Who is this course for?

This course is designed for individuals who are familiar with machine learning and want to deepen their knowledge in deep learning and natural language processing. It is ideal for aspiring AI practitioners, data scientists, and software engineers looking to enhance their skills in building advanced AI applications.

How long does it take to complete the course?

The course contains approximately 17 hours of video content. Depending on your pace and the time you dedicate to hands-on practice and projects, you can expect to complete the course in around 3-4 weeks.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Deep Learning, NLP, and AI Applications

Deep Learning, NLP, and AI Applications

Instructor: Packt - Course Instructors

Included with

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6 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build and optimize deep learning models using neural networks, CNNs, and RNNs.
Apply transformers and attention mechanisms to perform advanced NLP tasks.
Utilize transfer learning to fine-tune pre-trained models for specific tasks.
Develop AI-powered applications, including image classifiers, sentiment analyzers, and chatbots.

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There are 6 modules in this course

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.

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.

What's included

8 videos2 readings1 assignment

8 videos Total 137 minutes

Introduction to Week 9 Neural Networks and Deep Learning Fundamentals 1 minute
Day 1: Introduction to Deep Learning and Neural Networks 16 minutes
Day 2: Forward Propagation and Activation Functions 15 minutes
Day 3: Loss Functions and Backpropagation 16 minutes
Day 4: Gradient Descent and Optimization Techniques 22 minutes
Day 5: Building Neural Networks with TensorFlow and Keras 19 minutes
Day 6: Building Neural Networks with PyTorch 26 minutes
Day 7: Neural Network Project – Image Classification on CIFAR-10 22 minutes

2 readings Total 20 minutes

Introduction to the Course 'Deep Learning, NLP, and AI Applications' 10 minutes
Full Specialization Resources 10 minutes

1 assignment Total 15 minutes

Neural Networks and Deep Learning Fundamentals - Assessment 15 minutes

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.

What's included

8 videos1 assignment

8 videos Total 161 minutes

Introduction to Week 10 Convolutional Neural Networks (CNNs) 1 minute
Day 1: Introduction to Convolutional Neural Networks 26 minutes
Day 2: Convolutional Layers and Filters 24 minutes
Day 3: Pooling Layers and Dimensionality Reduction 24 minutes
Day 4: Building CNN Architectures with Keras and TensorFlow 18 minutes
Day 5: Building CNN Architectures with PyTorch 22 minutes
Day 6: Regularization and Data Augmentation for CNNs 19 minutes
Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10 28 minutes

1 assignment Total 15 minutes

Convolutional Neural Networks (CNNs) - Assessment 15 minutes

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.

What's included

8 videos1 assignment

8 videos Total 166 minutes

Introduction to Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling 1 minute
Day 1: Introduction to Sequence Modeling and RNNs 34 minutes
Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT) 25 minutes
Day 3: Long Short-Term Memory (LSTM) Networks 15 minutes
Day 4: Gated Recurrent Units (GRUs) 7 minutes
Day 5: Text Preprocessing and Word Embeddings for RNNs 24 minutes
Day 6: Sequence-to-Sequence Models and Applications 43 minutes
Day 7: RNN Project – Text Generation or Sentiment Analysis 18 minutes

1 assignment Total 15 minutes

Recurrent Neural Networks (RNNs) and Sequence Modeling - Assessment 15 minutes

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.

What's included

8 videos1 assignment

8 videos Total 135 minutes

Introduction to Week 12: Transformers and Attention Mechanisms 1 minute
Day 1: Introduction to Attention Mechanisms 15 minutes
Day 2: Introduction to Transformers Architecture 18 minutes
Day 3: Self-Attention and Multi-Head Attention in Transformers 21 minutes
Day 4: Positional Encoding and Feed-Forward Networks 20 minutes
Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT 20 minutes
Day 6: Advanced Transformers – BERT Variants and GPT-3 21 minutes
Day 7: Transformer Project – Text Summarization or Translation 19 minutes

1 assignment Total 15 minutes

Transformers and Attention Mechanisms - Assessment 15 minutes

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.

What's included

8 videos1 assignment

8 videos Total 140 minutes

Introduction to Week 13: Transfer Learning and Fine-Tuning 1 minute
Day 1: Introduction to Transfer Learning 15 minutes
Day 2: Transfer Learning in Computer Vision 26 minutes
Day 3: Fine-Tuning Techniques in Computer Vision 22 minutes
Day 4: Transfer Learning in NLP 17 minutes
Day 5: Fine-Tuning Techniques in NLP 26 minutes
Day 6: Domain Adaptation and Transfer Learning Challenges 15 minutes
Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task 18 minutes

1 assignment Total 15 minutes

Transfer Learning and Fine-Tuning - Assessment 15 minutes

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.

What's included

10 videos1 reading3 assignments

10 videos Total 157 minutes

Day 71: Spam Email Detector 23 minutes
Day 72: Text Sentiment Analyzer 12 minutes
Day 73: Handwriting Digit Recognition 25 minutes
Day 74: Voice Assistant 17 minutes
Day 75: Face Detection App 19 minutes
Day 76: Simple Recommendation System 14 minutes
Day 77: AI Chatbot with NLP 13 minutes
Day 78: Object Detection App 11 minutes
Day 79: Language Translator Tool 11 minutes
Day 80: Fake News Detector 14 minutes

1 reading Total 10 minutes

Conclusion to the Course 'Deep Learning, NLP, and AI Applications' 10 minutes

3 assignments Total 90 minutes

Full Course Practice Assessment 15 minutes
AI & Machine Learning Projects - Assessment 15 minutes
Full Course Assessment 60 minutes

Instructor

Packt - Course Instructors

Packt

1,471 Courses 392,127 learners

Offered by

Packt

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Frequently asked questions

This course focuses on the key concepts and techniques in deep learning and natural language processing (NLP), two core areas driving the development of modern AI applications. Understanding deep learning and NLP is crucial for developing systems that can perform complex tasks like image recognition, speech processing, and language translation. These skills are highly sought after in industries like healthcare, finance, and technology.

This course covers the fundamentals of deep learning, including neural networks, CNNs, RNNs, and transformers, as well as their applications in NLP. You will learn to build and optimize models for various AI tasks such as image classification, text generation, sentiment analysis, and language translation. The course also explores advanced topics like transfer learning, fine-tuning, and working with pre-trained models, equipping you with the tools to tackle real-world AI challenges.

After completing the course, you will be able to design and implement deep learning models for a variety of AI applications, including computer vision tasks (e.g., image classification), NLP tasks (e.g., text summarization, sentiment analysis), and even transfer learning for specific domains. You will also be skilled in using popular deep learning frameworks like TensorFlow, Keras, and PyTorch to build, train, and optimize models.

To get the most out of this course, you should have a basic understanding of machine learning and programming, particularly in Python. Familiarity with concepts like supervised learning and neural networks will be helpful. Some knowledge of linear algebra, calculus, and probability/statistics is beneficial, as these are foundational to understanding the underlying principles of deep learning models.