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New course by Beatrice Gamba: AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands now live -> Start learning today ✨
Introduction to Machine Learning for SEOs
Beginner-friendly course, perfect for SEOs and digital marketers, who are interested in practically implement artificial intelligence (specifically – machine learning models) in their workflows. This course is designed to walk you through not only the theory of different machine learning models but also equip you with the tools to implement everything you learn and the knowledge of applying the insights for improving your processes as an organic search marketer.
10+
Hours of video tutorials, explaining in a practical way AI/ML for beginners and non-technical marketers
20+
Scripts, no-code templates, checklists, dataset samples, and other resources shared to kickstart your implementation
30+
Forward-thinking marketers are currently taking this course
What Students are Saying
COURSE AUTHOR & INSTRUCTOR
Lazarina Stoy.
Lazarina Stoy is a recognized expert in the intersection of SEO, data science, and machine learning. With a background in digital marketing and technology, Lazarina has worked with top-tier enterprise-level companies like AWS, Skyscanner, and Extreme Networks, leveraging machine learning to enhance SEO strategies, implement process automation, and drive organic growth. She is known for her ability to simplify complex technical concepts, making them accessible and exciting for both beginners and professionals.
Lazarina has led numerous projects implementing machine learning for SEO tasks, before turning her passion for technology into the training platform MLforSEO, where she helps organic search marketers get onboarded into the world of AI. Additionally, she has developed training resources, contributed to major industry publications like Search Engine Land and Moz, and spoken at leading digital marketing conferences worldwide.
What you will learn in this course ✨
Course Intro/ Getting ready
In this module, we’ll explore how ML-enabled automation, when properly understood and approached, can transform you as a marketer. We will talk about how the course is structured and why, what motivations you might have for learning ML as an SEO, the skills you might expect to gain, and the psychological mindset that will help you succeed.
Lessons
Course Introduction Preview Advantages and Scenarios of Using ML-enabled Automation Preview Hard and Soft Skills you will gain Preview Psychological Mindset and Overcoming Limiting Beliefs PreviewMachine Learning Basics
In this module, we’re tackling the very basics of machine learning by contextualising it in the field of AI, and defining it via characteristics of different tasks, data that might be used, and solutions you might develop. This module is focused on providing you with everything you need in understanding how to go about finding ML-enabled solutions for any marketing automation idea you might have!
Lessons
The difference between AI and ML Preview ML Task Characteristics Preview ML Data Characteristics Preview ML Solution Characteristics Preview How to find ML-enabled automation for any project you are working on PreviewIntroduction to Classification
This module is all about machine learning classification fundamentals, where we’ll explore what classification is, how it works, and where it’s best applied in SEO and digital marketing projects. You’ll learn to identify areas where classification can enhance decision-making and automation, and I’ll guide you through a hands-on practical exercise using the Google Natural Language API to classify page content effectively, as well as how to use ML classification to classify explicit search intent.
Lessons
What is classification? Preview Marketing and SEO implementations of ML classification Preview Practical: Text Classification of page content with Google Natural Language API Preview Practical: Methods for search intent classification – Rule-based and ML-enabled Classification PreviewIntroduction to Clustering
In this module, we’re diving into unsupervised ML with the powerful technique of clustering. We’ll go over what clustering is, and explore practical applications of clustering, from grouping similar page content (topic modelling) to better organizing keywords. Through hands-on exercises, you’ll learn how to apply popular models like LDA, BERTopic, and KeyBERT, equipping you with everything needed to tackle internal linking, content organisation, and keyword research strategies more efficiently.Bonus Lessons coming soon on customer segmentation, anomaly detection, and image clustering ✨
Lessons
What is ML clustering? Preview Marketing and SEO implementations of ML Clustering Preview Practical: Keyword Clustering with KeyBERT Preview Practical: Clustering of page content with LDA Preview Practical: Clustering of page content with BERTopic PreviewIntroduction to Entity Extraction and Semantic Analysis
In this module, we introduce the concept of entity extraction and semantic analysis, exploring what entity extraction is and it’s many implementations in SEO projects, including applications in keyword research, internal linking, content analysis, and user feedback analysis – to name just a few! Via hands-on labs, practical exercises, and beginner-friendly projects, you’ll learn everything you need on where, how, and why to harness the power of semantic analysis.
Lessons
What is entity extraction and semantic analysis? Preview Marketing and SEO implementations of entity and sentiment analysis Preview Comparative Analysis of NLP APIs versus generative AI on entity extraction Preview Practical: Query Entity Extraction with Google NLP and Entity ML-enabled data analysis Preview Practical: Entity Analysis in Web Content Audits for Automated Internal Link Opportunity Identification Preview Practical: Semantic Analysis of Customer reviews PreviewIntroduction to Fuzzy Matching
In this module, we’re introducing a simple yet mighty approach to ML-based text processing – fuzzy matching. We’ll go over what fuzzy string matching is in machine learning, and explore its practical applications in the context of SEO and other marketing functions. Through the hands-on practical labs, and with the help of the scripts and demo datasets, you’ll learn how to use string matching to eliminate 404s on your website, create redirect maps automatically, or map your content’s URLs and titles against competitors’ for identification of content opportunities and content gaps. Bonus Lesson coming soon on hreflang analysis with fuzzy matching ✨
Lessons
What is ML fuzzy string matching? Preview Marketing and SEO Implementations of fuzzy matching Preview Practical: 404 and Redirect mapping with fuzzy matching Preview Practical: Competitor or Internal Metadata Opportunity Analysis using fuzzy matching PreviewIntroduction to Content Transformation
In this final module of the course, we’ll go over the latest and greatest in machine learning – content transformation. We’ll explore different ways to scale content transformation with the help of machine learning models – both some of the more traditional, and with the latest technologies in generative AI. We’ll cover practical use cases for SEO and digital marketing.
Lessons
What is content transformation in ML? Preview Practical: Transform blog posts to social posts with OpenAI's GPT Preview Practical: Rewrite titles and meta descriptions PreviewKey Takeaways & What's Next
In this module, we say goodbye and most importantly – congratulations, by recapping everything learned and looking ahead to new horizons.