📘 Complete Syllabus Coverage

Fundamentals of
Machine Learning

An interactive, visually stunning educational platform designed to help you master ML concepts — from basic foundations to Python implementations.

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🎯 What is FML?

Fundamentals of Machine Learning (FML) is a comprehensive course covering the core principles of how machines learn from data. This platform takes you from the very basics — understanding what ML is and where it's used — all the way to implementing algorithms in Python.

🧠 Conceptual Foundation

  • Human vs Machine Learning
  • Types: Supervised, Unsupervised, Reinforcement
  • Real-world applications
  • Data preprocessing & quality

⚙️ Practical Mastery

  • Model selection & evaluation
  • Classification & Regression algorithms
  • Clustering & Association rules
  • Python libraries: Pandas, NumPy, Scikit-learn

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📚 Course Units

Unit I

Introduction to Machine Learning

Discover what ML is, its types, applications, and the tools that power it.

  • Human Learning vs Machine Learning
  • Supervised, Unsupervised & Reinforcement
  • Real-world Applications
  • Tools & Technologies
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Unit II

Preparing to Model

Learn about ML activities, data types, quality, preprocessing, and feature selection.

  • ML Activities & Lifecycle
  • Types & Structures of Data
  • Data Quality & Preprocessing
  • Dimensionality Reduction
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Unit III

Modeling & Evaluation

Select, train, and evaluate ML models with hands-on calculators.

  • Predictive vs Descriptive Models
  • Holdout & K-Fold Cross-Validation
  • Confusion Matrix & Metrics
  • Improving Model Performance
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Unit IV

Supervised Learning

Master classification & regression with KNN, SVM, and linear/logistic regression.

  • Classification vs Regression
  • K-Nearest Neighbor (KNN)
  • Support Vector Machine (SVM)
  • Linear & Logistic Regression
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Unit V

Unsupervised Learning

Explore clustering with K-Means and pattern finding with Apriori algorithm.

  • Supervised vs Unsupervised
  • K-Means Clustering Algorithm
  • Cluster Visualization
  • Apriori & Association Rules
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Unit VI

Python Libraries for ML

Hands-on with Pandas, NumPy, Matplotlib, and Scikit-learn.

  • Pandas — Data Manipulation
  • NumPy — Numerical Computing
  • Matplotlib — Visualization
  • Scikit-learn — ML Workflows
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