Learning Path
This learning path provides a structured approach to understanding machine learning concepts, from foundational principles to advanced models. Each model page follows a consistent structure with Overview, Interactive Demo, and Code Implementation sections to enhance your learning experience.
How to Use This Learning Path
Each model page in our platform follows a consistent three-part structure:
1. Overview
Comprehensive explanation of the model's theory, key concepts, and applications
2. Interactive Demo
Visual demonstrations where you can adjust parameters and see real-time effects
3. Code Implementation
Practical examples with executable code cells to reinforce your understanding
1. Foundations
Start with these fundamental concepts to build a solid understanding of machine learning basics.
Understand the theoretical foundations of machine learning, including bias-variance tradeoff, overfitting, and regularization.
Explore key concepts in the glossary →2. Regression Models
Begin with regression models to understand how to predict continuous values.
Start with linear regression to understand the core concepts of supervised learning, model fitting, and evaluation.
Explore Linear RegressionLearn how to model non-linear relationships by extending linear regression with polynomial features.
Explore Polynomial RegressionUnderstand regularization techniques to prevent overfitting in linear models.
Explore Ridge & Lasso Regression3. Classification Models
After understanding regression, move on to classification problems and models.
Learn about logistic regression, a fundamental classification algorithm that predicts binary outcomes.
Explore Logistic RegressionLearn about decision trees, a versatile and interpretable model for classification and regression.
Explore Decision TreesExplore SVMs to understand margin maximization and kernel methods for non-linear classification.
Explore Support Vector MachinesUnderstand how combining multiple decision trees can create a more powerful and robust model.
Explore Random Forests4. Deep Learning
Once you're comfortable with traditional machine learning models, advance to deep learning.
Learn about neural networks, backpropagation, activation functions, and optimization algorithms.
Explore Multilayer PerceptronUnderstand CNNs for image processing and computer vision tasks.
Explore Convolutional Neural NetworksLearn about RNNs for sequential data processing and natural language tasks.
Explore Recurrent Neural NetworksUnderstand the architecture behind modern NLP models like BERT and GPT.
Explore Transformers5. Model Comparisons
Compare different models to understand their strengths, weaknesses, and appropriate use cases.
Compare logistic regression, decision trees, SVMs, and random forests on various classification tasks.
Explore Classification ComparisonsCompare linear, polynomial, ridge, and lasso regression on different regression problems.
Explore Regression ComparisonsCompare MLPs, CNNs, RNNs, and Transformers on various tasks to understand their strengths.
Explore Neural Network ComparisonsReady to start your machine learning journey with our structured, consistent learning experience?
Begin with Linear Regression