DeepLearning ai offers a variety of short courses designed to improve your skills in generative ai and other ai technologies. These courses are designed to provide students with the right knowledge, tools, and techniques to excel in ai. Below are the most relevant short courses available:
ai/short-courses/red-teaming-llm-applications/”>Red Teaming LLM Applications
This course offers essential guidance to improve the security of LLM applications through red teaming. Participants will learn how to detect and address vulnerabilities within LLM applications, applying cybersecurity methods to the ai domain. By using Giskard's open source library, students will be equipped with the techniques to automate red teaming methods. Basic knowledge of JavaScript is recommended, making this course suitable for beginners eager to contribute to the development of safer ai applications.
ai/short-courses/javascript-rag-web-apps-with-llamaindex/”>JavaScript RAG Web Applications with LlamaIndex
Immerse yourself in the world of creating full-stack interactive web applications that leverage the power of augmented recovery (RAG) capabilities. Through this beginner-level course, you will learn how to build a RAG application in JavaScript, allowing intelligent agents to discern and extract information from various data sources to respond to user queries effectively. With a focus on creating an attractive interface that communicates seamlessly with your data, this course is perfect for those with basic JavaScript skills looking to expand their web development repertoire.
ai/short-courses/efficiently-serving-llms/”>Serving LLMs Efficiently
This intermediate course provides a comprehensive understanding of how to efficiently deploy LLM applications in a production environment. Participants will explore techniques such as KV caching to speed up text generation and delve into the fundamentals of Low Range Adapters (LoRA) and the LoRAX framework inference server. With a prerequisite of intermediate knowledge of Python, this course is designed for those looking to scale their LLM applications effectively, serving a large user base while balancing performance and speed.
ai/short-courses/knowledge-graphs-rag/”>Knowledge Graphs for RAG
Students will gain hands-on experience creating and utilizing knowledge graph systems to power their augmented generation retrieval applications. The course covers using Neo4j's Cypher query language and building knowledge graph queries to provide LLMs with more relevant context. Recommended for those familiar with LangChain, this intermediate course bridges the gap between traditional databases and ai-based query mechanisms.
ai/short-courses/open-source-models-hugging-face/”>Hugging face open source models
Aimed at beginners, this course demystifies building ai applications with open source models and tools from Hugging Face. From filtering models based on specific criteria to writing minimal lines of code for various tasks, students will learn how to leverage the transformer library effectively. Additionally, the course covers how to easily share and run ai applications using Gradio and Hugging Face Spaces, making it ideal for those new to the field of ai.
ai/short-courses/prompt-engineering-with-llama-2/”>Quick engineering with Llama 2
Discover the art of rapid engineering with Meta's Llama 2 models. This beginner course teaches best practices for requesting and selecting between different Llama 2 models, including Chat, Code, and Llama Guard. Participants will explore how to create safe and responsible ai applications, emphasizing the practical use of Llama 2 models in real-world scenarios.
ai/short-courses/building-applications-vector-databases/”>Creating applications with vector databases
This beginner-level course is designed to teach how to develop applications based on vector databases. Covering six different applications, including semantic search and image similarity search, students will learn how to implement them using Pinecone. With basic knowledge of Python, machine learning and LLM, this course offers a practical approach to the exciting possibilities of vector databases.
ai/short-courses/llmops/”>LLMOps
This course presents LLMOps best practices, from designing to automating the process of fine-tuning an LLM for specific tasks and implementing it. Participants will learn how to adapt open source pipelines for supervised tuning, manage model releases, and preprocess data sets. Aimed at beginners with basic knowledge of Python, this course is perfect for those looking to delve deeper into the operational aspects of LLM implementation.
ai/short-courses/automated-testing-llmops/”>Automated testing for LLMOps
This intermediate course focuses on developing automated testing frameworks for LLM applications and introduces continuous integration (CI) pipelines. Participants will learn how LLM-based testing differs from traditional software testing, implementing rule-based and model-scored assessments. Basic knowledge of Python and experience with LLM-based applications are prerequisites, making this course suitable for developers looking to improve their testing strategies.
ai/short-courses/build-llm-apps-with-langchain-js/”>Build LLM Applications with LangChain.js
Expanding on the use of LangChain.js, this intermediate course provides information on how to build powerful, context-sensitive applications. With a focus on orchestrating and chaining together different modules, participants will learn essential data preparation and presentation techniques. Intermediate knowledge of JavaScript is required, making this course ideal for developers looking to enhance their LLM application development skills.
ai/short-courses/reinforcement-learning-from-human-feedback/”>Reinforcement learning from human feedback
ai/short-courses/building-evaluating-advanced-rag/”>Building and testing advanced RAG applications
Get into advanced RAG mastery with this beginner-friendly course. Delve into improving recovery techniques and mastering evaluation metrics to optimize the performance of RAG applications. Students will explore sentence window retrieval and automatic merge retrieval techniques, focusing on evaluating the relevance and veracity of LLM responses through the RAG triad: context relevance, rationale, and response relevance. Designed for those with basic knowledge of Python, this course provides you with the skills to iteratively develop robust RAG systems beyond the baseline.
ai/short-courses/quality-safety-llm-applications/”>Quality and security for LLM applications
This course prioritizes the security and integrity of LLM applications and is designed for beginners with basic knowledge of Python. Participants will learn how to evaluate and improve the security of their LLM applications, focusing on monitoring security measures and identifying potential risks such as hallucinations, jailbreaks, and data leaks. By exploring real-world scenarios, the course prepares you to protect your LLM applications against evolving threats and vulnerabilities, ensuring a secure and reliable ai deployment.
ai/short-courses/vector-databases-embeddings-applications/”>Vector Databases: From Embeddings to Applications
This intermediate course unlocks the potential of vector databases for ai applications, bridging the gap between embeddings and practical real-world applications. Designed for those with a basic understanding of Python and an interest in data structures, students will develop efficient, industry-ready applications. The course covers a broad spectrum of applications, including hybrid and multilingual search, and emphasizes the use of vector databases to develop GenAI applications without requiring extensive training or fine-tuning of LLMs.
ai/short-courses/functions-tools-agents-langchain/”>Features, Tools and Agents with LangChain
Delve into the latest advancements in LLM APIs and learn how to use LangChain Expression Language (LCEL) for faster agent and chain composition. Suitable for people with basic knowledge of Python and familiarity with LLM prompts, this intermediate course offers a practical approach to using LLMs as development tools. Through hands-on exercises, students will understand how to apply these capabilities to create conversational agents, improving their ability to create more sophisticated and interactive ai applications.
Each course is designed with a specific skill level, from beginner to intermediate, ensuring that students can find courses that match their current skills and help them progress. Whether you're looking to build more secure LLM applications, create ai-powered web applications, or dive into vector databases, DeepLearning.ai short courses provide a comprehensive learning path tailored to your needs. For those interested in improving their ai skills quickly and efficiently, these courses offer an excellent opportunity to learn cutting-edge ai technologies.
Hello, my name is Adnan Hassan. I'm a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a double degree from the Indian Institute of technology, Kharagpur. I am passionate about technology and I want to create new products that make a difference.
