← Back to the catalogueMachine Learning
Beginner to Advanced
Level
Classroom . Online . Hybrid
Mode
Course overview
A practical machine learning course built on scikit-learn and real business datasets. The course covers the supervised and unsupervised toolkit, the mathematics needed to reason about it, and the discipline around it: data splitting, leakage, cross-validation, metric choice and hyperparameter search.
Learners complete three predictive projects churn, price and spam and deploy one as an API.
Who it is for
- Graduates entering data roles
- Analysts moving from reporting to prediction
- Developers adding ML to products
- Students in engineering and statistics
Prerequisites
Basic Python and school-level mathematics. NumPy and Pandas are revised in Module 2.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
Deployed Prediction Service
Programme sheet
The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.
Learning outcomes
01
Frame a business question as a learning problem.
02
Train regression, classification and clustering models.
03
Tune models with grid and randomised search.
04
Serve a trained model behind an API.
05
Prepare, encode and scale features without leakage.
06
Select metrics that match the business cost of error.
07
Explain model behaviour with feature importance and SHAP.
Module structure
9 modulesWhat machine learning is . Reinforcement learning overview . Supervised vs unsupervised . Train, validation and test . Bias and variance . Overfitting
NumPy . Pandas . Matplotlib . Seaborn . Data loading . Vectorised operations
Module 03
Data Preparation
Missing values . Encoding . Feature engineering . Data leakage . Outliers . Scaling . Pipelines
Lab
Preprocessing Pipeline
Linear regression . Regularisation . Residual analysis . Polynomial regression . Loss functions . Metrics
Lab
House Price Prediction
Logistic regression . Naive Bayes . Support vector machines . K-nearest neighbours . Decision trees
Lab
Spam Detection . Class Imbalance
Bagging . Boosting . LightGBM . Random forest . XGBoost . Stacking
Lab
Customer Churn Prediction
Module 07
Unsupervised Learning
K-means . DBSCAN . Anomaly detection . Hierarchical clustering . Principal component analysis
Module 08
Evaluation and Tuning
ROC and AUC . Precision-recall . Grid and random search . Model selection
Module 09
Interpretability and Deployment
Feature importance . SHAP . Model cards . Pickle and joblib . FastAPI serving . Monitoring drift
Assessment & certification
Module assignments and labs
25%
Final project and review
40%
Learners who complete all modules, submit the final project and clear the review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio, and interview preparation is included in the closing sessions.
Career outcomes . Roles this programme prepares for
Machine Learning EngineerData ScientistPredictive Analytics AnalystPython Developer (ML)AI Engineer
