Google has updated its machine learning crash course with new videos and modules on large language models and automated machine learning. These courses are useful introductions to the technologies behind modern search engines and generative AI, information that will make you a better SEO.
What Is Google’s Machine Learning Crash Course?
Google’s machine learning course is an easy to understand introduction to machine learning. It shows what machine learning is all about and how it can be useful to you and your business.
The different courses are self-contained in modules, beginning with introductions to the fundamentals of Linear Regression, Logistic Regression, and Binary Classification Models
The other modules cover:
- Data
How to work with machine learning data - Advanced Machine Learning Models
Introductions to Neural Networks, Embeddings, and Large Language Models - Real-world ML
These modules cover best practices for deploying machine learning models in the real world.
The new course adds topics that include:
New Large Language Model (LLM) Module
The Large Language Models module is a new addition to the courses and is a good way to get up to speed fast with the technology and be conversant about it.
Google’s documentation shows what students learn with the module:
“Define a few different types of language models and their components.
Describe how large language models are created and the importance of context and parameters.
Identify how large language models take advantage of self-attention.
Reveal three key problems with large language models.
Explain how fine-tuning and distillation can improve a model’s predictions and efficiency.”
Google recommends first taking six other courses before starting the LLM module, so as to gain an understanding of the fundamentals. The six recommended courses look very interesting:
- Introduction to Machine Learning
- Linear regression
- Working with categorical data
- Datasets, generalization, and overfitting
- Neural networks
- Embeddings
The courses for linear regression, neural networks and embeddings can arguably be called essential for SEOs because these technologies have been a major part of how search ranking algorithms work. Obtaining a basic understanding about these technologies will improve your ability to understand how the backend of search engines work.
Many misleading ideas are popular in the SEO community because they sound like common sense, much like some answers you may have experienced from generative AI make sense but are hallucinations. Learning what these technologies are and how they work will help you become a better search marketer.
Read Google’s announcement:
Our Machine Learning Crash Course goes in depth on generative AI
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