Master predictive modeling & production MLOps workflows. — Machine Learning Course in Coimbatore at AIRA Global Tech
Deep dive into statistical learning theory, supervised/unsupervised algorithms, neural networks, feature engineering pipelines, model monitoring, and scalable MLOps deployment.
Get Course Syllabus & Fee Details
How You'll Progress Through This Program
ML Foundations
Supervised, unsupervised and core machine-learning concepts.
Data Preparation
Preprocessing, feature engineering and dataset preparation.
Model Development
Regression, classification, clustering and model selection.
Evaluation & Optimization
Validation, metrics, hyperparameter tuning and model improvement.
ML Projects
Build complete machine-learning projects using real datasets.
A curriculum built around real engineering
Mathematical Foundations & Statistical Learning
Linear algebra, multivariable calculus, loss function optimization, gradient descent, and probability.
What You Will Learn
- Matrix decompositions, eigenvalues, and gradient vectors
- Cost functions: Mean Squared Error, Binary Cross-Entropy, Log-Loss
- Gradient descent variants: Batch, Stochastic (SGD), and Adam
- Bias-variance tradeoff, overfitting, and underfitting diagnostics
- Regularization techniques: Ridge (L2), Lasso (L1), and ElasticNet
Implement linear regression and gradient descent from scratch in pure NumPy without Scikit-Learn.
How the engineering stack connects
You won't learn isolated tools. You'll understand how databases, APIs, client interfaces, and cloud deployments operate as a unified system.
Scikit-Learn
Comprehensive Python toolkit for classical machine learning algorithms
XGBoost & LightGBM
High-efficiency gradient boosted decision tree implementations
PyTorch
Modern deep learning library for neural network training and inference
MLflow
Platform for managing the end-to-end machine learning lifecycle
Optuna
Hyperparameter optimization framework utilizing Bayesian search
FastAPI
Web framework for serving low-latency model inference APIs
Docker
Containerized environments ensuring model reproducibility
Evidently AI
Evaluation and monitoring tool for detecting data and prediction drift
Train. Evaluate. Improve.
Design mathematical models with rigorous validation, hyperparameter tuning, and production pipeline deployment.
Reliable Training Data
Target leakage prevention, feature scaling, categorical encoding, and class imbalance mitigation strategies.
Robust Evaluation
Rigorous train-test splits, cross-validation scoring, confusion matrix analysis, and F1-score optimization.
Optimization & Improvement
Bayesian hyperparameter tuning, gradient boosting, ensemble voting, and model inference latency profiling.
Capstone projects you will ship
Concrete projects solving authentic industry requirements.
Real-Time Credit Card Fraud Detection
High-speed fraud classification pipeline analyzing transactions with sub-10ms response latency.
SMOTE sampling with asymmetric cost-sensitive loss optimization.
End-to-End MLOps Training & Deployment Pipeline
Automated continuous training pipeline tracking model experiments, versioning weights, and deploying to cloud.
Automated canary deployment with rollback fallback triggers.
Industrial Equipment Predictive Maintenance
Time-series predictive maintenance model forecasting mechanical component failures before downtime occurs.
Rolling window feature engineering capturing vibration degradation trends.
Target industry roles
Machine Learning Engineer
Designs, trains, tests, and deploys high-performance machine learning models.
MLOps Engineer
Builds automated ML pipelines, model registries, monitoring systems, and cloud infrastructure.
Applied AI Specialist
Implements state-of-the-art machine learning algorithms to solve domain-specific enterprise problems.
All-Inclusive Program Fee
Complete industry curriculum, live 1:1 mentorship, capstone project evaluations, and placement support.
Machine Learning
3 Months structured curriculum with all project labs & mentorship.
Frequently Asked Questions
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