This is one of the DeepLearning.AI short courses, and it does the thing short courses should do, which is get you building quickly without pretending to be a full curriculum. Harrison Chase, who created LangChain, walks through the core building blocks in notebooks alongside Andrew Ng, and you cover models, prompts, and output parsers, then memory, then chains, then the pattern most people actually want, which is asking questions over your own documents using embeddings and a vector store, and finally a first taste of agents. Because the person teaching it is the person who wrote the library, the explanations land with a confidence you do not get from a third party guessing at intent, and the notebook format means there is almost no gap between hearing an idea and running it. For free, in an afternoon, it is a genuinely good on-ramp.
The catch is the one that haunts every framework course, which is pace of change. LangChain has iterated hard since this was recorded, and some of the exact imports and calls in the videos no longer match the current library, so you will hit moments where the code on screen is not quite the code you should write today. It is also short by design, so memory, chains, and agents each get a quick pass rather than real depth, and the genuinely hard parts of shipping an LLM app, cost control, evaluation, handling the model when it goes off the rails, are simply out of scope. I still recommend it as a starting point, with one rule.
Use it to build the mental model of how the pieces fit, then keep the official LangChain documentation open beside it and trust the docs over the video whenever they disagree, because by the time you watch this they probably will.