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

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Deep Learning, NLP, and AI Applications

Dieser Kurs ist Teil von Spezialisierung „AI & Python Development Megaclass“

Dozent: Packt - Course Instructors

Bei enthalten

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6 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

6 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Recurrent Neural Networks (RNNs)
Kategorie: Deep Learning
Kategorie: Model Evaluation
Kategorie: Embeddings
Kategorie: Convolutional Neural Networks
Kategorie: Transfer Learning
Kategorie: Large Language Modeling
Kategorie: Data Preprocessing
Kategorie: Artificial Intelligence
Kategorie: Machine Learning Methods
Kategorie: Natural Language Processing
Kategorie: Artificial Neural Networks
Kategorie: Image Analysis
Kategorie: Computer Vision

Werkzeuge, die Sie lernen werden

Kategorie: Keras (Neural Network Library)
Kategorie: Tensorflow
Kategorie: PyTorch (Machine Learning Library)

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

Februar 2026

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8 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 6 Module

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.

Das ist alles enthalten

8 Videos2 Lektüren1 Aufgabe

8 Videos Insgesamt 137 Minuten

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

2 Lektüren Insgesamt 20 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

Neural Networks and Deep Learning Fundamentals - Assessment 15 Minuten

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.

Das ist alles enthalten

8 Videos1 Aufgabe

8 Videos Insgesamt 161 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

Convolutional Neural Networks (CNNs) - Assessment 15 Minuten

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.

Das ist alles enthalten

8 Videos1 Aufgabe

8 Videos Insgesamt 166 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

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

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.

Das ist alles enthalten

8 Videos1 Aufgabe

8 Videos Insgesamt 135 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

Transformers and Attention Mechanisms - Assessment 15 Minuten

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.

Das ist alles enthalten

8 Videos1 Aufgabe

8 Videos Insgesamt 140 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

Transfer Learning and Fine-Tuning - Assessment 15 Minuten

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.

Das ist alles enthalten

10 Videos1 Lektüre3 Aufgaben

10 Videos Insgesamt 157 Minuten

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

1 Lektüre Insgesamt 10 Minuten

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

3 Aufgaben Insgesamt 90 Minuten

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

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Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

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Häufig gestellte Fragen

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.