Natural Language Processing Bootcamp

Intermediate – Advanced

Natural Language Processing Bootcamp

This program will enhance learners’ existing machine learning and deep learning skills with the addition of natural language processing, speech recognition techniques and Generative AI.

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Included with – BAI Plus


Natural Language Processing to drive your earnings


Average salary increase of Natural Language Processing students who provided pre- and post-course salaries

September 2022

In this robotic engineering bootcamp you will:

This program will enhance learners’ existing machine learning and deep learning skills with the addition of natural language processing and speech recognition techniques
You’ll learn the fundamentals of how Generative AI works, and how to deploy it in real-world applications.

Meet the growing demand for Natural Language Processing and master the job-ready skills that will take your career to new heights.

Get an edge with human support

Work with a mentor, career coach, and more. They have your back and will hold you accountable.

Verify skills mastery

Project review cycle creates a feedback loop with multiple opportunities for improvement—until the concept is mastered.

Verify skills mastery

Learning accelerates as skilled mentors identify areas of achievement and potential for growth.

What will you learn

Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

A well-prepared learner should have significant experience with Python and entry-level experience with probability, statistics, and deep learning architectures.

Learners should also have the ability to write a class in Python and add comments to their code for others to read.

Lastly, learners should have familiarity with the term “neural networks” and the differential math that
drives backpropagation.

Introduction to Natural Language Processing

Learn text processing fundamentals including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model.

Course Project

Part of Speech Tagging

Use several techniques, including table lookups, n-grams, and hidden Markov models, to tag parts of speech
in sentences, and compare their performance. This project demonstrates text processing techniques that
allow one to build a part of speech tagging model. Work with a simple lookup table and progressively add
more complexity to improve the model using probabilistic graphical models. Use a Python package to build
and train a tagger with a hidden Markov model, and compare the performances of all these models in a
data set of sentences


Computing with Natural Language

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

Course Project

Machine Translation
Build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation. Be able to explore several recurrent neural network architectures and compare their performance. Pre-process the data by converting text to sequence of integers. Build several deep learning models for translating the text into French. Run this models on English test to analyze their performance.

Communicating with Natural Language

Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks.

Course Project

Build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text. Begin by investigating a data set that will be used to train and evaluate the models.

Convert any raw audio to feature representations that are commonly used for ASR. Build neural networks that map these features to transcribed text.

Generative AI use cases, project lifecycle, and model pre-training

Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment.


Summarize Dialogue. 

In this lab you will do the dialogue summarization task using generative AI. You will explore how the input text affects the output of the model, and perform prompt engineering to direct it towards the task you need. By comparing zero shot, one shot, and few shot inferences, you will take the first step towards prompt engineering and see how it can enhance the generative output of Large Language Models.


Fine-tuning and evaluating large language models

– Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
– Use empirical scaling laws to optimize the model’s objective function across dataset size, compute budget, and inference requirements


Fine-Tune a Generative AI Model for Dialogue Summarization

You will fine-tune an existing LLM from Hugging Face for enhanced dialogue summarization. You will use the FLAN-T5 model, which provides a high quality instruction tuned model and can summarize text out of the box. To improve the inferences, you will explore a full fine-tuning approach and evaluate the results with ROUGE metrics. Then you will perform Parameter Efficient Fine-Tuning (PEFT), evaluate the resulting model and see that the benefits of PEFT outweigh the slightly-lower performance metrics.

Reinforcement learning and LLM-powered applications

Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project.


Fine-Tune FLAN-T5 with Reinforcement Learning

You will fine-tune a FLAN-T5 model to generate less toxic content with Meta AI’s hate speech reward model. The reward model is a binary classifier that predicts either “not hate” or “hate” for the given text. You will use Proximal Policy Optimization (PPO) to fine-tune and reduce the model’s toxicity.

What is a professional certificate?

Develop the skills necessary to complete the job

Whether you want to start a new career or change your current career, Coursera’s professional certificates help you prepare for the position. Learn at your own pace, at a time and place that is most comfortable for you. Enroll today and discover a new career with a 7-day free trial. You can pause your classes or end the subscription at any time.

Practical projects

Apply your skills to practical projects and develop a portfolio that demonstrates your job readiness to potential employers. You will need to finish the projects correctly to get your certificate.

Get a professional credential

When you complete all the courses in the program, you earn a certificate that you can share with your professional network, as well as access to professional support resources to help you start your new career. Many professional certificates have partners interested in hiring staff who recognize the professional certificate credential, and others can help you prepare for the certificate exam. You can see more information on the pages of the particular professional certificate where it applies.

Program Offer It includes
Real world projects Yes
Mentor Tech Support Yes
Student community Yes
CV support Yes
Freelance Projects Yes
Real World Project
Develop an End-to-End project that will allow you to interact in a real work environment.
Self-paced mode

All the materials of the course are available, so that you can take the course at your own pace. 

  • Follow the suggested syllabus week by week
  • Just start watching the videos and join Slack
  • Check FAQ if you have problems
  • If you can’t find a solution to your problem in FAQ, ask for help in Slack
Apply to the next bootcamp
This robotic bootcamp is a five-month program for students devoting 15-20 hours per week.
The next cohort starts

Oct 16, 2023

Deadline for applications

Oct 10, 2023

Certificate to share
Get a certificate upon completion

100% online

Get started right away and learn at your own pace.


Basic – Intermediate

Estimated Time

5 – 10 hours / week

4 months complete


Subtitles:  All languages


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