Machine Learning with Python and scikit-learn (ML): Models, Evaluation and Deployment

Name: Machine Learning with Python and scikit-learn (ML): Models, Evaluation and Deployment
Price: 19999 INR

Learn machine learning live: regression, classification, clustering, feature engineering, model evaluation, ensembles, XGBoost, time series, explainability and deploying a model with FastAPI.

Live batches with a trainer · Level: Intermediate · Duration: 50 hours · Language: English · Fee: ₹19,999

About this course

Machine learning is how companies predict churn, demand, fraud, prices and risk, and ML engineer and data scientist are among the best-paid roles in tech. This course teaches the models, the maths behind them in plain language, and the engineering of a model that works in production.

The course starts with the ML workflow and the maths you need (linear algebra, statistics and gradient descent, explained with code), then supervised learning: linear and logistic regression, decision trees, random forests, gradient boosting with XGBoost and LightGBM, support vector machines, k-nearest neighbours and naive Bayes, with feature engineering, scaling, encoding, pipelines, cross-validation, metrics and hyperparameter tuning. You then learn unsupervised learning (k-means, hierarchical and DBSCAN clustering, PCA and anomaly detection), recommendation systems and time series forecasting.

You then make models trustworthy and useful: imbalanced data, explainability with SHAP, bias and fairness, experiment tracking with MLflow, and deployment as an API with FastAPI and Docker, with an introduction to neural networks and when to move to deep learning. The course ends with two portfolio projects and preparation for ML interviews and Kaggle-style case rounds. Every topic is coded live, and every session is recorded.

What you will learn

Run the machine learning workflow end to end: problem framing, data, features, model, evaluation and deployment
Understand the maths behind the models in plain language: linear algebra, statistics and gradient descent
Build supervised models: linear and logistic regression, trees, random forests, XGBoost, SVM, k-NN and naive Bayes
Engineer features, build scikit-learn pipelines, cross-validate and tune hyperparameters
Evaluate models with the right metrics and handle imbalanced data
Use unsupervised learning: clustering, PCA and anomaly detection, and build a recommender
Forecast time series with classical and ML methods
Explain models with SHAP, track experiments with MLflow and deploy a model as an API with FastAPI and Docker
Finish two portfolio projects and prepare for ML interviews

Requirements

Python basics (variables, loops, functions, Pandas) and school-level maths; the Python course covers this
Best suited to Python developers, data analysts, engineering and science graduates and professionals moving to ML roles
A laptop with a stable internet connection (8 GB RAM is enough; cloud notebooks are used for heavy work)

Syllabus

Module 1: ML foundations (4 hours)
Module 2: Regression (6 hours)
Module 3: Classification (8 hours)
Module 4: Feature engineering and pipelines (6 hours)
Module 5: Unsupervised learning (5 hours)
Module 6: Time series (4 hours)
Module 7: Trustworthy ML (4 hours)
Module 8: Deployment (5 hours)
Module 9: Projects and interviews (8 hours)

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