All courses
Amit SanganiDeepLearning.AI
Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, about the latest additions to the Llama models 3.1 and 3.2. Discover the new vision capabilities, tool-calling, and Llama Stack, an open-source orchestration layer for building on top of the Llama family of models. Start building exciting applications on Llama!
Britney MullerMaven
In this course, you'll transform AI from a buzzword into your competitive advantage. You'll gain hands-on experience with AI tools and techniques that marketers can apply immediately to improve productivity, streamline campaigns, and create powerful data insights. Every session is designed to help you accomplish real tasks that translate into results, making you a more effective and efficient marketer.
Aurimas GriciūnasMaven
This bootcamp offers a comprehensive journey into AI engineering, focusing on building production-grade AI systems. Participants will learn to design, build, and deploy AI applications using cutting-edge technologies like LLM APIs, vector databases, and AI agent libraries. The course emphasizes practical skills, enabling learners to create a capstone project that demonstrates their ability to solve real-world business challenges.
Krishna SridharDeepLearning.AI
This course provides comprehensive training on deploying AI models on edge devices, reducing latency, enhancing efficiency, and preserving privacy. You will learn key concepts such as neural network graph capture, on-device compilation, and hardware acceleration. The course includes practical exercises like converting pretrained models from PyTorch and TensorFlow, deploying real-time image segmentation models, and integrating models into Android apps. By the end of the course, you will be equipped to develop and deploy AI models on billions of devices, optimizing them for efficient on-device performance.
Jiantao JiaoDeepLearning.AI
In this course, you will learn two critical skills for building applications with LLMs: function-calling and structured data extraction. You will work with NexusRavenV2-13B, an open-source model fine-tuned for these tasks. The course covers forming prompts with function definitions, using LLM responses to call functions, handling multiple function calls, and building applications that process customer service transcripts. These skills will enable you to create advanced AI agents and assistants for various real-world applications.
James GrayMaven
In this comprehensive course, you'll master AI assistants, build AI workflows, and create multi-agent systems to automate tasks and achieve outcomes. Gain hands-on experience with cutting-edge AI tools and learn to integrate AI into your work and business, enhancing your productivity and impact.
Sharon ZhouDeepLearning.AI
Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work. When you complete this course, you will be able to understand when to apply finetuning on LLMs, prepare your data for finetuning, and train and evaluate an LLM on your data. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.
Mike TaylorUdemy
This bootcamp is your one-stop solution to becoming a Prompt Engineer working with cutting-edge AI tools like GPT-4, Stable Diffusion, and GitHub Copilot. You'll learn the strengths and weaknesses of major AI models, recognize the 'Five Principles of Prompting', and apply your knowledge to generate new AI products in 15+ real-world projects. Understand the Python coding patterns and tooling needed to run and scale AI reliably in production, and start working as an AI Engineer.
Amir Feizpour, PhDMaven
In this comprehensive bootcamp, you'll learn to build multi-agent applications using LLM systems. Through a structured 3-sprint approach, you'll design, develop, and deploy an MVP, gaining insights into AI agent workflows, tool integration, and system security. Benefit from 1:1 mentorship, live sessions, and a community of peers to support your learning journey.
Harrison ChaseDeepLearning.AI
In this course, you will learn to build an agent from scratch using Python and an LLM, and then rebuild it using LangGraph. You will explore LangGraph’s components and how to combine them to build flow-based applications. Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. The course also covers implementing persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states. You will incorporate human-in-the-loop into agent systems and develop an agent for essay writing, replicating the workflow of a researcher working on this task.
Cecilia ZinitiMaven
This course will give you practical, immediately useful learnings on actual AI prompting with GC AI or general-purpose tools like ChatGPT or Claude. You'll get a great intro to how AI works, then get your hands dirty with an experienced GC and AI expert to help and push you with tips and tricks. You'll be shocked at the quality of AI legal outputs.
Harrison ChaseDeepLearning.AI
In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. You will learn about models, prompts, parsers, memories for LLMs, chains, question answering over documents, and agents. By the end of the course, you will have a model that can serve as a starting point for your own exploration of diffusion models for your applications.
Marc SunDeepLearning.AI
In this course, you will implement and customize linear quantization from scratch, studying the tradeoff between space and performance. You will build a general-purpose quantizer in PyTorch to quantize any open source model, compressing model weights from 32 bits to 8 bits and even 2 bits. This course provides the foundation to explore more advanced quantization methods.
Pranav RajpurkarDeepLearning.AI
In this Specialization, you will learn to diagnose diseases from x-rays and 3D MRI brain images, predict patient survival rates using tree-based models, estimate treatment effects using data from randomized trials, and automate the labeling of medical datasets using natural language processing. The course covers model interpretation, image segmentation, natural language extraction, and more.
Jacob LeeDeepLearning.AI
This course will show web developers how to expand their toolkits with LangChain.js, a popular JavaScript framework for building with LLMs. It covers useful concepts for creating powerful, context-aware applications. By taking this course, you will learn to use LangChain’s underlying abstractions to build your own JavaScript apps, understand the basics of retrieval augmented generation (RAG), and have the structure of a basic conversational retrieval system that you can use for building your own chatbots.
Younes Bensouda MourriDeepLearning.AI
In the Natural Language Processing Specialization, you will learn to use logistic regression, naïve Bayes, and word vectors for sentiment analysis, analogies, and translations. You will also explore dynamic programming, hidden Markov models, and word embeddings for autocorrect, autocomplete, and part-of-speech tagging. Advanced topics include dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks for sentiment analysis, text generation, named entity recognition, and question identification. Finally, you will delve into encoder-decoder, causal, and self-attention models for machine translation, text summarization, question-answering, and chatbot creation.
