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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

50+

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 Preview

Machine 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 Preview

Introduction 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 Preview

Introduction 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 Preview Practical: Clustering images based on color Practical: Customer Segmentation with ML

Introduction 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 Preview

Introduction 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 Preview

Introduction 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 Preview

What's Next

In this module, we say congratulations to you and your achievements and go over recommendations on what’s next for you.

Lessons

Where to go from here Preview

Introduction to Machine Learning for SEO – FAQs

1) What is this course about and what will I be able to do after it?

A beginner-friendly, practical intro to machine learning for SEOs and digital marketers. You’ll learn core ML concepts and apply them to real SEO workflows like clustering keywords and pages, fuzzy matching for redirects and content mapping, and content transformation using modern models.

By the end, you’ll know when to use specific models, how to run them with provided scripts and datasets, and how to translate outputs into SEO actions and reporting.

2) Do I need coding or data-science experience to follow along?

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

3) Which tools and libraries are used?

We use accessible Python tooling (e.g., notebooks plus beginner-friendly scripts) and popular libraries behind the scenes for tasks like clustering (e.g., LDA/BERT-based methods), keyword extraction, and similarity scoring. Everything is packaged so you can run it without a complex setup.

4) How is ML applied to SEO in the course?

You’ll complete practical labs for:

  • Clustering: topic modelling for pages/keywords to inform information architecture and briefs.
  • Fuzzy matching: automate 404 mapping, redirects, and competitor/content mapping.
  • Content transformation: turn one format into another at scale with ML and modern generative tools.
5) How do I decide which model or approach to use?

The course gives a decision framework: start from the business problem (e.g., grouping content, mapping redirects, scaling formats), pick an approach (clustering, string similarity, or transformation), and validate with simple metrics and visual checks before scaling to production.

6) What data do I need and how do I prepare it?

You can start with exports you already have (crawl data, keyword lists, GSC queries, page titles/URLs). We show simple cleaning steps and provide sample datasets so you can practice even without immediate access to production data.

7) Do I need a powerful computer or paid cloud services?

No heavy setup required. Exercises are designed to run on typical laptops. Where larger workloads help, we outline light-touch options and tips to keep costs low.

8) How will this change my day-to-day SEO work?

Expect faster research and clearer prioritisation via clusters, less manual redirect/content mapping, and the ability to repurpose content formats at scale—turning ML outputs into briefs, recommendations, and measurable wins.

Who this course is for

Built for SEOs and marketers who want practical, repeatable ML workflows—without needing a data-science background.

SEO Specialists & Leads

  • Use clustering to shape IA, briefs, and opportunity mapping.
  • Automate redirect mapping and large-scale content audits.

Content & Editorial Teams

  • Turn research outputs into structured content plans at scale.
  • Repurpose content with ML-powered transformation workflows.

Data-savvy Marketers & Analysts

  • Run beginner-friendly scripts and notebooks with provided data.
  • Translate ML outputs into KPIs, dashboards, and roadmaps.

Agencies & In-house Teams

  • Standardise research and audits with ML-assisted processes.
  • Scale deliverables (briefs, mappings, migration support).

Consultants & Freelancers

  • Differentiate with ML-driven insights for strategy and ops.
  • Deliver faster site audits, mappings, and content scaling.

Course Purchase FAQs

Can you issue an invoice?

Yes. Complete checkout and enter your billing details (name, company, address, and—if applicable—VAT/Tax ID). An invoice/receipt is issued automatically and emailed to you. If anything needs correcting, reply to the order email and we’ll re-issue.

What payment methods do you accept?

Major debit/credit cards are supported, with additional local options shown at checkout depending on your location. For company POs or bank transfers for multiple seats, reply to your order email for assistance.

How soon do I get access after purchasing?

Access is provisioned as soon as payment completes. You’ll receive a confirmation email and can log into your Academy dashboard to start right away.

How long do I have access to the course?

The course is self-paced and on-demand—no time limits for completion.

Do you charge VAT or sales tax?

Taxes are calculated based on your billing country and shown at checkout. If you have a valid VAT/Tax ID (where applicable), enter it during checkout for the correct treatment.

Can I buy multiple seats or a team license?

Yes. For teams, we can organise bulk checkout and centralised invoicing. Reply to your order email with the number of seats you need and we’ll set it up.

Can you update my invoice details after purchase?

Yes. If you need a correction (e.g., company name, address, VAT number), reply to the invoice or order email with the exact details and we’ll re-issue the document.

Do you provide a certificate of completion?

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

Can I change the seat to a different person after purchase?

If you purchased with the wrong email or need to reassign a seat, reply to your order email. We can move access to the correct learner’s account.

Can I expense the course to my company?

Absolutely—use your company billing details at checkout to receive a formal invoice. If your finance team needs specific wording or a PO reference, include it in the “Company” or “Notes” field.