Course Curriculum
Machine LearningComplete Syllabus & Module Breakdown
Full Curriculum
Machine Learning — Complete Syllabus
5 modules · all topics listed · last updated 2026
ML Foundations
- Types of Machine Learning
- Python for ML
- Linear Algebra & Calculus Review
- Data Preprocessing
Supervised Learning
- Regression Algorithms
- Classification Algorithms
- Ensemble Methods
- Model Tuning
Unsupervised & Reinforcement Learning
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Q-Learning Basics
Deep Learning
- Neural Network Architecture
- Backpropagation
- CNNs & RNNs
- Transfer Learning
MLOps
- Model Versioning with MLflow
- Feature Stores
- Model Monitoring
- Deployment Pipelines
Frequently Asked Questions
What is the prerequisite for this course?
Python basics and a fundamental understanding of statistics are recommended. We cover advanced concepts from there.
How is this different from the Data Science course?
This course goes deeper into ML algorithms, model optimization, and production deployment (MLOps) — ideal for those targeting ML Engineer roles.
Will I learn PyTorch?
Yes. Both TensorFlow and PyTorch are covered. PyTorch is increasingly dominant in research and production.
What is MLOps?
MLOps is the practice of deploying, monitoring, and maintaining ML models in production — a critical skill for ML engineers.
What salary do ML engineers earn?
ML engineers are among the highest-paid tech professionals — ₹8–15 LPA entry-level, ₹20–40 LPA with experience.
Machine Learning
Tools You'll Master
Career Outcomes
- ✓ML Engineer
- ✓Data Scientist
- ✓AI Engineer
- ✓Research Scientist
- ✓Deep Learning Engineer
- ✓MLOps Engineer
Who Should Attend
- Data scientists wanting ML engineering depth
- Software engineers moving into AI/ML
- CS graduates targeting AI companies
- Researchers transitioning to industry