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Lazarina Stoy – MLforSEO Academy Bundle

Name: Lazarina Stoy - MLforSEO Academy Bundle
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Published on: June 12, 2025

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Category: Product For Sale Tag: Lazarina Stoy - MLforSEO Academy Bundle

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Here’s What You Get:

Introduction to Machine Learning Course by Lazarina Stoy.

Beginner-friendly course, perfect for SEOs and digital marketers, who are interested in practical machine learning. It 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 SEO.

Key Details

On-demand course with video lessons
9+ hours of content already available, and dozens of templates, scripts, workflows, checklists.
a ton of bonus lessons added

Introduction to Machine Learning for SEOs

Our beginner-friendly course is perfectly tailored for SEOs and digital marketers looking to delve into practical machine learning applications. This course combines essential theoretical knowledge with hands-on tools to enhance your skill set.

Throughout this course, you will explore different machine learning models, gaining insights that are directly applicable to your SEO strategies. Each session is designed to be interactive, ensuring that you not only learn but also apply the concepts effectively.

By the end of the course, you’ll have a comprehensive understanding of machine learning techniques to improve your SEO performance. Join us to enhance your marketing strategy, making data-driven decisions that propel your business forward.

Modules & Lessons

The ‘Why’ behind using machine learning in SEO work
1. Course Introduction
2. Advantages and Scenarios
3. Skills you’ll gain (hard and soft)
4. The Psychological mindset I want you to develop to succeed: Overcoming limiting beliefs
Machine Learning Basics
1. The difference between AI and ML
2. Task Characteristics
3. Data Characteristics
4. Solution Characteristics
5. How to find ML-enabled automation for any project you are working on
Introduction to Classification
1. What is classification?
2. In which SEO and digital marketing projects can classification be implemented in, and why?
3. Practical: Text Classification of page content with Google Natural Language API
4. Practical: Search Intent Classification of User Search Queries (✨ bonus lesson, live now)
Introduction to Clustering
1. What is clustering?
2. In which SEO and digital marketing projects can clustering be implemented in, and why?
3. Practical: Clustering of page content with LDA
4. Practical: Clustering of page content with BERTopic
5. Practical: Keyword Clustering with KeyBERT
6. Practical: Customer Segmentation (✨ bonus lesson – coming soon)
7. Practical: Anomaly Detection in GA4 data or GSC data (✨ bonus lesson – coming soon)
8. Practical: Clustering images based on color (✨ bonus lesson – coming soon)
Introduction to Entity Extraction and Analysis
1. What is entity extraction?
2. In which SEO and digital marketing projects can entity extraction be implemented in, and why?
3. Analysing different entity extraction APIs versus generative AI
4. Practical: Query Entity Extraction with Google NLP and Entity ML-enabled data analysis
5. Practical: Entity Analysis of Web Content Audits for Internal Link Opportunities (✨ bonus lesson – coming soon)
6. Practical: Semantic Analysis of Customer reviews – Entity and Sentiment analysis (✨ bonus lesson, live now)
Introduction to Fuzzy Matching
1. What is fuzzy matching?
2. In which SEO and digital marketing projects can fuzzy matching be implemented in, and why?
3. Practical: 404 and Redirect mapping with fuzzy matching
4. Practical: Competitor or Internal Metadata Opportunity Analysis using fuzzy matching
5. Practical: hreflang URL analysis with fuzzy matching (✨ bonus lesson – coming soon)
Introduction to Content Transformation (March release)
1. What is content transformation (different types)?
2. In which SEO and digital marketing projects can content transformation be implemented in, and why?
3. Practical: Transform blog posts to social posts with OpenAI’s GPT
4. Practical: Summarise content to write titles and meta descriptions with BERT
Key Takeaways and What’s Next (March release)
1. Course Outro
2. Where to go from here

Semantic ML-enabled Keyword Research Course by Lazarina Stoy.

This course, Semantic ML-Enabled Keyword Research: From Theory to Practical Application, offers an in-depth exploration of modern strategies on semantic query analysis, moving beyond traditional approaches.

You’ll learn how to leverage query semantics, entities, search intent, and knowledge graphs (to name a few concepts) to create user-centric content strategies. With a focus on improving your skill set on not only understanding the conceptual framework of semantic query analysis but also knowing when, why, and how to implement AI (ML-enabled automation), this course will guide you through advanced data analysis techniques and practical tools for semantic keyword research.

In this 8+ hour course, you will also be provided with numerous practical programming scripts, made as friendly to beginners as possible, in-depth step-by-step explanations and walk-throughs, countless exercises, checklists, and other relevant tools and templates – everything you need to understand not only the theory behind the concepts but also how to practically get started.

By the end, you’ll be equipped to conduct comprehensive semantic query analysis, visualize your findings, and integrate them into actionable projects.

Modules & Lessons

Introduction & Overview
1. The problem with traditional keyword research
2. Who this course is for and what we’ll cover
Fundamentals of Semantic Keyword Research
1. Entities, Entity Attributes, Entity Attribute Variables (EAV Model)
2. Practical/Lab – How to use the Google Natural Language API for Query Entity Extraction and Analysis
3. Search Query Sequences and Query Path
4. Practical/Lab – How to work with Google’s Autocomplete API to uncover Google-suggested query paths
Query Understanding and Analysis
1. Query Augmentation
2. Query Context and Session Context
3. Implicit User Feedback and User Search Behaviour
Understanding the SERP – Theory and practice
1. SERP Feature Analysis
2. Practical/Lab – How to work with dataforSEO and other SaaS for SERP collection (programmatic and no-code approaches)
3. Practical/Lab – How to identify desired content formats and platforms served from SERP data
Search Intent – Theory and Practice
1. Search Intent
2. Practical/Lab – Methods for explicit search intent classification (rule-based, and AI/ML-enabled)
Advanced Semantic Keyword Analysis Concepts
1. Knowledge Graphs & working with Google’s Knowledge Graph API
2. Information Gain
3. How Google Uses Entities and the Knowledge graph (Patents Analysis Resource)
Building a semantic keyword universe – Start to Finish
1. Getting your data – data sources run-through
2. Organising your database and keyword categorisation
3. How to move from traditional to semantic keyword universe – Checklist, based on course tasks
4. What a good semantic keyword universe looks like – guidance on delivery and presentation
Integrating Semantic Keyword Research Into Projects
1. How to integrate semantic keyword research into real-world projects
2. Practical/Lab – How to Automate Content Briefs from your Semantic Keyword Universe
Key Takeaways and What’s Next
1. Course Takeaways
2. What’s next

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Lazarina Stoy – MLforSEO Academy Bundle

IMPORTANT

IM-COURSES MAIN FEATURES

Here’s What You Get:

Introduction to Machine Learning Course by Lazarina Stoy.

Key Details

Introduction to Machine Learning for SEOs

Our beginner-friendly course is perfectly tailored for SEOs and digital marketers looking to delve into practical machine learning applications. This course combines essential theoretical knowledge with hands-on tools to enhance your skill set.

Throughout this course, you will explore different machine learning models, gaining insights that are directly applicable to your SEO strategies. Each session is designed to be interactive, ensuring that you not only learn but also apply the concepts effectively.

By the end of the course, you’ll have a comprehensive understanding of machine learning techniques to improve your SEO performance. Join us to enhance your marketing strategy, making data-driven decisions that propel your business forward.

Modules & Lessons

The ‘Why’ behind using machine learning in SEO work

Course Introduction

Advantages and Scenarios

Skills you’ll gain (hard and soft)

The Psychological mindset I want you to develop to succeed: Overcoming limiting beliefs

Machine Learning Basics

The difference between AI and ML

Task Characteristics

Data Characteristics

Solution Characteristics

How to find ML-enabled automation for any project you are working on

Introduction to Classification

What is classification?

In which SEO and digital marketing projects can classification be implemented in, and why?

Practical: Text Classification of page content with Google Natural Language API

Practical: Search Intent Classification of User Search Queries (✨ bonus lesson, live now)

Introduction to Clustering

What is clustering?

In which SEO and digital marketing projects can clustering be implemented in, and why?

Practical: Clustering of page content with LDA

Practical: Clustering of page content with BERTopic

Practical: Keyword Clustering with KeyBERT

Practical: Customer Segmentation (✨ bonus lesson – coming soon)

Practical: Anomaly Detection in GA4 data or GSC data (✨ bonus lesson – coming soon)

Practical: Clustering images based on color (✨ bonus lesson – coming soon)

Introduction to Entity Extraction and Analysis

What is entity extraction?

In which SEO and digital marketing projects can entity extraction be implemented in, and why?

Analysing different entity extraction APIs versus generative AI

Practical: Query Entity Extraction with Google NLP and Entity ML-enabled data analysis

Practical: Entity Analysis of Web Content Audits for Internal Link Opportunities (✨ bonus lesson – coming soon)

Practical: Semantic Analysis of Customer reviews – Entity and Sentiment analysis (✨ bonus lesson, live now)

Introduction to Fuzzy Matching

What is fuzzy matching?

In which SEO and digital marketing projects can fuzzy matching be implemented in, and why?

Practical: 404 and Redirect mapping with fuzzy matching

Practical: Competitor or Internal Metadata Opportunity Analysis using fuzzy matching

Practical: hreflang URL analysis with fuzzy matching (✨ bonus lesson – coming soon)

Introduction to Content Transformation (March release)

What is content transformation (different types)?

In which SEO and digital marketing projects can content transformation be implemented in, and why?

Practical: Transform blog posts to social posts with OpenAI’s GPT

Practical: Summarise content to write titles and meta descriptions with BERT

Key Takeaways and What’s Next (March release)

Course Outro

Where to go from here

Semantic ML-enabled Keyword Research Course by Lazarina Stoy.

This course, Semantic ML-Enabled Keyword Research: From Theory to Practical Application, offers an in-depth exploration of modern strategies on semantic query analysis, moving beyond traditional approaches.

By the end, you’ll be equipped to conduct comprehensive semantic query analysis, visualize your findings, and integrate them into actionable projects.

Modules & Lessons

Introduction & Overview

The problem with traditional keyword research

Who this course is for and what we’ll cover

Fundamentals of Semantic Keyword Research

Entities, Entity Attributes, Entity Attribute Variables (EAV Model)

Practical/Lab – How to use the Google Natural Language API for Query Entity Extraction and Analysis

Search Query Sequences and Query Path

Practical/Lab – How to work with Google’s Autocomplete API to uncover Google-suggested query paths

Query Understanding and Analysis

Query Augmentation

Query Context and Session Context

Implicit User Feedback and User Search Behaviour

Understanding the SERP – Theory and practice

SERP Feature Analysis

Practical/Lab – How to work with dataforSEO and other SaaS for SERP collection (programmatic and no-code approaches)

Practical/Lab – How to identify desired content formats and platforms served from SERP data

Search Intent – Theory and Practice

Search Intent

Practical/Lab – Methods for explicit search intent classification (rule-based, and AI/ML-enabled)