AI Course in Pune | Artificial Intelligence Course in Pune with Placement

Module 1: Foundations of Advanced AI

Recap of AI basics: Machine Learning, Deep Learning essentials

What makes AI "advanced"? Focus on scalability, complexity, and innovation

Overview of advanced AI domains: NLP, Computer Vision, Generative AI

Career paths: AI researcher, architect, specialist roles

Hands-on: Set up advanced tools (Python, Jupyter, Git)

Module 2: Advanced Python for AI

Python review: Data structures, functions, OOP

Libraries for advanced AI: NumPy, Pandas, SciPy

Multithreading and multiprocessing for performance

Memory management and optimization

Hands-on: Optimize a Python script for large datasets

Module 3: Mathematics for Advanced AI

Eigenvalues & SVD

Calculus: Partial derivatives, optimization gradients

Probability: Bayesian inference, Markov Chains

Information Theory: Entropy, KL Divergence

Hands-on: Solve matrix problems with Python

Module 4: Deep Learning Fundamentals

Neural network recap: Layers, weights, activations

Backpropagation and gradient descent in depth

Optimizers: Adam, RMSprop, learning rate schedules

Frameworks: TensorFlow 2.x, PyTorch setup

Module 5: CNN’s - Advanced

CNN architecture: Convolutions, pooling, strides

Advanced models: ResNet, Inception, EfficientNet

Transfer learning for custom datasets

Fine-tuning pre-trained models

Hands-on: Train a ResNet model on a custom image dataset

Module 6: Recurrent Neural Networks (RNNs) - Advanced

RNN recap: Sequential data processing

LSTM & GRU

Bidirectional RNN’s & attention mechanisms

Applications: Time series, speech recognition

Hands-on: Build an LSTM for stock price prediction

Module 7: Transformers and Attention Mechanisms

Transformer architecture: Encoder, Decoder, Self-Attention

Multi-Head Attention and Positional Encoding

Models: BERT, GPT, T5 overview

Applications: Text generation, translation

Hands-on: Fine-tune a BERT model for text classification

Module 8: Advanced NLP

Word2Vec & GloVe

Sequence-to-sequence models for translation

Contextual embeddings: ELMo, BERT, RoBERTa

Advanced tasks: Summarization, question answering

Hands-on: Build a text summarization model

Module 9: Generative Adversarial Networks (GANs) - Part 1

GAN basics: Generator vs. Discriminator

Training challenges: Mode collapse, instability

Loss functions: Wasserstein, Least Squares

Applications: Image synthesis, data augmentation

Hands-on: Train a basic GAN for image generation

Module 10: GANs - Part 2

DCGAN, StyleGAN & CycleGAN

Conditional GANs for controlled outputs

Evaluation: Fréchet Inception Distance (FID)

Practical use: Art generation, face editing

Hands-on: Implement a StyleGAN model

Module 11: Variational Autoencoders (VAEs)

VAE structure: Encoder, latent space, decoder

Reparameterization trick and KL Divergence

Generating new data with VAEs

Applications: Data denoising, anomaly detection

Hands-on: Build a VAE for MNIST digit generation

Module 12: Diffusion Models

Diffusion process: Noise addition and removal

DDPM

Comparison of GANs & VAEs

High-quality image generation

Hands-on: Train a diffusion model for images

Module 13: Reinforcement Learning - Advanced

Recap: Agents, rewards, environments

Deep Q-Learning (DQN) with neural networks

Policy Gradient Methods: REINFORCE, PPO

Applications: Robotics, game AI

Hands-on: Build a DQN for a game environment

Module 14: Graph Neural Networks (GNNs)

Graphs: Nodes, edges, and their importance

GNN basics: Message passing, aggregation

Models: Graph Convolutional Networks (GCN), GraphSAGE

Applications: Social networks, molecular modeling

Hands-on: Train a GCN on a graph dataset

Module 15: Self-Supervised Learning

What is self-supervised learning? No labeled data

Techniques: Contrastive Learning, SimCLR, MoCo

Pretext tasks: Rotation prediction, masking

Applications: Pre-training for downstream tasks

Hands-on: Implement a contrastive learning model

Module 16: Federated Learning

Decentralized AI: Training across devices

Privacy preservation with federated averaging

Challenges: Communication costs, data heterogeneity

Use cases: Healthcare, mobile apps

Hands-on: Simulate a federated learning setup

Module 17: AI for Computer Vision - Advanced

Object detection: YOLO, Faster R-CNN

Semantic segmentation: U-Net, DeepLab

Pose estimation and tracking

Real-time vision systems

Hands-on: Build an object detection model

Module 18: Audio and Speech Processing

Audio data: Waveforms, spectrograms

Speech-to-text with RNNs and Transformers

Text-to-speech with WaveNet

Applications: Virtual assistants, music generation

Hands-on: Train a speech recognition model

Module 19: Multimodal AI

Combining text, images, and audio

Models: CLIP, DALL·E, VQ-VAE

Cross-modal learning challenges

Applications: Image captioning, video analysis

Hands-on: Build a text-to-image pipeline

Module 20: Optimization Techniques for AI

Advanced optimizers: AdamW, LAMB

Gradient clipping and learning rate scheduling

Second-order methods: Newton, Quasi-Newton

Stabilizing training for large models

Hands-on: Optimize a deep learning model

Module 21: AI Model Interpretability

Why interpretability matters in AI

Techniques: SHAP, LIME, Grad-CAM

Visualizing model decisions

Trust and transparency in deployment

Hands-on: Interpret a CNN’s predictions

Module 22: Scalable AI Systems

Distributed training: Data vs. model parallelism

GPU/TPU usage with PyTorch and TensorFlow

Handling large datasets with batching

Cloud tools: AWS SageMaker, GCP AI Platform

Hands-on: Train a model on multiple GPUs

Module 23: AI Security and Robustness

Adversarial attacks: FGSM, PGD

Defending models: Adversarial training

Data poisoning and model theft risks

Secure AI deployment

Hands-on: Test an attack on a model

Module 24: AI in Industry - Advanced Applications

Healthcare: Drug discovery, medical imaging AI

Finance: Algorithmic trading, fraud detection

Autonomous systems: Self-driving tech

Case studies: Cutting-edge deployments

Hands-on: Solve an industry problem

Module 25: Model Deployment and MLOps

Model serving: Flask, FastAPI, TensorFlow Serving

CI/CD pipelines for AI models

Monitoring: Drift detection, performance tracking

Cloud deployment: AWS, Azure, GCP

Hands-on: Deploy a model with an API

Module 26: Ethics and Responsible AI - Advanced

Bias mitigation: Fairness-aware algorithms

Privacy: Differential privacy, federated learning

AI regulations: GDPR, AI Act

Ethical case studies: Deepfakes, autonomous weapons

Discussion: Balancing innovation and responsibility

Module 27: Research Trends in Advanced AI

Emerging models: Sparse Transformers, NeRF

Quantum AI: Basics and potential

Open-source AI ecosystems: Hugging Face, PyTorch Hub

Reading research papers effectively

Hands-on: Replicate a recent AI paper

Module 28: Placement Preparation - Technical Skills

Resume building: Highlight advanced AI projects

Portfolio: Showcase GANs, Transformers, etc.

Coding interviews: Advanced AI problems

Mock technical interviews

Hands-on: Solve AI coding challenges

Module 29: Placement Preparation - Professional Skills

Behavioral interviews: Leadership, teamwork

Job search: LinkedIn, networking in Pune

Industry expectations: AI roles and skills

Mock HR interviews

Hands-on: Prepare a job pitch

Module 30: Capstone Project

Project selection: Advanced AI challenge (e.g., multimodal model, real-time AI)

Full cycle: Data prep, model design, evaluation, deployment

Presentation: Demo to peers and mentors

Placement support: Project showcased to recruiters

Final submission: Report and live demo