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Data Science Intermediate Live Classes

Applied Machine Learning

Hands-on machine learning specialization covering the full modeling workflow from feature engineering to deployment, with real datasets and projects.

4.8 820 enrolled 16 weeks · 120+ live learning hours Certificate
Overview
Curriculum
Instructor
FAQ

Program Overview

A practical specialization for practitioners who want to build and ship machine learning models with confidence. Across 16 weeks and 120+ live hours you master the end-to-end ML workflow: feature engineering, model selection, tuning, evaluation and deployment, working on real datasets and multiple projects.

What you'll learn

  • Engineer features and prepare data for modeling
  • Train and tune regression and classification models
  • Use ensembles and gradient boosting effectively
  • Evaluate, interpret and validate models
  • Deploy a model as an API

Tools you'll use

Pythonscikit-learnXGBoostPandasMLflowDocker

Hands-on Projects

  • Predictive pricing model
  • Deployed classification API

Career Outcomes

Machine Learning EngineerData ScientistML Practitioner

Complimentary software included

Enroll in this course and get these professional tools free — licenses and access are provided as part of the program, at no extra cost.

Anaconda Python Data Platform

Python and ML libraries.

scikit-learn ML Framework

Core machine learning library.

MLflow MLOps

Experiment tracking.

Google Colab Credits Compute

Notebooks for training.

Curriculum

ML Workflow & Data Prep
  • Pipeline
  • Data prep
Regression & Regularization
  • Linear models
  • Regularization
Classification & Metrics
  • Classifiers
  • Metrics
Trees, Random Forests & Boosting
  • Trees
  • XGBoost
Feature Engineering & Selection
  • Feature engineering
  • Selection
Model Tuning & Validation
  • Cross-validation
  • Hyperparameters
Deployment & Applied Project
  • Model API
  • Capstone

Dr. Priya Nair

Lead Data Scientist

10+ years building ML systems at scale.

Certificate

Verified specialization certificate with project review.

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