Before a language model can learn from text, that text needs a representation the model can work with. Google DeepMind: Represent Your Language Data explores this step through preprocessing, tokenization and embeddings, showing why data representation deserves more than a default setting.
This free intermediate course combines implementation-focused lessons with questions about the people and communities represented in a dataset. It is useful if you want to understand tokenizer choices, build on small-language-model foundations and make your data decisions easier to explain.
Course at a glance
| Provider | |
|---|---|
| Platform | Google Skills |
| Level | Intermediate |
| Language | English |
| Estimated time | 4 hours; coding and review may take longer |
| Format | Web-based instruction, tokenizer exercises, reflection and four knowledge checks |
| Access | Free instruction; free learner account |
| Recognition | Completion badge advertised; not professional certification |
What you’ll learn
- Compare character, word and subword tokenization and their effect on vocabulary and sequence length.
- Explore word-frequency patterns, vocabulary size and the trade-offs involved in language representation.
- Implement byte pair encoding, examine embeddings and train a small language model using a subword tokenizer.
- Consider privacy, consent, ownership and representation when documenting a dataset with a Data Card.
Skills you’ll gain
- Text preprocessing
- Tokenizer comparison
- Byte pair encoding
- Embedding concepts
- Responsible dataset documentation
Follow text through the representation pipeline
The syllabus moves from preprocessing to tokenization, embeddings and a challenge. Character and word tokenizers appear before subword tokenization and byte pair encoding. That order helps you compare approaches before treating any one tokenizer as the obvious choice.
The coding material includes preprocessing, tokenizer experiments, a byte pair encoding implementation and embeddings. A later exercise connects a byte pair encoding tokenizer with small-language-model training. The course therefore links a data decision with the model workflow it supports, rather than stopping at terminology.
As you work, track what changes when a tokenizer changes. How is the same sentence divided? What happens to vocabulary size or the number of tokens? These are useful questions for reading the exercises; they are not promises that one particular tokenizer will always reduce costs or improve quality.
Describe the dataset as carefully as the model
The responsible-data objectives cover privacy, consent, ownership and representation. The course also introduces Data Cards and community-aligned dataset work, with attention to African contexts. This gives documentation a practical role: make the choices around the data visible, including assumptions that might otherwise disappear behind a training script.
For an optional study exercise, write a short description of a fictional text collection. State its intended purpose, where the text would come from and whose language or experiences might be missing. Keep the collection fictional; there is no need to gather personal information to understand the documentation task.
Then add a representation note explaining which tokenizer you would investigate and what you would compare. This is independent reflection, not a Google assignment or a claim that a Data Card resolves every legal or ethical question. The aim is to connect implementation decisions with a clear explanation of the data.
The course is Intermediate. Some familiarity with programming and the language-model training process will make the practical material easier to follow. If you need more time with a coding exercise, use the four-hour estimate as a guide rather than a deadline.
Free instruction and completion details
The course instruction is advertised as Free on Google Skills, with a free learner account used for access and progress. Its public curriculum contains web-based coding exercises and four required knowledge checks, without a separately listed provisioned cloud lab.
A completion badge is advertised after the required activities. It is not academic credit, professional certification or a guarantee of lawful dataset use. Independently executing code, training models or using hosted computing can require separate resources and may involve costs.
Explore the free course catalogue for further learning that fits the concepts you want to develop.
Frequently asked questions
Which tokenization approaches are covered?
The objectives include character, word and subword tokenization, special tokens, vocabulary and sequence-length trade-offs, plus implementation of byte pair encoding.
Is this only a discussion of data ethics?
No. The curriculum combines tokenizer and embedding exercises with privacy, consent, ownership, representation and Data Card documentation.
Are model-training resources guaranteed free?
The course instruction is advertised as Free. Independently running tokenizer experiments or training a model may use computing resources with separate access or costs; unlimited free hosted computing is not promised.
Questions & discussion
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