Back to Catalog

Master Word Embeddings from Scratch with Word2Vec & PyTorch

BeginnerGuided Project

Learn to create word embeddings from scratch using Word2Vec and PyTorch. In this project, you'll implement Continuous Bag of Words (CBOW) and Skip-gram models, essential for Natural Language Processing (NLP) tasks. Gain a deep understanding of how word embeddings represent text data, enabling better context and meaning extraction. This hands-on project focuses on building foundational skills in NLP, empowering you to better understand and apply word embedding techniques to real-world text-processing tasks.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • NLP, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • May 21, 2025
About this Guided Project
Unlock the power of Word2Vec for natural language processing in this hands-on project. Learn to implement and train Continuous Bag of Words (CBOW) and Skip-gram models using PyTorch, mastering how word embeddings represent relationships between words. Additionally, explore GloVe (optional but fun to learn) as another powerful technique for generating word embeddings, broadening your understanding of NLP. With practical coding exercises, this project helps you understand the principles behind Word2Vec and introduces GloVe, foundational NLP techniques used in search engines, recommendation systems, and sentiment analysis.

------------------------------------------------------------------------------------------------------------------

Why This Topic Is Important:

Word embeddings are crucial for understanding and processing text data in real-world applications, from chatbots to text classification systems. This project offers a clear and practical introduction to Word2Vec while also introducing GloVe, giving you insights into two widely used techniques in NLP workflows. By completing this project, you’ll gain the foundational knowledge to use word embeddings effectively and enhance your ability to tackle language-driven challenges in AI and machine learning.

------------------------------------------------------------------------------------------------------------------

A Look at the Project Ahead:

In this project, you’ll implement Word2Vec models using PyTorch and understand the mechanics of CBOW and Skip-gram architectures. You’ll also have the opportunity to explore GloVe as an optional but valuable technique for generating word embeddings. Dive into the mathematical foundations and coding implementation of word embeddings while exploring their applications in NLP.
By the end of the project, you will:
  • Understand the principles of Word2Vec and the role of CBOW and Skip-gram models in generating word embeddings.
  • Learn to train Word2Vec models from scratch using PyTorch, equipping you with hands-on experience in building and testing embeddings.
  • Explore how word embeddings capture semantic relationships between words for improved text representation in machine learning tasks.

------------------------------------------------------------------------------------------------------------------

What You’ll Need

To successfully complete this project, you’ll need:
  • A basic understanding of Python programming and experience with PyTorch.
  • Familiarity with foundational concepts in natural language processing.
  • A web browser to access tools and execute code.
This project is perfect for NLP enthusiasts and professionals looking to deepen their understanding of word embeddings while enhancing their machine learning skills.

Instructors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

Read more

Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

Read more

Contributors

Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

Read more