you will learn how machines can learn from data and improve automatically.

Topics Covered:

• Supervised Learning: Regression, Classification

• Unsupervised Learning: Clustering, Dimensionality Reduction

• Reinforcement Learning: Training agents through feedback

• Popular Algorithms: Decision Trees, SVM, Random Forest, k-means

• Model Training, Testing, and Validation

Outcome: Ability to build machine learning models and evaluate their performance.

Module 4: Deep Learning and Neural Networks

Deep Learning enables breakthroughs in computer vision, speech recognition, and more.

Topics Covered:

• Artificial Neural Networks (ANNs)

• Convolutional Neural Networks (CNNs) for images

• Recurrent Neural Networks (RNNs) for sequences

• Advanced Architectures: LSTM, Transformers

• Regularization and Hyperparameter tuning

Outcome: Confidence in building and deploying deep learning models for real-world applications.

Module 5: Natural Language Processing (NLP)

AI is not just about numbers — it’s also about understanding human language.

Topics Covered:

• Text Preprocessing and Tokenization

• Sentiment Analysis and Language Modeling

• Named Entity Recognition (NER)

• Tools: NLTK, spaCy, Hugging Face Transformers

• Applications: Chatbots, Text Summarization, Speech Recognition

Outcome: Ability to build AI systems that can read, understand, and respond to human language.

Module 6: Computer Vision

Computer Vision allows machines to see and interpret the world just like humans.

Topics Covered:

• Image Classification with CNNs

• Object Detection and Recognition

• Image Segmentation

• Filters, Pooling, and Feature Extraction

• Real-world Applications: Face Recognition, Medical Imaging, Autonomous Vehicles

Outcome: You will learn to create vision-based AI applications for various industries.

Module 7: AI Ethics and Responsible AI

AI is powerful, but it also brings ethical challenges. This module helps you understand how to build AI responsibly.

Topics Covered:

• AI Bias and Fairness

• Data Privacy and Security

• Explainability and Transparency

• Social and Economic Impact of AI

Outcome: Understanding of responsible AI practices that are essential for building trust in AI systems.

Module 8: AI in Business and Industry

AI is revolutionizing industries across the globe.

Topics Covered:

• AI in Healthcare: Diagnosis, Drug Discovery

• AI in Finance: Fraud Detection, Predictive Analytics

• AI in Marketing: Personalization, Recommendation Engines

• AI in Manufacturing: Predictive Maintenance, Automation

Outcome: Practical knowledge of how AI is applied in different sectors.

Module 9: Tools and Frameworks

Hands-on experience is key to mastering AI.

Topics Covered:

• Python programming for AI

• Frameworks: TensorFlow, PyTorch, scikit-learn

• Cloud platforms: Google AI, AWS AI, Microsoft Azure AI

• Deployment techniques: Flask, FastAPI, Docker

Outcome: Industry-relevant skills to implement and deploy AI solutions.

Module 10: Capstone Projects and Portfolio Building

Your learning journey ends with real-world projects that showcase your skills.

Examples of Projects:

• Image Classification System

• Chatbot for Customer Support

• Predictive Analytics Model for Finance

• Sentiment Analysis on Social Media Data

Outcome: A professional portfolio that demonstrates your expertise to employers.

Course Duration and Structure

Our AI classes are divided into three learning levels:

• Beginner Level (4–6 weeks): Basics of programming, math, and ML

• Intermediate Level (3–6 months): Deep learning, NLP, Computer Vision

• Advanced Level (6–12 months): Industry projects, AI Ethics, Capstone Portfolio

Both live instructor-led sessions and self-paced learning options are available, so you can learn at your own speed.

Why Choose Our Classes?

• Practical Learning: Focus on real-world applications and projects

• Expert Mentors: Learn from experienced AI professionals

• Flexible Delivery: Choose between live classes and recorded sessions

• Career Guidance: Resume building, interview preparation, and placement support

• Global Recognition: Certification that adds value to your career