Vizuara Books
Machine Learning Fundamentals
The core of ML, built from scratch.
A hands-on tour of classical machine learning, from perceptrons and logistic regression to linear regression and your first neural network. Every model is built by hand in Python with the math, the intuition, and the code, so nothing stays a black box.
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00Foundations of Machine Learning6 capsules
Foundations of Machine Learning — 6 chapters.
01What Is Machine Learning?concept14 min02Types of ML Models: Supervised, Unsupervised, and Beyondconcept13 min03The Six Steps of Any ML Projectconcept13 min04Setting Up Python and Running Your First Codecode13 min05Jupyter Notebooks, NumPy, and Scikit-learncode14 min06How a Model Learns from Dataintuition15 min01Linear Classifiers and the Perceptron7 capsules
Linear Classifiers and the Perceptron — 7 chapters.
07Linear Classifiers, Part 1: Drawing a Decision Boundaryconcept14 min08Linear Classifiers, Part 2: Weights, Bias, and Geometrymath13 min09The Random Linear Classification Algorithmintuition13 min10The Perceptron Explainedconcept14 min11Coding the Perceptron from Scratchcode15 min12The Perceptron Convergence Theoremmath14 min13When a Line Is Not Enough: The XOR Problemdeep-dive15 min02Features and Representation3 capsules
Features and Representation — 3 chapters.
14The Magic of Featuresintuition13 min15Feature Representation: Turning the World into Numbersconcept13 min16One-Hot Encoding for Categorical Datacode14 min03Logistic Regression and Optimization8 capsules
Logistic Regression and Optimization — 8 chapters.
17From Scores to Probabilities: The Sigmoidmath13 min18Logistic Regression from the Ground Upconcept14 min19Cross-Entropy Loss: Measuring Wrongnessmath13 min20The Gradient Descent Algorithmconcept17 min21Gradient Descent in Code: 1D and 2Dcode14 min22Training Logistic Regression from Scratchproject14 min23Introduction to Regularizationconcept14 min24Implementing Regularization for Logistic Regressioncode14 min04Linear Regression6 capsules
Linear Regression — 6 chapters.
25Linear Regression: Fitting a Line to Dataconcept12 min26Ordinary Least Squares: The Closed-Form Solutionmath15 min27Ridge Regression: Regularizing the Fitmath14 min28When Least Squares Breaks: Non-Invertible Matricesdeep-dive15 min29Stochastic Gradient Descentconcept13 min30Regression Recap for Interviewsconcept13 min05Neural Networks7 capsules
Neural Networks — 7 chapters.
31Neural Network Architecture: Neurons and Layersconcept14 min32Activation Functions: Where Nonlinearity Comes Frommath14 min33Backpropagation, Intuitivelyintuition13 min34Backpropagation: The Math Worked Throughmath14 min35Momentum in Gradient Descentconcept13 min36Hands-On: Training a Neural Network in Pythonproject15 min37Putting It All Together: From Perceptron to Deep Learningdeep-dive12 minRatings & reviews
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