My Top Picks: 5 Free NLP Courses I’d Recommend for 2025

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Have you ever asked Siri a question, used Google Translate, or noticed how spam emails magically disappear from your inbox? That’s Natural Language Processing in action. It’s the technology that allows computers to understand, interpret, and even generate human language—just like we do. Even if you’re not a techie, I’m sure you’ve heard about the hype around Large Language Models. With their rise, NLP has become one of the hottest fields in AI right now. Plus, its a solid career choice.
 

NLP Engineers in the U.S. earn an average salary of around $157,000 per year, showing just how valuable these skills are.

I know what you’re probably thinking: “This sounds awesome, but where do I even start?” With so many courses and resources out there, it can be hard to know which ones are worth your time. That’s exactly why I’m here! In this article, I’ll share my personal, no-nonsense picks for learning NLP in 2025. You’ll find a mix of classic and new resources in this list, but trust me—you won’t need to look elsewhere. And the best part? You can start right now without spending a dime, thanks to free courses from some of the top experts in the field. Let’s jump right in!

1. CS224N: Stanford’s Natural Language Processing with Deep Learning

 
Link: Youtube Playlist – Stanford’s CS224N
Here’s my all-time classic favorite! While different versions of this course are taught by various professors, my personal top pick is the one by Christopher Manning, Director of the Stanford Artificial Intelligence Laboratory (SAIL) and Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He’s also the founder of the Stanford NLP group.

This version consists of 19 lectures that present content ranging from the bare basics of neural networks, RNNs, and LSTMs to more advanced topics including Seq2Seq models, transformers, language models, linguistics, and Reinforcement Learning with Human Feedback (RLHF). Along the way, Prof. Manning also makes a decent review of general deep learning concepts, including gradient descent, computation graphs, and the backpropagation algorithm. Also, the course provides access to past student projects which inspires your experiments. Check them out here. It’s completely free on YouTube-so it’s an amazing resource to learn from one of the top experts in the field without spending anything.

2. Coursera: Natural Language Processing Specialization

 
Link: DeepLearning.AI NLP Specialization
This specialization on Coursera is a great way to build a strong foundation in NLP and is perfect for those who prefer a structured learning approach. The program consists of four courses, each designed to introduce different aspects of NLP and its applications. It is taught by experts in the field, including Younes Bensouda Mourri (AI Instructor at Stanford), Lukas Kaizer (Researcher at OpenAI and contributor to Tensor2Tensor, Trax, and the Transformer paper), and Eddy Shyu (AI Product Manager at Cisco).

This course is, of course, a paid one, but one can audit it completely free if there is no need for assignments or certificates. This makes it great for learners with a bit of machine learning, deep learning, and Python background. If you are a newbie, it may seem a little advanced but it is an excellent way to learn about the core concepts of NLP. The lessons are very brief and to the point, so you will find your way through them easily; however, do remember that the assignments are TensorFlow-based, and that might be like a dark alley for someone who does not know it well

3. Hugging Face NLP Course

 
Link: NLP Course by HF
The Hugging Face NLP Course is a fantastic resource for learning practical NLP using libraries from the Hugging Face ecosystem, such as 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate, along with the Hugging Face Hub. This course is hands-on, filled with code examples, and focuses on tools and techniques used in real-world production environments.

If you’re already familiar with the theory behind NLP and want to get practical experience, this course is an excellent choice. It’s structured into 12 chapters, divided into three parts:

• Part 1: Introduction to Hugging Face Transformers library (Chp 1-4): You’ll learn about transformer models, their architecture, and how to use models from the Hugging Face Hub. It also covers accessing datasets, selecting appropriate models, fine-tuning them, and publishing your models to the Hub for others to use.
• Part 2: Basics of Datasets and Tokenizers (Chp 5-8): It teaches you how to preprocess datasets into suitable formats for various NLP tasks. Additionally, you’ll learn how to train a tokenizer from scratch, which is especially useful when a pre-trained tokenizer for your specific language is unavailable.
• Part 3: Beyond Basic NLP and Optimizing for Production Environments: The final part explores advanced applications of transformer models, such as solving speech recognition and computer vision problems. You’ll also learn how to prepare your models for deployment, making them production-ready.

This course is highly practical and focused on helping you build the skills needed for real-world applications. Whether you want to fine-tune pre-trained models, create custom tokenizers, or prepare your models for production, the Hugging Face NLP Course has you covered.

4. Advanced Natural Language Processing by Mohit Iyyer

 
Link: CS 685, Spring 2024, UMass Amherst
If you’re interested in advanced NLP and large language models, this course by Mohit Iyyer is a must-watch! Mohit Iyyer is an Associate Professor in Computer Science at UMass Amherst and a key member of UMass NLP. His research focuses on improving instruction-following abilities of large language models, building collaborative human-LLM systems, and designing methods to evaluate long-form multilingual text. The course is updated regularly with the latest NLP research, ensuring that students always have access to current content and advanced techniques. What stands out in Mohit’s teaching style is the clarity with which complex topics are explained, along with his practical approach to hands-on assignments. You’ll be exposed to topics like:

• Tokenization & Efficient Fine-tuning: Learn about tokenization techniques (like T5) and parameter-efficient adaptation methods, such as prompt tuning and LoRA, to optimize large models with minimal retraining.
• RLHF, RLAIF & DPO: Explore Reinforcement Learning with Human Feedback (RLHF), AI-based feedback (RLAIF), and Direct Preference Optimization (DPO) for improving model performance by adjusting to human or AI preferences.
• Decoding Techniques & Prompt Engineering: Understand decoding methods (e.g., nucleus sampling, RankGen) and master prompt engineering along with Retrieval-Augmented Generation (RAG) to improve text generation accuracy and relevance.
• Model Evaluation & Scaling: Study text generation evaluation methods (like BLEURT, FactScore) and scaling laws for LLMs, focusing on computational efficiency and training considerations at large scales.
• Vision-Language Models & In-Context Learning: Explore Vision-Language Models (like CLIP) and in-context learning, exploring how models can integrate visual and textual information.
• LLM Security & Interpretability: Address security challenges, such as LLM detection and watermarking, and learn techniques for model interpretability, probing, and knowledge manipulation.

If you’re passionate about exploring the latest innovations and enjoy hands-on assignments, this course is a perfect choice for you.

5. CMU Advanced NLP Course by Graham Neubig

 
Link: YouTube Playlist – Advanced NLP Fall 2024
Course Site: Advanced NLP Fall 2024
Graham Neubig is an Associate Professor at Carnegie Mellon University’s Language Technology Institute and the Chief Scientist at All Hands AI. His research focuses on using NLP technologies to bridge communication gaps in human-human and human-machine interactions. It integrates foundational topics, such as syntactic, semantic, and discourse analysis, with emerging trends in the field, including retrieval-augmented generation and addressing fairness and bias in NLP models. One of the key highlights is the hands-on assignments, like developing a minimalist version of LLaMA2, providing valuable insights into the workings of large language models. The course also emphasizes the importance of research methodologies, offering access to extensive reading materials that reflect the latest advancements in NLP. With a focus on both core topics and emerging trends, this course provides a well-rounded experience for learners looking to make meaningful contributions to NLP research and applications.

Bonus Resource: Umar Jamil

 
Link: https://www.youtube.com/@umarjamilai
While this list is about NLP courses, I can’t help but recommend Umar Jamil’s content as a must-follow resource. Umar Jamil is a Machine Learning Engineer based in Milan, Italy, who creates some of the most detailed and beginner-friendly video tutorials on machine learning and AI topics. His videos are particularly valuable for those who want to dive deep into the inner workings of popular AI architectures, algorithms, and mathematical concepts. He doesn’t just talk theory—he codes everything from scratch, providing a hands-on learning experience. His videos are an absolute goldmine for AI community.

Here are a few standout tutorials from his channel:

• Flash Attention: Derived and coded from first principles using Triton in Python.
• Multimodal Vision-Language Models: A complete walkthrough on building these models in PyTorch.
• Low-Rank Adaptation of LLMs (LoRA): Explained visually with PyTorch code.
• Distributed Training with PyTorch: A complete tutorial on setting up cloud infrastructure for large-scale training.
• Building LLaMA 2 from Scratch: Full coding and a look into the workings of this powerful language model.

And there you go! These free courses are perfect for anyone looking to explore NLP in 2025. So, grab your laptop, get comfy, and start your learning journey. Who knows? You might just discover your next passion in NLP : )
 
 

Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.

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