// 25 courses indexed
The course directory
Every course we track across 10+ providers, in one searchable index. Want a shortlist picked for your goals? Run a free skill gap analysis.
Deep Learning Foundations & Azure Environments
Production-grade deep learning starts with solid engineering foundations. This course builds the core PyTorch and Azure ML skills needed to build, train, and track deep learning models in enterprise environments. You'll implement feedforward neural networks from scratch, including forward and backward passes, loss functions, and optimizers, then move into custom PyTorch module design, efficient DataLoader pipelines, and mixed precision training with torch.compile. You'll configure Azure ML workspaces, compute clusters, and GPU targets using the Azure ML SDK v2, manage training jobs, and track
All levels
Advanced Machine Learning and its Applications
The third course introduces advanced machine learning techniques and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of machine learning, and Course 1, which developed LLM-empowered Python programming skills for AI. In this course, advanced data preprocessing, machine learning outcome evaluation, neural network design and optimization, deep learning, and generative artificial intelligence are introduced. Since these techniques underpin essential modern AI systems in science and engineering, after completing this course, y
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Deep Learning with PyTorch
This course offers a comprehensive and practical introduction to deep learning using PyTorch, a leading open-source framework. Learners will develop a solid understanding of foundational concepts such as neural networks, activation functions, forward and backward propagation, and optimization algorithms. Through a structured progression, the course covers essential architectures including perceptrons, multi-layer networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Transformers. Learners will apply these models to real-wor
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Optimize TensorFlow Models For Deployment with TensorRT
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput. Prerequisites: In order to successfully complete this project, you should be competent in Python programming, understand deep learning and what inference is, and
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Deep Learning for Business
Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open sour
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Foundations of Artificial Intelligence
This course introduces learners to the core principles of artificial intelligence, including its history, definitions, and the role of data in AI. You’ll explore algorithms, specialized hardware, and delve into machine learning and deep learning fundamentals. Ethical considerations around AI, especially generative AI (GenAI), will also be discussed, ensuring you understand the broader impact AI has on society and the workforce. With clear explanations and real-world examples, the course ensures you not only learn theoretical concepts but also understand their practical implications. You’
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Generative AI in Software Development
The course provides a comprehensive exploration of how generative AI is reshaping software development by accelerating coding, improving debugging, and enhancing automation. It is designed for aspiring software engineers, developers, and professionals who want to integrate AI into modern development workflows to build efficient, scalable, and error-free applications. You will explore the role of large language models (LLMs) like GPT, Gemini, and LLaMA in coding tasks, software testing, and project automation. The course begins with foundational AI concepts—machine learning, deep learning, and
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Introduction to Embedded Machine Learning
Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine
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Foundations of LLMs and Deep Learning for Text Analysis
This course introduces the foundational concepts of large language models (LLMs) and deep learning techniques for text analysis, a critical skill set in today’s AI-driven landscape. As organizations increasingly rely on intelligent systems to process and interpret language data, understanding these technologies has become essential for modern professionals. Throughout the course, learners will explore how deep learning models analyze and extract meaning from textual data, gaining practical insights into real-world NLP applications. By studying the architecture and working principles of transf
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Foundations and Core Concepts of PyTorch
Updated in May 2025. This course now 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. In this comprehensive course, you'll embark on a journey through the foundational elements and core concepts of PyTorch, one of the most popular deep learning frameworks. Starting with a detailed overview and system setup, you'll be guided through installing and configuring your environment to ensure a smooth learning experience. The course t
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Fundamentals of Natural Language Processing
The field of natural language processing (NLP) aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the 3 pillars underlying modern NLP: probabilistic language models, simple neural networks with a focus on gradient based learning, and vector-based meaning representations in the form of word embeddings. At the end of the course, students will be able to implement and analyze probabilistic language models based on N-grams, text classifiers using logistic regression and gradient-based learning, and vector-based approaches to wo
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Deep Learning: Advanced Backbones and Efficient GPU Training
Master advanced deep learning architectures and efficient training techniques using PyTorch Lightning, timm, ConvNeXt, Vision Transformers, RoPE, SwiGLU, RMSNorm, and Weights & Biases. This course equips you to design, train, and benchmark modern backbones on limited GPU hardware for real-world production use. Module 1 introduces modern backbone architectures, tracing the evolution from ResNets to ConvNeXt and Vision Transformers, covering patch embeddings, multi-head self-attention, and position encodings. Module 2 dives into training dynamics and stabilization techniques including RMSNorm,
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Automate Financial Analysis with AI Pipelines
Automate Financial Analysis with AI Pipelines is an intermediate-level course designed for finance and data professionals who want to integrate artificial intelligence into their analysis workflows. You’ll start by evaluating multiple AI models—such as Random Forest, XGBoost, and Neural Networks—for credit-risk classification using real financial datasets. Then, you’ll design an automated pipeline that retrieves SEC filings, retrains models, and updates dashboards with no manual intervention. Through readings, videos, and hands-on labs, you’ll gain the practical skills to compare model perfor
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PyTorch: Fundamentals
This course introduces you to the core principles of deep learning through hands-on coding in PyTorch. You’ll start by learning how PyTorch represents data with tensors and how datasets and data loaders fit into the training process. Step by step, you’ll build and train neural networks, experiment with different architectures, and explore how models learn from examples. You’ll also learn how to monitor training progress, interpret results, and evaluate performance. By the end of the course, you’ll understand PyTorch’s workflow and be ready to design, train, and test your own neural network
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Clinical Decision Support Systems
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
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Create Image Captioning Models
This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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Applied Generative AI & Prompt Engineering Mastery
Everyone talks about AI. Few actually understand it. Most people copy prompts from Google, get inconsistent results, and quietly wonder if they are falling behind. This course closes that gap for good. Here is what you will cover: Generative AI Foundations Understand how Generative AI differs from Machine Learning and Deep Learning so you can confidently follow modern AI conversations and trends. Explore ChatGPT features, the evolution of GenAI, and tools for text, image, audio, video, and code generation. Learn how newer AI systems are becoming more capable and interactive. LLMs, Use
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AI for Knowledge Workers
If you’re new to AI or a beginner looking to increase your use of AI in knowledge work, this class is for you! It is designed for anyone interested in how AI works and the many ways it is transforming the way we work and create. From business professionals to creatives to leaders, this course will give you the tools to understand and harness the power of Generative AI for both creative and analytical work. We’ll discuss how Machine Learning and Deep Learning are essential to AI and we’ll review how neural networks are trained. You’ll learn how to select the right AI tools, and review real-worl
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Deep Learning and Modern AI Architectures
Build practical deep learning skills that help you design, train, troubleshoot, and improve modern neural network models for vision, sequence, and generative tasks. In this course, you’ll develop hands-on experience used in roles such as machine learning engineer, deep learning engineer, AI engineer, data scientist, and applied scientist. You’ll work with feedforward neural networks, convolutional neural networks, transfer learning, and model optimization techniques, while building a stronger understanding of how modern architectures are applied to real machine learning problems. This is a no
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Introduction to Computer Vision
Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation. Additionally, you will learn the creation and impact of AI-generated images and videos, exploring the ethical consideration
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Introduction to RNN and DNN
Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN archit
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Deep Learning Specialization
Five courses on neural networks, CNNs, sequence models, and ML strategy from DeepLearning.AI.
Intermediate