Project-based learning
Build real work you can show in interviews.
Course details
Machine Learning
Train models that learn from data and predict real outcomes.
Taught by Ananya Krishnan · ML Mentor
CertificateIncluded on completion
Lifetime access
Revisit lessons anytime after you enroll.
Mentor support
Learn with guidance from industry experts.
Career-ready skills
Practical modules aligned to real roles.
Overview
What this course covers
Learn how machines find patterns in data and turn them into predictions. From regression to ensemble models and neural networks, you will build, evaluate, and tune real models using Python and scikit-learn.
Outcome
Finish with practical skills and a portfolio-ready project in Machine Learning you can use for jobs, freelancing, or your next role.
Skills
What you'll learn
Supervised and unsupervised learning
Regression, classification & clustering
Feature engineering and model evaluation
Decision trees, random forests & boosting
Intro to neural networks
Deploying models to production
Audience
Who this course is for
Beginners
Starting Machine Learning from scratch and want a clear path.
Students & grads
Building a portfolio and interview-ready skills.
Working pros
Upskilling into ai roles with practical projects.
Before you start
Prerequisites
- Basic computer and internet skills
- Curiosity and consistency to practise weekly
- Open to learners of every background
Syllabus
Curriculum overview
8 modules · week-by-week topics · labs · certificate on completion
01Week 1: ML Foundations & Workflow
Hands-on lessons and a guided exercise covering ml foundations & workflow.
Topics covered
- Concept overview
- Workflow steps
- Evaluation metrics
- Mini project
Lab: Practice lab: ML Foundations & Workflow
02Week 2: Data Preprocessing & Features
Hands-on lessons and a guided exercise covering data preprocessing & features.
Topics covered
- Core concepts
- Hands-on practice
- Real dataset / case
- Review checkpoint
Lab: Practice lab: Data Preprocessing & Features
03Week 3: Regression Models
Hands-on lessons and a guided exercise covering regression models.
Topics covered
- Concept overview
- Workflow steps
- Evaluation metrics
- Mini project
Lab: Practice lab: Regression Models
04Week 4: Classification Models
Hands-on lessons and a guided exercise covering classification models.
Topics covered
- Concept overview
- Workflow steps
- Evaluation metrics
- Mini project
Lab: Practice lab: Classification Models
05Week 5: Clustering & Dimensionality Reduction
Hands-on lessons and a guided exercise covering clustering & dimensionality reduction.
Topics covered
- Concept overview
- Guided walkthrough
- Hands-on exercise
- Checkpoint review
Lab: Practice lab: Clustering & Dimensionality Reduction
06Week 6: Ensemble Methods
Hands-on lessons and a guided exercise covering ensemble methods.
Topics covered
- Concept overview
- Guided walkthrough
- Hands-on exercise
- Checkpoint review
Lab: Practice lab: Ensemble Methods
07Week 7: Neural Networks Intro
Hands-on lessons and a guided exercise covering neural networks intro.
Topics covered
- Concept overview
- Workflow steps
- Evaluation metrics
- Mini project
Lab: Practice lab: Neural Networks Intro
08Week 8: Model Deployment Project
Hands-on lessons and a guided exercise covering model deployment project.
Topics covered
- Tooling setup
- Pipeline / workflow
- Monitoring basics
- Ship exercise
Lab: Practice lab: Model Deployment Project
After the course
You'll walk away ready
By completing this course, you will have the practical skills and a portfolio project in Machine Learning to confidently move forward in your career.
- Job-ready ai fundamentals
- A completed capstone you can showcase
- Certificate of completion from Syncpedia
Faculty
Your course mentor
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