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Machine Learning Engineering

AI Course: NLP/LLM

US Online

The NLP & LLM short course is designed for data scientists and software engineers who want to become specialized in Natural Language Processing. It covers both traditional NLP methods as well as state of the art Large Language Models(LLM). If you want to learn how to fine-tune generative AI and LLM to build exciting applications, this will be the perfect course for you.

Course highlights

  • SOTA LLM techniques
  • LLM Fine-tuning
  • 6+ NLP Use Cases
  • Building generative AI applications
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Part-time
Online Live
6 weeks
60 hours
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About the Course

This course can be taken as a part of WeCloudData’s Machine Learning Engineering Bootcamp. It helps you build practical skills in NLP and LLM and applying them for building AI applications. It focuses on case-based learning and project building.

  • What you will learn
    • Text/Unstructured data processing
    • Classical NLP methods
    • Fine-tuning Large Language Models
    • Building intelligent chatbots using Langchain
    • 6+ ML NLP industry use cases
    • Build and deploy LLM applications
  • Case-based learning with real-life examples
    • Text Classification
    • Sentiment Analysis
    • Translation
    • Text Summarization
    • Question Answering
    • Agent & Chatbot (with Langchain)
    • Knowledge Search

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I can already tell Indrani is an incredible teacher.  She’s very knowledgeable. Her teaching style makes the material more memorable.  She asks questions which forces me to reread over my previous notes. Instructor Yi instills a lot of confidence in his knowledgeability. You can tell from his cadence and confidence that he knows what he’s teaching very well.

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CURRICULUM

Be ready for the new economy

WeCloudData programs are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends. 

MODULE 1
Traditional NLP Methods
This module teaches learners the basic text processing skills and traditional NLP methods. While generative AI and LLMs have created so much excitement at the moment, it's still very important for learners who want to get specialized in NLP to have thorough understanding of the fundamental NLP concepts such as text processing, feature engineering, and traditional language models. We will cover practical techniques to help learners build up NLP fundamentals and get ready for working with large language models.
LEARNING OUTCOMES

Text Processing Basics

  • Working with text files
  • Word Tokenization, Stopwords, Stemming & lemmatization
  • Regular expression
  • Text featurization

Traditional NLP Methods

  • Bag of words
  • Naive Bayes
  • TF-IDF
  • Topic modeling (LSA, LDA)
KEY SKILLS
  • Text Pre-processing
  • Gensim
  • spaCy
  • NLTK
  • Topic Modeling
  • Text Clustering
  • Text Classification
  • Text Vectorization
  • Text Feature Engineering
  • Regular Expression
  • Text Cleaning
  • BoW
  • Naive Bayes
  • LDA
Tools:
Python
PyTorch
gensim
spaCy
Regex
Tools:
Python
PyTorch
HuggingFace
BERT
MODULE 2
Sequence Models
This module introduces the sequence to sequence model and its use cases to the learners. We will begin with the classic recurrent neural networks (RNN) such as GRU and LSTM, and then introduce seq2seq with attention mechanism and transformers. This module paves the way for large language models (LLMs) and generative AI.
LEARNING OUTCOMES
  • Sequence to Sequence model basics
  • Recurrent neural networks (RNN, GRU, LSTM)
  • Seq2seq models with attention mechanism
  • Transformers
KEY SKILLS
  • Sequence Models
  • BERT
  • Transformer
  • Huggingface
  • Attention Models
  • LSTM
MODULE 3
Generative AI Introduction
This short module introduces students to the basics of generative AI. Students will learn different types of generative AI techniques, the motivation for pre-trained large foundation models, and generative AI application and use cases in NLP and Computer Vision. Students will be able to build up the foundational knowledge for LLM model fine-tuning in the next module.
LEARNING OUTCOMES
  • Large foundation models
  • Generative language models
  • Types of Generative AI models
  • Generative AI applications and use cases
KEY SKILLS
  • Generative AI
  • Foundation Models
  • Transformers
  • Generative Models
Tools:
PyTorch
Huggingface
Tools:
PyTorch
Huggingface
BERT
GPT
Llama
Falcon
MODULE 4
Large Language Models (LLMs)
This module introduces learners to Large Language Models. Learners will get familiar with different types of LLMs including encoder only models, decoder-only models, and encoder/decoder models. Specifically, we will cover the current SOTA LLMs including BERT, T5, GPT (3/4), Falcon, and Llama-2.
LEARNING OUTCOMES
  • Encoder models
  • Decoder models
  • Encoder/Decoder models
  • LLM Models: BERT, T5, GPT-3, Falcon, Llama-2
KEY SKILLS
  • Large Language Model
  • LLM
  • Generative Models
  • Falcon LLM
  • GPT-3
  • GPT-4
  • Llama-2 LLM
  • Open Source LLM
MODULE 5
Fine-tuning LLMs
This module teaches students the practical skills applied to fine-tune LLMs. Students will learn the difference between prompting and fine-tuning, use PyTorch and Huggingface to fine-tune LLMs and tailor it to task-specific scenarios, and learn 3 different parameter-based fine-tuning methods and understand when to choose each one. After this module, students will be ready to complete an end-to-end LLM fine-tuning projects.
LEARNING OUTCOMES

Different Fine-tuning Methods

  • Feature-based fine-tuning
  • Freeze pre-trained layer, update output layers
  • Full fine-tuning, update all layers

Parameter Efficient Fine-tuning (PEFT)

  • Prompt tuning (soft vs hard)
  • Prefix adaptors (Llama-adaptors)
  • Low-rank adaptation (LoRA)
KEY SKILLS
  • LLM Fine-tunings
  • Adaptors
  • LoRA
  • Supervised Fine-Tuning (SFT)
  • Parameter Efficient Fine-Tuning (PEFT)
  • DeepSpeed
  • Fairscale
  • Huggingface SFT
Tools:
PyTorch
Lightning
Huggingface
Tools:
Huggingface
LangChain
Elastic
Chroma
Milvus
MODULE 6
NLP Use Cases
This module introduces learners to various NLP use cases. Built on top of the LLM fundamentals, learners to learn how to implement some of the most popular NLP use cases such as sentiment analyiss, text summarization, question answering, and LLM-powered chatbots.
LEARNING OUTCOMES

NLP/LLM Use Cases

  • Text Classification
  • Sentiment Analysis
  • Translation
  • Text Summarization
  • Question Answering
  • NER
  • Agent & Chatbot (with Langchain)
  • Knowledge Search

Practice Guide for building NLP applications

  • LangChain
  • Vector database (pgvector, Milvus, Chroma, Faiss)
KEY SKILLS
  • Vector Database
  • Milvus
  • Chroma
  • Elasticsearch
  • Faiss
  • Chatbot
  • LangChain
FACULTY TEAM

Learn from the best

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

Indrani Gorti
Director of Platform and ML engineering, Loblaw Digital
Indrani Gorti is a polyglot with experience in data engineering, data science, and solving business problems using ML and AI. She has worked in major industries like Finance, Telecommunications, Technology, Retail, and Startups. Her work has involved end-to-end projects from data ingestion, data analysis to the deployment of models in various setups using user transaction and interaction logs. Indrani is a Director of Platform and ML engineering at Loblaw Digital. She holds a BS and MS in Computer Science.
Yi Zhang
Senior AI Applied Research Scientist at ServiceNow ATG
Dr. Yi Zhang is a Computer Scientist specializing in NLP, Machine Learning, and AI, working as a Senior AI Applied Research Scientist at ServiceNow Advanced Technology Group. He has years of academia and industry experience and has architected solutions to convert the latest technology to robust Business Intelligent. Prior to his previous experience as the Head of Engineering at NLPMatics, he also assumed team lead role at Tencent and Research Scientist at Chan Zuckerberg Institute.
Vanessa Feng
Software Engineer, Machine Learning @ Meta
Vanessa earned her Ph.D. in Computer Science from University of Toronto, with a research focus on Natural Language Processing. Throughout her career, Vanessa accumulated extensive knowledge and industrial experience with applying Data Science, Machine Learning, and Natural Language Processing in various domains, ranging from Online Recruiting, Media, E-commerce, to Integrity. In her recent role at Meta, she is focused on leveraging advanced ML techniques to identify malicious behaviors in order to safeguard the Facebook Marketplace platform.

Project

Portfolio Experience Building

Building portfolio projects that help you make a difference in the job market. Here’s what you will experience:

  • Pick an industry to focus on
  • Research NLP/LLM use cases for a specific use case
  • Write a project proposal
  • Find or collect datasets for the project
  • Clean, synthesize, and augment the text data
  • Fine-tune or train your NLP models
  • Deploy an NLP application using Gradio or Streamlit
  • Code review with your learning mentor
  • Present & publish your portfolio project
SCHEDULE

Upcoming Start Dates

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AI Course: NLP/LLM
$3,800 USD
  • Case-based learning
  • Portfolio project mentoring
  • Flexible payment plan
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Recommended Short Courses
$2,700 - $5,200 USD
  • Learn advanced skills to stand out
  • Get alumni discount for the AI, MLOps
  • Short courses to consider after completing this course ⇩
Upgrade to Bootcamp
$7,500 USD
  • Upgrade to the AI/MLE bootcamp and save $5,000
  • Get extensive 1-1 career mentoring and job support
  • Get the flexibility to create your own bootcamp
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Jason Lee, Alumni

Thank you so much for coordinating an awesome course. The assistant instructor was really great in exposing to us how course material is applied in production level environment. I also like the instructor’s approach of pushing us to build from fundamental to real project using PyTorch first. And then progressing to Tensorflow. Personally, I think it’s a super awesome course, but I believe you really have to dig yourself into the contents and dedicate many head banging hours. But course material is very practical both in theory and application. Thanks much!

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Waqas Khan, Senior Data Scientist

All the Instructors and TA’s are very knowledgeable and are always available for any clarification or support. There are dedicated TA office hours daily to assist students if there are any roadblocks in their assignments. Students are generally from very different backgrounds and experience levels but the Instructors and TA’s do a great job to make sure that everyone is following along and is on the same page.

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FAQ

Frequently asked questions about the bootcamp
To be successful in this course the learner will need to have intermediate level python programming skills and know how to work with machine learning and deep learning libraries such as scikit-learn and PyTorch or Tensorflow. Understanding of neural networks and deep learning basics is also helpful. If you don’t meet the requirements, we recommend you take WeCloudData’s applied machine learning course or AI fundamentals course.
NLP knowledge is not necessary but definitely helpful. We highly recommend learners pick up some basic text processing skills before attending this course.
Yes. Understanding the basics of neural networks and deep learning will be helpful. This course teaches advanced skills such as LLM fine tunings and building your own mini-GPT models. So you need to understand how to work with PyTorch and training neural networks.
This course is very practical and focuses on implementation. We will cover enough theory so learners know how to fine-tune and implement LLMs. There’re quite a bit theory and students will need to be comfortable with literature review. But we will leave the more theoretical components for self-paced learning so students interested in theories can dive deeper into those topics
We don’t offer extensive job support for short courses. If you need career mentorship and help, you have two options. You can either enrol in the career mentorship program with alumni discount or consider joining our machine learning bootcamp. You will be able to get a $2,000 – $5,000 scholarship for the Bootcamp and fully take advantage of the career support.
Depending on your existing skill sets and experience with machine learning, learners usually spend 15 to 20 hours each week (including the lectures and labs)
Yes, during the regular weeks we have office hours and labs sheets students get to follow labs and ask questions. During the project weeks, students will join the project mentoring sessions to interact the project mentors.
The project mentors will teach students the blueprint of building an NLP/LLM project. Students are encouraged to have their own ideas and project use cases. Project can be fairly advanced depending on the time commitment.
Labs are designed to help learners practice what’s taught during the lectures. Instructors will be hands-on demos and cover new topics. Any questions regarding the lectures, demos will be addressed in the lab sessions. Students will be given additional lab exercises and self-paced exercises to work on. Lab instructors will provide live solution walkthroughs and students are encouraged to follow along and ask lots of questions. If the students have additional questions outside of the class, we encourage you to reach out to teaching assistants on Slack or attend the office hours.
This course is designed to be very hands-on. It’s impossible to become good at AI without actually trying different methods and LLMs and building things. If you prefer a more academic environment, we recommend you consider a Master’s program. If you want to gain practical experience and build portfolio projects, this is the right course for you.
We cover various tools so students know how to deploy a LLM solution. The focus will still be on the data preparation, model training, and tuning. During the projects, if students want to deploy LLM models, we cover streamlit and gradio. Here’s a list of tools and models learners will get exposed to. – PyTorch – Streamlit – Gradio – AWS EC2 and S3 – Different LLMs listed in the curriculum section
Yes. Generative AI is one of the main topics but this is an NLP course. So we do cover classic NLP methods and traditional use cases. Text processing and data preparation will also be covered extensively.
There are two types of projects: personal projects (also called capstone or portfolio projects) and real client projects. All students in the course will need to complete the capstone project. The real client project is a different training and career service offered at WeCloudData via our partner Beamdata. Learners will become a trainee and receive project-based training. Learners will be assigned to a real project team to work with clients and get mentored and trained by our project managers and project leads. It’s a great learning opportunity and also allow the students to gain real experience to stand out in the job market. You can visit the real client project course page or talk to a learning advisor to find more details.
Yes. Lots of labs and exercises. Students will have access to quizzes so they can test their knowledge on certain topics.
Yes, payment plan is available for this course. You can fill out the form on this course page to access the course package page. It has funding related information. Our learning advisor can also help you with your questions.
Yes, absolutely. All short courses are eligible for bundling and scholarship. When you purchase multiple courses you can get good discounts. Please reach out to our learning advisors by filling out the inquiry form.
No. This AI course is developed for anyone with solid python programming experience. You don’t need to be a SDE or developer. Many students in this program are from data science and analytics background. Software development experience will be helpful when it comes to AI application development. But it’s not for everyone. If you don’t come from SDE background, you can work with a classmate from SDE background to build cool LLM apps.
Yes. Scholarship is available for students who meet the requirements. A scholarship test needs to be completed and the learner needs have a 20-minute live assessment with the program manager. Alumni who have completed courses that meet the pre-requisites will also be eligible for scholarships.
This is a short course that’s part of the diploma program. To get the diploma which is only available for the learners in Canada, you will need to complete 3 courses. Please contact our learning advisor for the diploma program details.
Courses in the AI/ML program usually have a good mix of learners from various background. Most students already have python programming and data science experiences. It doesn’t have to be related work experience though. We have learners who has completed self-paced ML courses or other data science bootcamps as well. Typically learners come from – Data scientist – Data analysts who have taken ML courses – Software engineers who have machine learning knowledge – New graduates from computer science or computer engineering who have taken ML courses in school or who can complete the pre-course materials on their own – Career switchers who have taken other data science and machine learning courses – Non-tech background learners who have completed WeCloudData’s machine learning course as a pre-requisite – PhD graduates who want to fast track their job search and meet the technical pre-requisite
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View our AI Course: NLP/LLM course package
View our AI Course: NLP/LLM course package