Learn how to analyze, forecast, and interpret sequential data over time using deep learning techniques. This module covers feature engineering, data preprocessing, and the implementation of predictive models for real-world scenarios like stock market predictions or demand forecasting.

Dive into the fascinating world of image processing and recognition. Explore convolutional neural networks (CNNs) and learn how to build models for tasks like object detection, image classification, and face recognition.

Understand the foundation of Deep Learning with neural networks. Learn the architecture, activation functions, backpropagation, and optimization techniques that power modern AI systems.

Master the art of teaching machines to understand and generate human language. Topics include text preprocessing, sentiment analysis, and building models for tasks like text classification and language translation.

This module introduces the fundamentals of MLOps, focusing on streamlining ML workflows for production. You will learn efficient tools for collaboration, building scalable pipelines, and deploying models seamlessly. The course covers creating APIs with Flask and Postman, managing experiments using MLflow, and deploying scalable applications with Docker and Kubernetes on AWS. Finally, apply all these concepts in a real-world case study to design and implement robust ML systems.

Gain an overview of how Generative AI creates new data similar to existing datasets. Learn about the applications, from text and image generation to advanced problem-solving using generative models.

This module provides an in-depth understanding of Google Gemini and its advanced capabilities. You'll explore its functionalities, architecture, and how it operates in AI-driven tasks. The module includes a detailed overview of the Google Gemini API, with hands-on sessions covering both basic and advanced implementations. Additionally, you'll learn about Gemini Pro Vision for predictive modeling and analytics, along with implementing conversational memory features using Gemini Pro for enhanced user interactions.

Explore how LangChain simplifies the development of applications powered by large language models (LLMs). Understand its integration with generative AI to create sophisticated workflows and applications.

Unlock the power of Deep Learning to transform Natural Language Processing (NLP) tasks. This module delves into the core techniques and architectures that enable machines to understand, interpret, and generate human language. Explore advanced models like RNNs, LSTMs, GRUs, and Transformers, and learn how they drive applications such as sentiment analysis, language translation, and text summarization. With hands-on projects, this module equips you with the skills to build deep learning models that excel in real-world NLP tasks.

Learn the fundamentals of Recurrent Neural Networks (RNNs), a powerful deep learning architecture designed for sequential data. This module explains how RNNs process inputs in sequence, maintaining contextual memory across time steps, making them ideal for tasks like language modeling, speech recognition, and time-series forecasting. Explore the challenges of RNNs, such as vanishing gradients, and understand how advanced variants like LSTMs and GRUs overcome these limitations. Gain hands-on experience by implementing RNNs to tackle real-world problems involving sequential data.

Explore Gated Recurrent Units (GRUs), an efficient alternative to LSTMs in the family of Recurrent Neural Networks. GRUs simplify the architecture by using fewer gates, yet retain the ability to capture dependencies in sequential data. This module explains how GRUs balance performance and computational efficiency, making them ideal for tasks like speech recognition, machine translation, and time-series forecasting. Learn the inner workings of GRUs and implement them in hands-on projects to solve real-world challenges with sequential data.

Dive into Long Short-Term Memory (LSTM) networks, a specialized type of RNN designed to handle long-range dependencies in sequential data. This module breaks down the unique architecture of LSTMs, including their forget, input, and output gates, which enable them to retain important information over extended time periods. Perfect for applications like language modeling, sentiment analysis, and stock price prediction, LSTMs overcome the limitations of standard RNNs, such as vanishing gradients. Gain practical experience by building LSTM models to solve complex sequential data problems effectively.

Discover the transformative architecture that revolutionized Natural Language Processing and Generative AI: Transformers. This module dives into how Transformers use self-attention mechanisms to process entire sequences of data simultaneously, enabling faster and more accurate performance compared to traditional RNNs. Explore their applications in tasks like machine translation, text summarization, and large-scale language modeling. Understand the key components such as attention heads, positional encodings, and encoder-decoder structures, and gain hands-on experience by building Transformer-based models to tackle advanced NLP and AI challenges.