Level: Beginner
🤖 Unsupervised Machine Learning with Python and Scikit Learn
Learn how machines uncover hidden patterns without labeled data. Build clustering and dimensionality reduction models using Python, work on real datasets, and understand when unsupervised learning shines.
4.0
2.2k+ learners
Duration
9 hr 5 min
Learners
2.2k+ Enrolled Students
Session Recording
Lifetime Access
Post Session
Mentor support
Session Schedule
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₹499
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Course Fee
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Worth of
₹499
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Includes
• Expert Designed Curriculum
• Doubt Clearing Session
• Forever Community Access
After this course, you can
Apply clustering algorithms
Discover hidden data patterns
Reduce data dimensions
Analyze unlabeled datasets
Course Curriculum
Expand or explore the full learning path.
Lessons
Start here after enrolling. These lessons form the core learning path for the course.
What Unsupervised Learning Is (And How It's Different)
What "A Pattern" Actually Means to a Machine
Distance and Similarity: How Machines Measure "Close"
Feature Scaling: The Silent Result-Wrecker
The Two Big Jobs: Clustering vs Dimensionality Reduction
Judging Results When There Is No Right Answer
The Core Idea: Centroids, Assignment, and Moving the Middle
K-Means By Hand: Watching the Flags Crawl Into Place
Choosing k: The Elbow Method and the Silhouette Score
Where K-Means Quietly Fails
K-Means++ and Why Good Starting Points Matter
Real Project: Segmenting Customers Into Groups That Make Sense
Hierarchical Clustering: Building a Family Tree of Your Data
Reading a Dendrogram and Cutting It at the Right Height
Linkage Methods: Single, Complete, Average, and Why They Change Everything
DBSCAN: Clustering by Density Instead of Distance to a Center
Eps and minPts: DBSCAN's Two Knobs (and Free Outlier Detection)
Picking the Right Clustering Tool for the Shape of Your Data
The Curse of Dimensionality, Explained Without the Scary Math
PCA: Finding the Directions That Matter Most
Applying PCA: Variance Explained and How Many Components to Keep
T-SNE: Squeezing High-Dimensional Data Into a 2D Map
UMAP: Faster Than t-SNE, and Often Keeps Structure Better
PCA vs t-SNE vs UMAP: When to Reach for Which One
What Counts as an Anomaly, and Why It's Trickier Than It Sounds
Simple Statistical Methods: Z-Scores and the IQR Rule
Distance and Density Based Detection (Your Clustering Skills Return)
Isolation Forest: Catching Outliers by Isolating Them
A Real Project: Fraud Detection End to End
Shipping It: Deploying and Monitoring an Anomaly Detector
Live Session Content
This topic will be taught live in an interactive session, allowing you to engage with the instructor and ask questions in real time. While recordings will be available, attending live offers the best learning experience.
Big data concepts and challenges
30 min
Distributed data processing overview
30 min
Working with distributed file systems
25 min
Designing batch processing workflows
30 min
ETL at scale using Spark
30 min
Handling large dataset transformations
25 min
Real-time streaming fundamentals
30 min
Kafka-based streaming pipelines
30 min
Stream processing with Spark Streaming
25 min
Designing scalable big data architectures
30 min
Performance optimization strategies
25 min
End-to-end big data pipeline implementation
30 min
Post Session Study Material
This section will be unlocked after the session. You'll get access to exclusive bonus content, additional examples, and on-demand resources to support continuous learning and deeper understanding.
Real-time ride tracking system walkthrough
35 min
Log processing system case study
35 min
IoT sensor data platform architecture
30 min
Scaling streaming systems in production
25 min
Build and deploy a real-time streaming pipeline
50 min
Design batch and streaming architecture diagram
30 min
Requirements
What you should have before starting the course.
Basic Python programming knowledge
Familiarity with machine learning
Understanding of data analysis basics
A computer with Python installed
Tools you are going to use
Apache Kafka
Spark
Hadoop
AWS
Python
Docker
Learn From Expert Instructor
Meet the mentors leading the live sessions.
Aarav Sharma
Senior Frontend Engineer, StudioLabs
Aarav has mentored 1200+ learners and specializes in production UI systems and scalable frontend architecture.
LinkedIn
Ananya Mehta
Design Technologist, CraftLabs
Ananya blends design and code to help learners ship portfolio-ready projects.
LinkedIn
Kabir Sayed
Frontend Mentor, PixelWorks
Kabir focuses on clean code, accessibility, and practical frontend delivery.
LinkedInYou should join this course if you
Students & Fresh Graduates
Build a strong portfolio
Learn from live mentors
Get feedback on your work
Working Professionals
Upgrade frontend skills
Ship real projects
Join a peer community
Career Switchers
Structured learning path
Hands-on practice
Guided sessions
Freelancers & Creators
Build client-ready sites
Improve delivery speed
Learn modern workflows
What you will get after completing this course
Receive an official course completion certificate
Earn skill-focused feedback from mentors
Showcase your project in the Shattak community
Access lifetime notes and references
Stay connected with instructors for guidance
Completion bonus
Certificate access, community showcase, and lifetime mentor guidance.
Hear From Learners Who've Taken This Course
Honest feedback from learners who completed the live sessions.
4.8
24
Clear structure, solid examples, and a fast pace that keeps you engaged.
Riya Jain
B.Tech, NIT Trichy
4.6
18
Practical sessions with feedback that helped improve my portfolio.
Kabir Singh
Frontend Intern, PixelWorks
4.9
12
Loved the structure and the live walkthroughs.
M
Meera Patel
Design Graduate
Join Our Community, Ask Questions
Frequently Asked Questions
Students, working professionals, and career switchers who want to build job-ready web projects.
Basic familiarity with HTML or CSS is helpful, but the course includes beginner-friendly guidance.
Yes, recordings are shared after each live session.
Mentors provide live feedback and follow-up notes during the sessions.
Yes, you can ask questions in the community group and during live office hours.
Shattak courses are primarily self-paced and may include lessons, recordings, assignments, projects, and additional learning resources. Live sessions may also be conducted based on community demand.
Yes. If a live session is conducted for your course, you can access the recording and revisit the content at your own pace.
Yes. Shattak has a learning community where you can ask questions, interact with other learners, share your progress, and get guidance from mentors and peers.
Not always. Each course clearly mentions its requirements and the level of experience needed. Many courses are designed for complete beginners.
Yes. You can earn a course completion certificate after completing the required learning materials and submitting the required assignments or projects.
Yes. You can add your certificate to your LinkedIn profile, resume, portfolio, and other places where you want to showcase your learning and achievements. You may also use it for academic or professional purposes where applicable.
All Shattak courses are free to enroll in and start learning. After completing the course and earning your certificate, you can choose how much to pay from ₹0 to ₹10,000. Pay what you feel the learning experience was worth, with no questions asked.
You can explore courses by browsing different topics, skills, categories, or career paths. Each course page provides information about what you will learn, requirements, curriculum, and completion benefits.
Yes. Before enrolling, you can explore the course overview, curriculum, learning outcomes, requirements, and other details to understand what the course offers.
Simply open the course page and click Enroll. All courses are free to enroll in, so you can start your learning journey without paying upfront.
After enrolling, you will be redirected to your course workspace. From there, you can access your lessons and follow the learning path step by step.
Most courses allow you to learn at your own pace. However, some assignments, live sessions, or certification requirements may have specific deadlines.
Depending on the course, you may get access to: • Self-paced lessons • Live sessions based on community demand • Session recordings • Assignments and hands-on projects • Bonus learning resources • Mentor and peer community support • Doubt-clearing and guidance • Course completion certificates
You can ask questions through the learning community and connect with other learners. Depending on the course, you can also receive guidance from mentors and peers.
After completing the required lessons, assignments, and other course requirements, you can submit your work for certification. Once certified, you can access and share your certificate.
If you are an industry professional or expert and want to teach on Shattak, you can apply to become a mentor and create a course or learning experience based on your expertise.
Starting From
TBD
Duration
9 hr 5 min
Get Life Time Access
₹0
₹499
100% off
Get Life Time Access
₹0
₹499
100% off