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Self-paced · Foundations

Introduction to Machine Learning for SEO

A beginner-friendly, practical course for SEOs and digital marketers. Learn how the core machine learning models work, then apply them to real SEO workflows with the scripts, templates and demo datasets provided.

Enrol now See the curriculum
  • 10+ hours of video lessons
  • 20+ scripts, templates and datasets
  • Certificate of completion
  • FormatSelf-paced video course
  • Structure8 modules · 34 lessons
  • Video10+ hours
  • LevelBeginner
  • CertificateOn completion
  • AccessLifetime
The problem

AI is everywhere in SEO. Practical ML skills are not.

Most AI training for marketers stops at prompts. You get an output, but not the judgement behind it: which model fits which task, what data it needs, and whether the result holds up.

Machine learning for SEO is learnable without a data-science background. Classification, clustering, entity extraction, fuzzy matching and content transformation cover a large share of the automation SEOs need. This course teaches each one, then shows you how to run it on your own data and turn the output into SEO actions.

No hype, no gatekeeping, no prompt-pack shortcuts.

What you'll learn

Six ML techniques, applied to real SEO work

Every technique comes with the theory, the SEO and marketing use cases, and a hands-on lab.

  • The why behind ML for marketers

    Where ML-enabled automation pays off, the hard and soft skills you’ll build, and how to find ML-enabled automation for any project.

  • Classification workflows

    Classify page content with the Google Natural Language API and classify search intent with rule-based and ML-enabled methods.

  • Clustering for content and queries

    Cluster keywords with KeyBERT and model page topics with LDA and BERTopic to inform information architecture, internal linking and briefs.

  • Entity extraction pipelines

    Extract entities from queries and content, compare NLP APIs with generative AI, and analyse customer reviews for entities and sentiment.

  • Fuzzy matching at scale

    Map 404s to live pages, build redirect maps automatically, and compare your metadata against competitors’ to find content opportunities.

  • Content transformation with ML

    Turn blog posts into social posts and rewrite titles and meta descriptions at scale with generative models.

How this course is different

Built to be used, not just watched

  1. Taught by a practitioner

    Lazarina has implemented machine learning for SEO at enterprise scale. The course turns that experience into practical, repeatable workflows that marketing teams can run.

    Practitioner-led

  2. Theory first, then a lab

    Each technique module explains the concept, maps it to SEO and marketing use cases, then walks you through a practical with scripts and demo datasets you can reproduce and adapt.

    Hands-on labs

  3. A decision framework, not a prompt pack

    Start from the business problem, pick the approach, and validate with simple metrics and visual checks before you scale. You learn when to use a model, not just how.

    Decision framework
Curriculum

What's inside

Eight modules, from the why behind ML for marketers to a hands-on lab for each core technique. Each technique module follows the same arc: what it is, where it applies, then a practical.

Module 1: Course Intro / Getting ready4 lessons

Why ML-enabled automation matters for marketers, how the course is structured, the skills you’ll gain and the mindset that helps you succeed.

  1. 1.1 Course Introduction Free preview
  2. 1.2 Advantages and Scenarios of Using ML-enabled Automation Free preview
  3. 1.3 Hard and Soft Skills you will gain Free preview
  4. 1.4 Psychological Mindset and Overcoming Limiting Beliefs Free preview
Module 2: Machine Learning Basics5 lessons

Machine learning in the context of AI, defined through task, data and solution characteristics, and a method for finding ML-enabled automation for any project.

  1. 2.1 The difference between AI and ML Free preview
  2. 2.2 ML Task Characteristics Free preview
  3. 2.3 ML Data Characteristics Free preview
  4. 2.4 ML Solution Characteristics Free preview
  5. 2.5 How to find ML-enabled automation for any project you are working on Free preview
Module 3: Introduction to Classification4 lessons

What classification is, where it applies in SEO and marketing, and labs on classifying page content with the Google Natural Language API and classifying explicit search intent.

  1. 3.1 What is classification? Free preview
  2. 3.2 Marketing and SEO implementations of ML classification Free preview
  3. 3.3 Practical: Text Classification of page content with Google Natural Language API Free preview
  4. 3.4 Practical: Methods for search intent classification – Rule-based and ML-enabled Classification Free preview
Module 4: Introduction to Clustering7 lessons

Unsupervised ML for grouping keywords and page content. Labs with KeyBERT, LDA and BERTopic, plus bonus labs on image clustering by colour and customer segmentation.

  1. 4.1 What is ML clustering? Free preview
  2. 4.2 Marketing and SEO implementations of ML Clustering Free preview
  3. 4.3 Practical: Keyword Clustering with KeyBERT Free preview
  4. 4.4 Practical: Clustering of page content with LDA Free preview
  5. 4.5 Practical: Clustering of page content with BERTopic Free preview
  6. 4.6 Practical: Clustering images based on color
  7. 4.7 Practical: Customer Segmentation with ML
Module 5: Introduction to Entity Extraction and Semantic Analysis6 lessons

Entity extraction and semantic analysis for keyword research, internal linking, content analysis and user feedback, including a comparison of NLP APIs with generative AI.

  1. 5.1 What is entity extraction and semantic analysis? Free preview
  2. 5.2 Marketing and SEO implementations of entity and sentiment analysis Free preview
  3. 5.3 Comparative Analysis of NLP APIs versus generative AI on entity extraction Free preview
  4. 5.4 Practical: Query Entity Extraction with Google NLP and Entity ML-enabled data analysis Free preview
  5. 5.5 Practical: Entity Analysis in Web Content Audits for Automated Internal Link Opportunity Identification Free preview
  6. 5.6 Practical: Semantic Analysis of Customer reviews Free preview
Module 6: Introduction to Fuzzy Matching4 lessons

Fuzzy string matching to fix 404s, build redirect maps automatically and map your URLs and titles against competitors’ for content gaps.

  1. 6.1 What is ML fuzzy string matching? Free preview
  2. 6.2 Marketing and SEO Implementations of fuzzy matching Free preview
  3. 6.3 Practical: 404 and Redirect mapping with fuzzy matching Free preview
  4. 6.4 Practical: Competitor or Internal Metadata Opportunity Analysis using fuzzy matching Free preview
Module 7: Introduction to Content Transformation3 lessons

Scaling content transformation with traditional ML models and generative AI, with practical SEO and marketing use cases.

  1. 7.1 What is content transformation in ML? Free preview
  2. 7.2 Practical: Transform blog posts to social posts with OpenAI's GPT Free preview
  3. 7.3 Practical: Rewrite titles and meta descriptions Free preview
Module 8: What's Next1 lesson

A recap and recommendations on where to go next.

  1. 8.1 Where to go from here Free preview

Lessons marked Preview are free to watch on the academy before you enrol.

Is this course a good fit for you?

This course is for you if…

  • SEO specialists and leads who want to use clustering for IA, briefs and opportunity mapping, and automate redirect mapping and content audits.
  • Content and editorial teams who want to turn research into structured content plans and repurpose content with ML.
  • Data-savvy marketers and analysts who want to run beginner-friendly scripts and notebooks and translate ML outputs into KPIs and roadmaps.
  • Agencies and in-house teams who want to standardise research and audits with ML-assisted processes.
  • Consultants and freelancers who want to differentiate with ML-driven insights and deliver audits and mappings faster.

It might not be for you if…

  • You want a prompt pack or copy-paste shortcuts rather than an understanding of the models.
  • You want an academic, maths-first treatment of machine learning.
You keep everything

What you'll walk away with

Every practical comes with the resources to reproduce it. A selection from the 20+ scripts, templates, checklists and datasets in the course:

  • When to use Machine Learning in SEO – Decision Checklist SHEETS

    A checklist for deciding whether a task needs ML, and which kind.

  • Text Classification with Google Cloud Natural Language API in Python COLAB

    The notebook behind the page-content classification lab.

  • Web Content Clustering: Topic Modeling with BERTopic COLAB

    Group page content into topics with BERTopic. An LDA version is included too.

  • Keyword Cluster (KeyBERT) Looker Studio Demo Visualization Dashboard LOOKER

    A dashboard template for exploring keyword clusters from the KeyBERT lab.

  • Automated Redirect Mapping with Triple Fuzzy Matching for Site Migrations COLAB

    Build redirect maps automatically with fuzzy matching.

  • Entity Analysis with Google Cloud Natural Language API (No-Code Template) SHEETS

    Run entity analysis from Google Sheets, no code required.

Your instructor

Taught by a practitioner

Lazarina Stoy
LEAD INSTRUCTOR

Lazarina Stoy

Founder of MLforSEO · ML/SEO consultant

Lazarina Stoy is a marketing and machine learning consultant, trainer and speaker, and the founder of MLforSEO, a training platform and community that helps organic search marketers put machine learning to work in their day-to-day. Over more than a decade in digital marketing and data, she has become known for a practical, sustainable and scalable approach to applying AI and ML to SEO.

Through agency work she has partnered with enterprise organisations including AWS, Skyscanner, Compass and Extreme Networks, often using machine learning to execute ambitious strategies at scale. She built MLforSEO to onboard marketers into AI/ML without the hype, the gatekeeping or the prompt-pack shortcuts. Her work has appeared in Search Engine Land and Moz.

Focus areas
  • Machine Learning for SEO
  • AI Search
  • Technical SEO Automation
  • AI Content Engineering
  • Data Platform & MLOps
  • Measurement, Experimentation & Attribution
Speaker at
  • BrightonSEO
  • TechSEO Connect
  • Pubcon
  • WTSFest
Full profile
From students

What learners say

  • ML can be a dense and complicated subject, but Lazarina explains everything in a way that feels engaging and accessible. Not overwhelming but also not watered down. She is great at breaking things down clearly, which makes the subject far less intimidating.

    Emina Demiri-Watson · Head of Marketing, Vixen Digital

Enrolment

Start today

€250

One payment · instant access on MLforSEO Academy

Enrol now

Prices exclude VAT. VAT is added for EU individuals. Checkout and lessons are on academy.mlforseo.com.

Save with a bundle

Questions

Frequently asked questions

Do I need coding or data-science experience?

No advanced background is required. Light familiarity with spreadsheets and basic Python concepts helps, but every exercise comes with step-by-step walkthroughs, scripts and demo data, so you can reproduce it and adapt it for your site or client.

How much does it cost, and is VAT included?

The course is €250. Prices exclude VAT; VAT is added for EU individuals. Taxes are calculated from your billing country and shown at checkout. If you have a valid VAT/Tax ID, enter it at checkout for the correct treatment. It is also part of the 3-course bundle and the 2-course bundle with Entity SEO & Knowledge Graph Strategies.

How long do I have access?

The course is self-paced and on-demand, with lifetime access and no time limit for completion. Access is provisioned as soon as your payment completes, and you can start from your Academy dashboard straight away.

Do I get a certificate?

Yes. MLforSEO Academy provides a certificate of completion for each course you finish.

Can I buy seats for my team or get an invoice?

Yes. An invoice is issued automatically at checkout with your billing details. For multiple seats, bulk checkout or centralised invoicing, reply to your order email with the number of seats you need.

Which tools and libraries are used?

Accessible Python tooling (notebooks and beginner-friendly scripts) and popular libraries for clustering (LDA and BERT-based methods), keyword extraction and similarity scoring, plus the Google Natural Language API and no-code templates. Everything is packaged so you can run it without a complex setup.

Do I need a powerful computer or paid cloud services?

No. The exercises are designed to run on a typical laptop. Where larger workloads help, the course outlines light-touch options and tips to keep costs low.

What data do I need?

You can start with exports you already have: crawl data, keyword lists, Google Search Console queries, page titles and URLs. The course shows simple cleaning steps and provides sample datasets, so you can practise without production data.

How do I decide which model to use?

The course gives you a decision framework. Start from the business problem, such as grouping content, mapping redirects or scaling formats. Pick the approach: clustering, string similarity or transformation. Validate with simple metrics and visual checks before scaling.

Keep going

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