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Core Foundation Course

Fundamentals of Machine Learning

Master the essential concepts and algorithms that form the backbone of machine learning. From supervised and unsupervised learning to advanced ensemble methods, learn to build, evaluate, and deploy robust ML models. Covers regression, classification, clustering, feature engineering, regularization techniques, and practical model deployment strategies.

Supervised & Unsupervised Learning
Ensemble Learning Methods
Model Evaluation & Metrics
Model Deployment Basics

This comprehensive course provides hands-on experience with real datasets, covering everything from basic ML workflows to advanced techniques like bias-variance tradeoff, regularization, and ensemble methods, culminating in a complete supervised learning project deployment.

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Fundamentals of Machine Learning Course

Course Overview

Key Features

  • Complete 10-Lesson ML Foundation covering supervised, unsupervised, and ensemble methods
  • Practical ML Workflow Training from data preparation to model deployment
  • Comprehensive Algorithm Coverage including regression, classification, and clustering techniques
  • Industry-Standard Evaluation Methods with cross-validation and performance metrics
  • Production-Ready Skills including model deployment, API creation, and serving frameworks
  • End-to-End Capstone Project building, evaluating, and deploying a complete ML solution

Skills You'll Master

  • ML Algorithm Implementation: Linear/polynomial regression, logistic regression, and decision trees
  • Clustering Techniques: K-means, hierarchical clustering, and DBSCAN for unsupervised learning
  • Model Evaluation: Accuracy, precision, recall, F1-score, ROC curves, and cross-validation methods
  • Advanced Techniques: Bias-variance tradeoff, Ridge/Lasso regularization, and early stopping
  • Feature Engineering: Categorical encoding, feature scaling, selection, and dimensionality optimization
  • Ensemble Methods: Random forests, gradient boosting, and understanding bagging vs boosting approaches

Curriculum Details

Eligibility:
Basic Python programming and mathematics background
Prerequisites:
Statistics fundamentals and data manipulation skills
Duration:
10 comprehensive lessons + supervised learning capstone
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