docs: Add course content for overview page

qdrant-landing/content/course/essentials/_index.md

@@ -1,105 +1,204 @@
 ---
 title: Qdrant Essentials Course
 page_title: Qdrant Essentials Course
-description: The ultimate guide to production-grade vector search is here. And it’s free.
+description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
 content:
   sidebarTitle: Qdrant Essentials
   menuTitle:
     text: Course Overview
     url: /course/essentials/
-  getStarted:
-    text: Get Started
-    url: /course/essentials/day-0/
   nextButton: Continue to Next Video
   nextDay: Complete
-  title: Qdrant Essentials Course
-  description: The ultimate guide to production-grade vector search is here. And it’s free.
+  title: Qdrant Essentials
+  description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
 partition: course
 ---
 
-# Qdrant Essentials Course
-
-The ultimate guide to production-grade vector search is here. And it’s free.
+# Qdrant Essentials
+
+**Ship a production-ready docs search in 7 days**
+
+Build the vector search skills that matter: hybrid retrieval, multivector reranking, quantization, distributed deployment, and multitenancy. Ship a complete documentation search engine as your final project.
+
+<div class="video">
+<iframe 
+  src="https://www.youtube.com/embed/QnRjMolv8Qk?si=uqWQLcLp_oBWt3bO"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen>
+</iframe>
+</div>
+
+<br/>
+
+{{< cards-list >}}
+- icon: /icons/outline/play-white.svg
+  title: 7 days of lessons
+  content: Short, focused videos with hands‑on exercises
+- icon: /icons/outline/cloud-check-blue.svg
+  title: Shareable certificate
+  content: Earn a digital certificate upon completion
+- icon: /icons/outline/time-blue.svg
+  title: Flexible schedule
+  content: Learn at your own pace (1–2 hours/day)
+- icon: /icons/outline/plan.svg
+  title: Beginner level
+  content: No prior Qdrant experience required
+
+{{< /cards-list >}}
 
-From your first vector upsert to optimizing high-performance retrieval at scale, this free course takes you from zero to production-ready. Learn how to build efficient vector search, fine-tune Qdrant for maximum performance, and keep your system lightweight, even when working with billions of vectors.
+<br/>
 
+## What you'll learn
 {{< course-card
-title="Skills you’ll gain:"
-image="/icons/outline/training-white.svg"
-isWideList="true">}}
-- Vector search fundamentals
-- Performance optimization
-- Hybrid and similarity search
-- Portfolio project development
-{{< /course-card >}}
+ title="Skills you'll gain:"
+ image="/icons/outline/training-white.svg"
+ type="wide-list">}}
 
-## What Is the Course?
+- Qdrant data modeling: points, payloads, and schemas
+- Embeddings, chunking, and similarity metrics
+- Indexing and retrieval tuning ([HNSW](https://qdrant.tech/articles/filtrable-hnsw/), filters, recall/latency)
+- Hybrid search with sparse + dense vectors and re-ranking
+- Performance optimization, compression, and quantization
+- Scaling, sharding/replication, and security
 
-No matter if you're exploring vector search for the first time or fine-tuning a large-scale RAG system, this free course gives you the practical foundation and advanced skills you need.
+{{< /course-card >}}
 
-Over 9 days (plus bonus content), you’ll build up from the fundamentals to advanced deployment strategies with Qdrant. Each module focuses on a single concept or capability, paired with a hands-on exercise to apply what you’ve learned. You will start with basics, build confidence, and gradually progress to complex topics. 
+### The Path
 
-Every day includes a hands-on exercise or mini-project, like creating a collection, uploading points, building a hybrid search pipeline, or tuning the HNSW index.
+**Days 0–2**: Foundations. Connect to Qdrant Cloud, work with points and payloads, compute semantic similarity, chunk text, and tune HNSW for speed and recall.
 
-At the end, you’ll bring everything together by building a full production-grade vector search application. You’ll graduate with a portfolio-worthy project, plus a deep understanding of how to apply Qdrant in production scenarios.
+**Days 3–5**: Advanced retrieval. Combine dense and sparse signals, do hybrid search with server-side fusion, use multivectors (ColBERT) with the Universal Query API, and build recommendations.
 
-## Course Overview
+**Day 6**: Ship. Wire ingestion, hybrid retrieval, multivector re-ranking, and evaluation (Recall@10, MRR, latency P50/P95).
 
-{{< accordion >}}
-- title: "Days 0: Setup, Orientation & “Hello Qdrant!”"
-  content: |
-    - Welcome & Course Orientation
-    - Environment Setup
-    - Mini “Hello Qdrant!” Demo
-
-- title: "Day 1: Core Qdrant Data Model & Vector Search 101"
-  content: Content
+**Day 7 (bonus)**: Ecosystem. Try integrations with AI frameworks, search tools, and data pipelines.
 
-- title: "Days 2: Indexing & Vector Storage Architecture"
-  content: Content
+## How the course works
 
-- title: "Day 3: Hybrid Search"
-  content: Content
+{{< cards-list >}}
 
-- title: "Day 4: Optimizations & Query APIs"
-  content: Content
-{{< /accordion >}}
+- icon: /icons/outline/training-purple.svg
+  title: Video-first lessons
+  content: Clear, concise modules by the Qdrant team
+- icon: /icons/outline/hacker-purple.svg
+  title: Final project
+  content: Ship a production-ready vector search app
+- icon: /icons/outline/similarity-blue.svg
+  title: Bonus day
+  content: Explore partner integrations on Day 7
+- icon: /icons/outline/copy.svg
+  title: Pitstop projects
+  content: Small builds each day to apply the concept
+  {{< /cards-list >}}
 
-## Certificate of Completion
+<br/>
 
-image
+## Syllabus
 
-## Who Is the Course For?
+{{< accordion >}}
+- title: "Day 0: Setup and First Steps"
+  content: |
+    - Qdrant Cloud Setup
+    - Implementing a Basic Vector Search
+    - Project: Building Your First Vector Search System
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-0/">→ Start Day 0</a></p>
+
+- title: "Day 1: Vector Search Fundamentals"
+  content: |
+    - Points, Vectors and Payloads
+    - Distance Metrics
+    - Text Chunking Strategies
+    - Demo: Semantic Movie Search
+    - Project: Building a Semantic Search Engine
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-1/">→ Start Day 1</a></p>
+
+- title: "Day 2: Indexing and Performance"
+  content: |
+    - HNSW Indexing Fundamentals
+    - Combining Vector Search and Filtering
+    - Demo: HNSW Performance Tuning
+    - Project: HNSW Performance Benchmarking
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-2/">→ Start Day 2</a></p>
 
-You! But really, this course is great for hands-on professionals who need to build or improve applications with semantic or hybrid search capabilities, or developers exploring vector databases for the first time.
+- title: "Day 3: Hybrid Search"
+  content: |
+    - Sparse Vectors and Inverted Indexes
+    - Demo: Keyword Search with Sparse Vectors
+    - Hybrid Search with Score Fusion
+    - Demo: Implementing a Hybrid Search System
+    - Project: Building a Hybrid Search Engine
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-3/">→ Start Day 3</a></p>
+
+- title: "Day 4: Optimization and Scale"
+  content: |
+    - Vector Quantization Methods
+    - Accuracy Recovery with Rescoring
+    - High-Throughput Data Ingestion
+    - Project: Quantization Performance Optimization
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-4/">→ Start Day 4</a></p>
+
+- title: "Day 5: Advanced APIs"
+  content: |
+    - Multivectors for Late Interaction Models
+    - The Universal Query API
+    - Demo: Universal Query for Hybrid Retrieval
+    - Project: Building a Recommendation System
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-5/">→ Start Day 5</a></p>
+
+- title: "Day 6: Final Project - Building a Production-Grade Search Engine"
+  content: |
+    - Project Architecture and Evaluation Framework
+    - Implementation and Performance Evaluation
+    - Course Summary and Next Steps
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-6/">→ Start Day 6</a></p>
+
+- title: "Day 7: Partner Ecosystem Integrations (Bonus)"
+  content: |
+    - AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
+    - Data Processing (Unstructured.io)
+    - ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/essentials/day-7/">→ Start day 7</a></p>
+{{< /accordion >}}
 
-If your job title includes:
 
-- Machine Learning Engineer
-- Backend Developer
-- Data Engineer
-- Search Engineer
-- MLOps Engineer
+## Who it's for
 
-you’re in the right spot.
+ML, backend, data, and search engineers building RAG, semantic search, or recommendations. Requires intermediate Python, basic CLI/APIs, and familiarity with embeddings.
 
-## Pre-Reqs
+## Time commitment
 
-You don’t need prior Qdrant or vector database experience, but you should be comfortable with:
-- Basic Python programming
-- Running commands in your terminal
-- Working with APIs or Python SDKs
-- Some ML background (e.g., embeddings)
+- Duration: 7 days at 1–2 hours/day + optional bonus day
+- Video learning: ~3 hours
+- Hands-on learning: 4-5 hours
+- Final project: 2–4 hours
+- Total: 9–12 hours
 
-Optional but helpful:
-- Docker basics
-- Experience with search systems or deploying applications
 
 {{< course-card 
-title="Why Start Today"
-image="/icons/outline/rocket-white-light.svg" 
-link="/course/day-0/">}}
-- Seeing practical examples (e.g., hybrid search, sparse+dense vectors)
-- Learning key deployment tactics (multi-node clusters, on-disk indexing, RBAC)
-- Building a final portfolio-grade project to showcase
-{{< /course-card >}}
+ title="Ready to start your vector search journey?"
+ image="/icons/outline/rocket-white-light.svg" 
+ link="/course/essentials/day-0/">}}
+**What you’ll get**
+- Build a production-ready docs search engine
+- Practice with real projects
+- Learn performance tuning techniques
+- Portfolio artifacts and community support
+{{< /course-card >}}

qdrant-landing/content/course/essentials/certification.md

@@ -1,6 +1,8 @@
 ---
-title: Certification
+title: Qdrant Essentials Certification
 url: /course/certification/
 ---
 
-# Certification
+# Qdrant Essentials Certification
+
+Coming soon!

qdrant-landing/content/course/essentials/faq.md

@@ -1,9 +1,9 @@
 ---
-title: FAQ
+title: Qdrant Essentials FAQs
 url: /course/faq/
 ---
 
-# Qdrant Essentials Course FAQs
+# Qdrant Essentials FAQs
 
 ## Who is this course for?
 
@@ -15,29 +15,31 @@ Yes, completely free. No credit card required.
 
 ## Do I get a certificate of completion? 
 
-Yes! But just watching the videos doesn’t quite get you there. You need to [[insert instructions]](/#) to obtain the certification. And, you’ll also have your project to add to your portfolio!
+Certification coming soon!
 
 ## How much time should I spend on this course?
 
-Each “Day” of this course is designed to be completed in 1 day. Day 0 is the quickest, and then there are 8 days of content. So you are looking at a minimum of 9 days, but don’t forget about the bonus content.
+Each "Day" is designed to be completed in 1 day. Day 0 is the quickest, followed by 5 more days of content. Plan for at least 6 days, plus bonus content.
 
-The good news is that you can take as much time as you need to complete the course since it is on-demand and at your own pace.
+The good news: you can take as much time as you need since the course is on-demand and self-paced.
 
 ## What tools do I need?
 
-Python, Docker, and optionally Qdrant Cloud. We walk you through setup on Day 0.
+Python and either Qdrant Cloud or Docker. We walk you through setup on Day 0.
 
 ## Can I use Qdrant locally or do I need a cloud account?
 
 You can do either. We support both local (via Docker) and Qdrant Cloud setups.
 
 ## What if I get stuck or have a question?
 
-Join the Qdrant Discord. Our team and community are ready to help.
+Join the Qdrant Discord <a href="https://discord.com/invite/qdrant" target="_blank" rel="noopener noreferrer" aria-label="Qdrant Discord"> <img src="https://img.shields.io/badge/Qdrant%20Discord-5865F2?style=flat&logo=discord&logoColor=white&labelColor=5865F2&color=5865F2"
+    alt="Post your results in Discord"
+    style="display:inline; margin:0; vertical-align:middle; border-radius:9999px;" /> </a>. Our team and community are ready to help.
 
 ## Where can I get the code for the course? 
 
-Look at each Day’s page of content. You can also find them in this repo.
+Check each Day’s page of content.
 
 {{< course-card
 title="Why Start Today"