Master advanced ML and DL concepts
Convolutional Neural Networks (CNNs)
Learn about CNNs and their applications in image processing and computer vision.
Recurrent Neural Networks (RNNs)
Understand RNNs and their applications in sequence modeling.
Long Short-Term Memory (LSTM)
Learn about LSTMs and their ability to capture long-term dependencies in sequential data.
Advanced Architectures (ResNet, GANs, Transformers)
Dive into advanced deep learning architectures including ResNet, GANs, and Transformers.
Autoencoders and Variational Autoencoders (VAEs)
Learn about autoencoders and their variant, variational autoencoders, for unsupervised learning tasks.
Advanced NLP
Explore advanced NLP topics including sequence-to-sequence models, attention mechanisms, and state-of-the-art architectures like BERT and GPT.
Time Series Analysis
Learn about various techniques and models for time series analysis and forecasting.
Reinforcement Learning
Delve into reinforcement learning concepts and algorithms, including Q-learning and policy gradient methods.
Big Data and Scalability
Learn about working with large datasets and distributed training techniques.
Model Deployment
Learn how to save, load, and deploy models using various frameworks and cloud services.
Model Interpretability and Explainability
Explore techniques for interpreting and explaining machine learning models.
Projects
Apply your skills by working on projects involving image classification, time series forecasting, text generation, and more.
