Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Explore the cutting-edge world of Deep Learning, Natural Language Processing (NLP), and AI applications in this advanced course. You’ll gain hands-on experience with neural networks, CNNs, RNNs, transformers, and other state-of-the-art architectures. Learn to tackle real-world AI tasks such as image classification, sentiment analysis, text summarization, and language translation. This course will guide you through the powerful tools and techniques that are transforming industries, preparing you to build sophisticated AI models. You will start by building foundational knowledge in deep learning, understanding neural networks, forward propagation, and backpropagation. As the course progresses, you’ll work with convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence modeling, and transformers for NLP tasks. Additionally, you’ll learn transfer learning to leverage pre-trained models for efficient AI development. This course is designed for learners with a background in machine learning or deep learning who want to expand their expertise into NLP and advanced AI techniques. Whether you’re an AI researcher or aspiring AI engineer, this course will help you apply deep learning to real-world applications. By the end of the course, you will be able to design and implement deep learning models, optimize them for complex AI tasks, and apply cutting-edge NLP techniques to build powerful AI applications.

Syllabus

Neural Networks and Deep Learning Fundamentals

In this module, we will introduce you to the fundamentals of neural networks and deep learning. You will learn the core components of neural networks, including forward propagation, activation functions, and loss functions. The module also covers backpropagation, gradient descent, and hands-on experience with TensorFlow, Keras, and PyTorch to build and train neural networks. You'll finish by applying your knowledge to an image classification project using CIFAR-10.

Convolutional Neural Networks (CNNs)

In this module, we will focus on Convolutional Neural Networks (CNNs), which are essential for computer vision tasks like image recognition. You will learn about the building blocks of CNNs, including convolutional and pooling layers, and explore techniques like regularization and data augmentation to improve model performance. You will apply these concepts to a real-world image classification project using the Fashion MNIST or CIFAR-10 datasets.

Recurrent Neural Networks (RNNs) and Sequence Modeling

In this module, we will delve into Recurrent Neural Networks (RNNs) and sequence modeling. You will learn about RNN architectures, including LSTMs and GRUs, and their ability to handle sequential data. The module covers how to train these networks with backpropagation through time and applies them to text-based tasks like sentiment analysis and text generation.

Transformers and Attention Mechanisms

In this module, we will explore transformers and attention mechanisms, which have revolutionized the field of Natural Language Processing (NLP). You will gain a deep understanding of the transformer architecture, including self-attention and multi-head attention. The module also covers practical applications with pre-trained models like BERT and GPT for NLP tasks such as text summarization and machine translation.

Transfer Learning and Fine-Tuning

In this module, we will focus on transfer learning and fine-tuning techniques that allow you to leverage pre-trained models for new tasks. You will learn how to fine-tune models in both computer vision and NLP and tackle challenges like domain adaptation. The module includes hands-on projects where you will apply transfer learning techniques to custom tasks, enhancing the performance of your models.

AI & Machine Learning Projects

In this section, we will guide you through a series of exciting AI and machine learning projects. You will build applications like a spam email detector, sentiment analyzer, voice assistant, and object detection app. These projects provide hands-on experience in solving real-world problems with deep learning and machine learning techniques, solidifying your understanding of the concepts learned throughout the course.