This advanced course dives deep into Generative AI and deep learning concepts using Pytorch. It covers essential topics such as neural networks, activation functions, optimizers, loss functions, and deep learning applications like Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). With practical, hands-on projects, students will apply Pytorch for building models and solving real-world problems, including breast cancer prediction, flight fare prediction, and leaf disease detection. This course also touches on machine learning techniques such as model evaluation, hyperparameter tuning, and handling common issues like missing data and outliers.

Overview: (25-30 hours.)

• The course is structured into 33 modules, starting with an introduction to Generative AI, followed by the basics of deep learning concepts and tools.
• The course provides in-depth knowledge of Neural Networks, Activation Functions, Optimizers, and Loss Functions.
• Students will gain hands-on experience in building neural networks using Pytorch.
• It also includes deep learning projects like Breast Cancer Prediction and Flight Fare Prediction.
• The course explores Computer Vision basics, Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN).
• Later modules focus on machine learning concepts and practical tasks such as Hyperparameter Tuning, Model Evaluation, and Predictive Modeling.

Projects:

1. Breast Cancer Prediction with ANN Pytorch: Build a neural network to predict breast cancer outcomes using the ANN model in Pytorch.
2. Flight Fare Prediction with ANN Pytorch: Predict flight fares using artificial neural networks with Pytorch.
3. Leaf Disease Detection using CNN Pytorch: Develop a Convolutional Neural Network (CNN) to detect leaf diseases from images.
4. Generative AI Project - MNIST Digits Generation with Simple GAN: Implement a basic GAN to generate digits based on the MNIST dataset.
5. Predicting Sales for FMCG Products: Use machine learning algorithms to predict sales for a fast-moving consumer goods (FMCG) company.

Tools Required:

1. Pytorch: A deep learning framework for building and training models, particularly suited for research and production-level applications in deep learning.
2. NumPy & Pandas: For numerical computations and data manipulation.
3. Sklearn: For implementing machine learning models and preprocessing data (scaling, encoding, handling null values).
4. TorchVision: A library built on top of Pytorch for working with image data, particularly useful for Convolutional Neural Networks (CNN).
5. Matplotlib & Seaborn: For visualizing data and model results.
6. TensorBoard: For monitoring training processes and model metrics.
7. Jupyter Notebook: For interactive coding and experimenting with machine learning and deep learning projects.

Skills Required:

1. Deep Learning Fundamentals: A strong grasp of neural networks, CNN, and GAN architectures.
2. Pytorch: Understanding how to use Pytorch for model building, training, and optimization.
3. Computer Vision: Basic knowledge of image processing and CNN for object detection or classification.
4. Generative AI: Understanding the principles of GANs and how to use them for data generation.
5. Machine Learning: Familiarity with traditional machine learning algorithms like linear models, SVM, Decision Trees, and Ensemble Methods.
6. Data Preprocessing: Skills in handling missing values, scaling, and encoding data using libraries like Pandas and Sklearn.
7. Model Evaluation: Proficiency in evaluating the performance of models using metrics such as accuracy, F1-score, and confusion matrices.
8. Python: A strong understanding of Python programming, especially in data science and AI.

Day-wise Topics List:

Day 1 - Introduction to Generative AI

Day 2 - Pytorch Basics

Day 3 - Deep Learning Concepts - Neural Networks, Activation Functions, Optimizers, Loss Functions

Day 4 - Creating Basic Neural Network with Pytorch

Day 5 - Project 1 - Breast Cancer Prediction with ANN Pytorch

Day 6 - Project 2 - Flight Fare Prediction with ANN Pytorch

Day 7 - Computer Vision Basics

Day 8 - Torch Vision

Day 9 - Basics of Convolutional Neural Network (CNN)

Day 10 - Project 3 - Leaf Disease Detection using CNN Pytorch

Day 11 - GANs Basics

Day 12 - MNIST Digits Generation using Pytorch with Simple GAN

Day 13 - Advanced Generative Models

Day 14 - Advanced Neural Network Architectures

Day 15 - Implementing Autoencoders

Day 16 - Image Generation with GANs

Day 17 - Advanced Computer Vision Techniques

Day 18 - Transfer Learning for Image Classification

Day 19 - Fine-tuning Pretrained CNN Models

Day 20 - Deep Learning Optimizers and Advanced Loss Functions

Day 21 - Project - Advanced GAN Application

Day 22 - Pandas Basics

Day 23 - Sklearn Library (Scaling, Encoding)

Day 24 - Sklearn Library (Null, Outlier Handling)

Day 25 - Sklearn Library (Train Test, Feature Selection)

Day 26 - ML Algorithm (Linear Models)

Day 27 - ML Algorithm (SVM, Decision Trees)

Day 28 - ML Algorithm (Ensemble Algorithms, Naive Bayes, KNN)

Day 29 - Hyperparameter Tuning, Model Evaluation

Day 30 - Project - ML Classification Project

Day 31 - Predicting the Sales of Products for an FMCG Company

Day 32 - Top 100 Interview Questions on Generative AI

Day 33 - Final Project Presentation and Wrap-up