Navigate through 9 topics on your path from data basics to deep learning.
Master the fundamentals: loading data, cleaning it, engineering features, and performing exploratory analysis.
Predict continuous values using linear and polynomial models, regularization techniques, and key error metrics.
Sort data into categories with logistic regression, tree-based models, SVMs, and precision/recall tradeoffs.
Find hidden structure in unlabeled data through clustering, dimensionality reduction, and anomaly detection.
Level up your models by tackling overfitting, cross-validation, hyperparameter tuning, and ensemble methods.
Build and train neural networks from scratch: perceptrons, backpropagation, CNNs, and recurrent architectures.
Process and understand human language with tokenization, embeddings, sentiment analysis, and transformers.
Teach machines to see: image preprocessing, convolutional layers, object detection, and transfer learning.
Train agents that learn by doing: rewards, Q-learning, policy gradients, and game-playing AI.