Explore a collection of curated AI/ML notebooks focusing on basic concepts like linear algebra, probability, and data visualization, as well as key machine learning techniques such as regression, gradient descent, and regularization. Each notebook provides detailed explanations and hands-on code examples, designed to help you master essential topics in machine learning. Run these notebooks directly in your browser to gain an interactive and practical understanding of these concepts.
Understand the fundamental concepts of linear algebra for machine learning.
Learn the basics of probability and statistics to analyze data effectively.
Explore how to visualize data and communicate insights effectively.
Learn techniques to scale data for better machine learning performance.
Understand and implement linear regression models for predictive analysis.
Dive into loss functions and understand their role in machine learning optimization.
Learn about gradient descent, the optimization algorithm behind model training.
Understand and implement logistic regression for binary classification tasks.
Learn how regularization techniques prevent overfitting in machine learning models.
Understand the concepts of overfitting and underfitting in machine learning.
Explore performance metrics to evaluate machine learning models.
Learn about decision trees, a popular algorithm for classification and regression.
Understand how KNN works for classification and regression tasks.
Learn about support vector machines for classification and regression.
Explore the perceptron algorithm, the building block of neural networks.
Understand the role of activation functions in deep learning models.
Dive into multilayer perceptrons and their applications in deep learning.
Explore reinforcement learning, an advanced topic in machine learning.