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⇱ Deep Learning and Advanced Techniques | Coursera

Deep Learning and Advanced Techniques

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Deep Learning and Advanced Techniques

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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Gain expertise in ensemble learning techniques like bagging, boosting, and gradient boosting to improve model accuracy.

  • Build and optimize neural networks using TensorFlow, Keras, and PyTorch for deep learning tasks.

  • Apply advanced techniques like transfer learning, fine-tuning, and handling complex data types.

  • Deploy deep learning models to production environments and troubleshoot performance issues.

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Recently updated!

February 2026

Assessments

5 assignments

Taught in English

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This course is part of the AI Engineer Associate Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 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. This course offers a deep dive into advanced deep learning concepts and techniques, focusing on both theory and hands-on implementation. Starting with ensemble learning, you will learn techniques like bagging, boosting, and gradient boosting, helping you improve model performance for real-world applications. The course also covers powerful tools like XGBoost, LightGBM, and CatBoost, allowing you to build efficient and accurate models using these state-of-the-art frameworks. You will then venture into neural networks, covering the fundamentals of deep learning, forward propagation, activation functions, loss functions, and backpropagation. You'll also explore optimization techniques such as gradient descent, all while building neural networks using popular frameworks like TensorFlow, Keras, and PyTorch. As the course progresses, you will apply these skills to practical projects, such as image classification with CIFAR-10, and learn how to fine-tune models with transfer learning and handle complex data types like images and sequences. Designed for learners with a basic understanding of machine learning and programming, this course is ideal for those looking to master advanced deep learning techniques. Whether you're an aspiring AI engineer or a data scientist looking to enhance your skills, this course will prepare you for tackling complex real-world deep learning tasks. Familiarity with Python and machine learning fundamentals is recommended, but not required. By the end of the course, you will be able to implement advanced machine learning algorithms, build neural networks using TensorFlow and PyTorch, apply transfer learning techniques, and deploy models into production environments.

In this module, we will explore advanced ensemble learning techniques, such as bagging, boosting, and gradient boosting, to enhance model performance. You’ll also learn how to implement cutting-edge frameworks like XGBoost and LightGBM. Additionally, we’ll address how to handle imbalanced data and apply these methods to real-world datasets, improving model accuracy and fairness.

What's included

7 videos1 reading1 assignment

7 videos•Total 125 minutes
  • Day 1: Introduction to Ensemble Learning•15 minutes
  • Day 2: Bagging and Random Forests•14 minutes
  • Day 3: Boosting and Gradient Boosting•16 minutes
  • Day 4: Introduction to XGBoost•20 minutes
  • Day 5: LightGBM and CatBoost•20 minutes
  • Day 6: Handling Imbalanced Data•17 minutes
  • Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset•23 minutes
1 reading•Total 10 minutes
  • Introduction to the Course 'Deep Learning and Advanced Techniques'•10 minutes
1 assignment•Total 15 minutes
  • Advanced Machine Learning Algorithms - Assessment•15 minutes

In this module, we will lay the foundation for deep learning by covering the essential concepts behind neural networks, including forward propagation, activation functions, and backpropagation. You'll learn how to build, train, and optimize neural networks using both TensorFlow and PyTorch. This section will equip you with the tools to apply deep learning to real-world problems such as image classification.

What's included

7 videos1 assignment

7 videos•Total 136 minutes
  • 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
1 assignment•Total 15 minutes
  • Neural Networks and Deep Learning Fundamentals - Assessment•15 minutes

In this module, we will provide a comprehensive introduction to PyTorch, guiding you through its core concepts and tools. You will learn how to handle tensors, use autograd for backpropagation, and construct neural networks for deep learning tasks. Additionally, we'll dive into advanced techniques like transfer learning, model deployment, and performance optimization, preparing you for real-world deep learning applications.

What's included

18 videos1 reading3 assignments

18 videos•Total 163 minutes
  • Introduction•1 minute
  • Introduction to PyTorch•9 minutes
  • Getting Started with PyTorch•8 minutes
  • Working with Tensors•10 minutes
  • Autograd and Dynamic Computation Graphs•7 minutes
  • Building Simple Neural Networks•10 minutes
  • Loading and Preprocessing Data•10 minutes
  • Model Evaluation and Validation•11 minutes
  • Advanced Neural Network Architectures•11 minutes
  • Transfer Learning and Fine-Tuning•8 minutes
  • Handling Complex Data•9 minutes
  • Model Deployment and Production•9 minutes
  • Debugging and Troubleshooting•10 minutes
  • Distributed Training and Performance Optimization•10 minutes
  • Custom Layers and Loss Functions•10 minutes
  • Research-oriented Techniques•10 minutes
  • Integration with Other Libraries•9 minutes
  • Contributing to PyTorch and Community Engagement•8 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Deep Learning and Advanced Techniques'•10 minutes
3 assignments•Total 90 minutes
  • Full Course Practice Assessment•15 minutes
  • Introduction to Learning PyTorch - Assessment•15 minutes
  • Full Course Assessment•60 minutes

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Frequently asked questions

Deep Learning is a subfield of machine learning that focuses on using neural networks with many layers (also known as deep neural networks) to model complex patterns in large datasets. It is highly relevant today due to its success in tasks such as image and speech recognition, natural language processing, and autonomous systems. Deep learning is transforming industries by enabling powerful AI applications, such as self-driving cars, voice assistants, and medical diagnostics.

The Deep Learning and Advanced Techniques course provides an in-depth understanding of advanced machine learning and deep learning techniques. It covers topics such as ensemble learning, boosting, neural networks, and working with deep learning frameworks like TensorFlow and PyTorch. You will learn how to implement and optimize advanced algorithms such as XGBoost, LightGBM, and CatBoost, and gain practical experience in building and fine-tuning deep neural networks, including handling complex data and deploying models into production.

After completing this course, you will be able to implement advanced machine learning techniques like ensemble learning, boosting, and various neural network architectures using both TensorFlow and PyTorch. You will have the skills to tackle more complex machine learning tasks such as image classification, time-series forecasting, and transfer learning. Additionally, you will be able to optimize models, handle imbalanced data, and deploy machine learning models to real-world applications.

This course is ideal for individuals who already have a foundational understanding of machine learning and are looking to dive deeper into advanced techniques. A basic knowledge of Python programming, machine learning algorithms, and frameworks like TensorFlow or PyTorch will be beneficial. If you’re new to deep learning, it’s recommended to first complete introductory machine learning courses before enrolling in this course to ensure a smooth learning experience.

This course is designed for intermediate learners and professionals who have a foundational understanding of machine learning and want to advance their skills in deep learning and advanced techniques. It is perfect for data scientists, machine learning engineers, and AI enthusiasts who wish to enhance their knowledge and ability to apply advanced machine learning models in real-world projects.

The Deep Learning and Advanced Techniques course is designed to be completed in approximately 9 hours. The pace may vary depending on your prior experience and the amount of time you dedicate to hands-on practice and projects. If needed, you can adjust the pace to ensure thorough understanding of the material.

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.

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.

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.

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.

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

Financial aid available,