Online Course – Certified Professional Internship in Ultimate Firepower 2024 by Packt Institute

Learn deep with PyTorch in a comprehensive and practical course. Develop, deploy, and innovate with models in regression models, convolutional neural networks, GANs, NLP, recommender systems, transformers, and more.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Beginners Intermediate level Advanced involved

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Recurrent Neural Networks (RNN)
  • Generative Affinity Networks (GAN)
  • Pytorch (machine learning library)
  • Convolutional Neural Networks (CNN)
  • Natural Language Processing (NLP)

What you will learn in the course

Courses for which the course is suitable

  • Artificial Intelligence Developer
  • Data Scientist
  • Machine Learning Engineer
  • Software developer specializing in PyTorch
  • Deep Learning Expert
  • Data Analyst
  • Data Engineer
  • Researcher in the field of artificial intelligence

Internship – 3-part course series

Embark on a transformative learning experience with our PyTorch Ultimate 2024 course. Start with a strong foundation, understand the key topics and objectives, and move seamlessly through the fundamentals of machine learning and deep learning principles. From setting up your environment to mastering tensors and neural networks, each section is carefully crafted to develop your expertise.

What does the course include?

  • Fundamentals of Computational Learning
  • Principles of deep learning
  • Setting the environment
  • Mastering tensors and neural networks

Advanced modules delve into PyTorch simulation, CNNs, RNNs, GANs, and more, ensuring you’re at the forefront of the rapidly evolving field of AI. With hands-on code exercises and real-world applications, this course is your gateway to becoming a PyTorch expert.

Who is the course for?

  • Technology professionals
  • Data scientists
  • Artificial intelligence enthusiasts

It is recommended that you have prior experience with Python and the basics of machine learning. By the end of the course, you will be equipped with the skills to tackle complex AI projects and leverage PyTorch for innovative solutions.

Hands-on Learning Project

The included projects offer hands-on experience, where learners apply their skills to real-world problems. These projects range from:

  • Building neural networks from scratch
  • Developing complex models for image, voice, and object recognition tasks

By engaging in these projects, learners will solve real-world problems and improve their ability to implement powerful deep learning solutions in a variety of practical scenarios.

Details of the courses that make up the specialization

Basics and terms of Pyotorch

Course 1 • 6 hours

Course Details

What will you learn?

  • Setting up and configuring a PyTorch environment.
  • Understand basic concepts in AI and machine learning.
  • Build, train, and evaluate neural networks from the ground up, using various optimization techniques.
  • Apply Pytorch to practical deep learning tasks.

Skills to Acquire

  • Category: Deep Learning
  • Category: Machine Learning
  • Category: Pytorch (machine learning library)
  • Category: Neural Network

Building and training neural networks with Pytorch

Course 2 • 7 hours

Course Details

What will you learn?

  • Build and train neural networks with Pytorch for various tasks.
  • Implement classification models with multi-category and multi-label datasets, and CNNs for image and sound classification.
  • Use object detection techniques with the YOLO algorithm.
  • Explore neural style transfer, transfer learning, and implement RNNs and LSTM networks.

Skills to Acquire

  • Category: Recurrent Neural Network (RNN)
  • Category: Pytorch (machine learning library)
  • Category: CNN
  • Category: YOLO
  • Category: Classification Models

Advanced Pyotorch techniques and applications

Course 3 • 11 hours

Course Details

What will you learn?

  • Create and evaluate machine learning models for specific datasets, evaluating performance with appropriate metrics.
  • Designing autoencoders for dimensionality reduction and creating GANs for data visualization, while analyzing quality.
  • Developing graph neural networks for graph data and implementing Transformers, including Vision Transformers.
  • Upgrading models with semi-supervised learning using limited data, and deploying them with Flask on Google Cloud.

Skills to Acquire

  • Category: Transformers
  • Category: Auto-encoders
  • Category: Recommendation Systems
  • Category: Flashlight Lightning
  • Category: GANs