What is ML?
+50 XP
~9 min
Supervised Learning
Unsupervised Learning
+60 XP
Model Evaluation
+70 XP
Linear Regression: Predicting Continuous Values
~10 min
Logistic Regression: Binary Classification
Decision Trees: Rule-Based Models
Feature Engineering: Preparing Data for ML
Support Vector Machines (SVMs): Powerful Classification Boundaries
Introduction to Neural Networks: The Dawn of Deep Learning
Ensemble Methods: Random Forests and Boosting
Clustering: K-Means and Beyond
Dimensionality Reduction: PCA for Data Insight
Improving Models: Overfitting, Underfitting, and Regularization
Hyperparameter Tuning: Optimizing Model Performance
Handling Imbalanced Datasets: Strategies for Skewed Data
Model Interpretability and Explainable AI (XAI): Understanding Why Models Decide
Recommender Systems: Personalized Suggestions and Discovery
Time Series Forecasting: Predicting Future Trends
Introduction to Natural Language Processing (NLP): Working with Text Data
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Thinking in Code
Coding
Programming with Variables
Thinking in Python