AI/ML Professional Curriculum

Master deep learning fundamentals, build generative AI applications, and implement production-ready MLOps pipelines. From theory to deployment, become an AI engineer.

Recommended for Students and Working Professionals

CURRICULUM

Your Learning Journey

Our curriculum is researched, developed & updated by understanding the global scope & job demands. Conducted by industry-leading expert instructors, the program offers more than 85% of an in-depth practical approach backed by essential theoretical frameworks.

CLASS FORMAT

Physical & Online Classes (Day and Night)

Skills you'll learn

Deep Learning Fundamentals: Master CNNs, RNNs, LSTMs, and Transformers.
Generative AI & LLMs: Build with GPT, Gemini, and RAG systems.
Computer Vision: Image classification, object detection, and generation.
MLOps & Deployment: Production pipelines with Docker, MLflow, and CI/CD.
Hands-on Projects: 12 lab projects and a capstone for your portfolio.

Month 1: Foundational Deep Learning

Module 1: Computer Vision (CV)

1.1 CV Fundamentals & Image Preprocessing

Introduction to computer vision and key tasks (classification, detection)
Image preprocessing: resizing, normalization, augmentation

1.2 Convolutional Neural Networks (CNNs)

Core CNN architecture and feature extraction
Convolution, pooling, and activation layers

1.3 Transfer Learning & Pre-Trained Models

Using VGG16, ResNet, MobileNet
Fine-tuning models for new datasets

Module 2: Natural Language Processing (NLP)

2.1 NLP Fundamentals & Text Preprocessing

Tokenization, stemming, stop-word removal
Intro to NLP tasks (sentiment analysis, summarization)

2.2 RNNs & LSTMs

Sequence modeling and long-term dependencies
Handling vanishing gradients

2.3 Transformers & LLMs

Self-attention and positional encoding
Overview of GPT and BERT architectures

Module 3: Time Series Analysis

3.1 Time Series Fundamentals

Trend, seasonality, and cyclic patterns
Traditional methods: ARIMA, Exponential Smoothing

3.2 Deep Learning for Time Series

RNNs and LSTMs for sequential prediction
Sequence preparation and sliding windows

3.3 Advanced Models & Feature Engineering

Temporal Convolutional Networks (TCNs)
Lag and rolling features

Module 4: Deep Reinforcement Learning (DRL)

4.1 RL & the Markov Decision Process

Agent, environment, state, action, reward concepts
Q-learning basics

4.2 Deep Q-Networks (DQNs)

Integrating neural networks with Q-learning
Handling high-dimensional states

4.3 Policy-Based Methods & Actor-Critic

Balancing exploration and exploitation
Actor-Critic architecture

Month 2: Applied AI and Generative Systems

Module 5: Applied Visual Intelligence

5.1 Vision APIs & Foundation Models

Using Google Vision AI, Open-Source models
Accessing APIs for object and face detection

5.2 Object Detection & Image Analysis

Using YOLO for image/video inference
Bounding boxes and confidence scores

5.3 Generative AI for Image Creation

Stable Diffusion, DALL·E 3
Prompt engineering for image generation

Module 6: Generative AI & LLM Engineering

6.1 Prompt Engineering & LLM APIs

Effective prompting for Gemini/GPT models
Summarization and structured data extraction

6.2 Fine-Tuning & Retrieval-Augmented Generation (RAG)

Customizing smaller LLMs
Building domain-specific assistants using RAG

6.3 Multimodal AI (Text + Vision)

Image captioning and VQA using CLIP/Gemini

Module 7: Time Series Forecasting & Predictive Solutions

7.1 AutoML for Forecasting

Rapid model selection and deployment

7.2 Traditional Models with Libraries

Prophet, Statsmodels for ARIMA/Exponential Smoothing

7.3 Deep Learning for Anomaly Detection

Using pre-trained models for sensor data analysis

Module 8: AI Agents & Deployment

8.1 Agentic AI & Function Calling

Allowing LLMs to interact with APIs and tools

8.2 Integrating AI Models in Web Apps

Flask/Streamlit deployment basics

8.3 MLOps Introduction

Versioning, monitoring, and deployment best practices

Month 3: Frameworks, Libraries & Production

Module 9: PyTorch – Dynamic Engine for Research

9.1 PyTorch Fundamentals

Tensors and autograd engine

9.2 Custom Models with nn.Module

Defining forward passes and state management

9.3 Training Loops & Optimization

Building loops from scratch, loss and optimizer steps

Module 10: TensorFlow and Keras – Production Ready

10.1 Keras API for Rapid Prototyping

Sequential and Functional APIs

10.2 tf.data & Distributed Training

Data pipelines, multi-GPU setups

10.3 TF Serving & TFLite Deployment)

Preparing cloud and mobile deployments

Module 11: Hugging Face Ecosystem

11.1 Hub, Pipelines, and Tokenizers

Using the Transformers library efficiently

11.2 Datasets & Metrics Libraries

Stream, filter, and evaluate large datasets

11.3 Accelerate & PEFT (LoRA)

Multi-device training and efficient fine-tuning

Module 12: MLOps – The AI Production Lifecycle

12.1 Experiment Tracking & Optimization

Using MLflow or Weights & Biases

12.2 Data & Model Version Control

DVC principles and model registry

12.3 CI/CD/CT for ML Pipelines

Using Docker and workflow orchestration tools

Projects & Hands-On Labs

This course emphasizes practical learning through hands-on labs and real-world datasets. Each module includes a project designed to help learners apply Python and data science concepts step by step.

Lab:

MRI Brain Tumor Classification Using CNNs

Learners build and fine-tune a convolutional neural network for medical image classification using transfer learning.

Deliverable

A trained CNN model with evaluation results and performance metrics.

Lab:

Text Summarization Tool Using Transformers

Learners develop a text summarization system using a pre-trained transformer model such as T5 or BART.

Deliverable

A functional text summarization tool with sample inputs and outputs.

Lab:

Cryptocurrency Price Prediction Using LSTM

Learners build a deep learning model to predict time series trends using historical cryptocurrency data.

Deliverable

An LSTM-based forecasting model with visualized predictions.

Lab:

Atari Game Agent Using Deep Q-Networks (DQN)

Learners train an AI agent to play an Atari game (e.g., Breakout) using reinforcement learning.

Deliverable

A trained RL agent with performance logs and gameplay results.

Lab:

Smart Security Camera with Object Detection

Learners build an AI-powered security system using object detection models to analyze images or video streams.

Deliverable

A smart object detection system with visual output.

Lab:

Document-Based AI Assistant Using RAG

Learners create an AI assistant that answers questions using custom documents via Retrieval-Augmented Generation.

Deliverable

A document-aware AI assistant with query and response examples.

Lab:

Retail Sales Forecasting Using Prophet

Learners build a forecasting system for retail sales using time series models.

Deliverable

A sales forecasting model with visual reports.

Lab:

AI-Powered Research Assistant Web App

Learners deploy an AI agent that performs research, summarization, and response generation through a web interface.

Deliverable

A deployed AI research assistant with a web interface.

Lab:

Custom Image Segmentation Model

Learners implement a custom segmentation model using PyTorch’s dynamic computation graph.

Deliverable

A PyTorch-based segmentation model with training loop and evaluation

Lab:

Model Deployment with SavedModel & TFLite

Learners rebuild a CV model and deploy it for cloud and mobile environments.

Deliverable

A production-ready TensorFlow model in SavedModel and TFLite formats.

Lab:

Fine-Tune an LLM Using LoRA

Learners fine-tune a language model efficiently using Hugging Face Accelerate and PEFT (LoRA).

Deliverable

A fine-tuned conversational LLM with performance benchmarks.

Lab:

End-to-End MLOps Pipeline

Learners build a full MLOps workflow covering data versioning, experiment tracking, and deployment.

Deliverable

A complete MLOps pipeline with documented workflow.

Capstone Mini Project

Develop and present a complete AI-powered solution that combines skills learned from all modules.

Demonstrate your ability to design, build, and deploy an AI system from data preprocessing to model deployment. This project showcases your complete skill set to potential employers.