Natural Language Processing
Learn the core NLP building blocks powering search engines like Google or voice assistants like Siri or Amazon Alexa. We will develop an understanding of core NLP components — word vectors, intent classification, entity recognition and many more using transformer architectures like BERT and GPT — while building projects like embedding-based retrieval and smart-compose.
We taught over 5000 learners from these companies:
Course taught by expert instructors
Applied Science Manager @ Amazon
Ankit Chadha is currently an Engineering Leader at Amazon, where he leads a team to build Deep Learning techniques that power Alexa's Web-scale Open Domain Question Answering experiences. Prior to Amazon, Ankit led an NLP team working on state-of-the-art language understanding, NER, and sentiment analysis for Salesforce's Einstein portfolio of tools.
A course you'll actually complete. AI-powered learning that drives results.
Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.
Live courses by leading experts
Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.
Practice on the cutting edge
Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.
Flexible schedule for busy professionals
We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.
Each course comes with a certificate for learners to add to their resume.
15-20x engagement compared to async courses
Support & accountability
You are never alone, we provide support throughout the course.
Learn and apply skills with real-world projects.
Software Engineers looking to learn more or work in machine learning fields
Data science and machine learning practitioners looking for practical learning in NLP to support their current work, including those completely new to NLP
- or anyone curious about NLP
Writing Python and an ability to find and read documentation of different libraries.
Basics of machine learning is recommended
Note: NLP experience is NOT required.
A emotion classification system that can be used as part of chatbot systems.
- PyTorch Lightning
- Intro to Deep Learning
- Word Vectors
- Multilayer perceptron (MLP)
A system to locate and classify named entities such as organizations, names, locations (i.e. TikTok, Lebron James, London), mentioned in unstructured text.
- Recurrent Neural Network (RNNs)
- Long Short-Term Memory (LSTMs)
- Named-entity Recognition (NER)
An embedding-based search system that can search millions of documents in a few milliseconds. We'll use this to de-duplicate any questions that are already asked on Quora.
- Sentence Vectors
- Siamese Networks
- Bidirectional Encoder Representations from Transformers (BERT)
Gmail like smart-compose system that can assist with typing by suggesting the next few words in realtime. Also, we use Co:here, a short introduction on ready-made NLP models through an easy to use API.
- Subword Tokenizers
- Generative Pre-trained Transformer (GPT)
Course success stories
Learn together and share experiences with other industry professionals
It is hard to believe how much I have learned in the span of a month. I was able to take my theoretical knowledge and convolve it with applied experience through the Uplimit course. I feel more confident in building my own deep learning architecture, and appreciate the reference to the material through the course and access to mentors. The Uplimit course takes you to the cutting edge of knowledge. Highly recommend.
Absolutely stoked to share this! Over the last month I had the pleasure of learning from some of the great minds coming out of Facebook, Google, and other enterprises doing bleeding edge work in Machine Learning and Natural Language Processing. Being part of a select cohort spanning three continents I was able to hear about initiatives and research from AI/ML professionals around the globe. NLP is a fast emerging sector that is changing the landscape of software everywhere you look. I'm so honored to be part of this experience and am taking away so many great connections and insights in a field that I really love.
The co:rise NLP course has been the most efficient way I've ever learned new skills. Having access to talented instructors and peers made the learning process both quicker and richer.
You will go from zero to MLhero with this course. A lot of concepts clicked for me with this training, we went beyond the classic reading and blog post and got really into the weeds and ins-and-outs of the different topics.
This course is a great way to familiarize yourself with state-of-the-art NLP techniques. You will go from building skip-gram models to classify emotions to text-prediction with transformers in 4 weeks.
I really enjoyed the course and the community around it. I think it was a great way to not only learn about NLP, but also connect with other engineers in the field.
An exceptional opportunity for motivated students. You will gain broad awareness and practical experience using state-of-the-art NLP techniques that can get you up to speed in this fast-moving field. The mentorship available to students is unparalleled.
I really appreciated the NLP course. I feel like every aspect of the course, from the work Judy did creating community, managing slack, and making sure we were all taken care of, to the instruction and support from Sourabh and Kaushik. I learned so much in this course, and had a great time learning from my fellow participants as well. I've finished this course feeling like I have a solid footing to move forward with NLP applications in the future. Thanks!
This course gives you a great overview of modern NLP techniques in just 4 weeks. But more importantly, you learn from people who use it in very big real life projects and have a change to get knowledge that wouldn't be available in a blog post