Basic Mathematics

• Linear Algebra: Understand vectors, matrices, matrix multiplication, determinants, eigenvalues, and eigenvectors.
• Calculus: Learn about derivatives, partial derivatives, gradients, and integrals. Familiarize yourself with concepts like chain rule and Taylor series.
• Probability and Statistics: Grasp the basics of probability distributions, Bayes' theorem, expectation, variance, and common statistical measures.

Fundamentals of Machine Learning

• Basic Concepts: Understand supervised and unsupervised learning, overfitting, underfitting, bias-variance tradeoff, and model evaluation metrics.
• Algorithms: Learn about common algorithms like linear regression, logistic regression, decision trees, k-nearest neighbors, and clustering techniques.

4. Neural Networks and Deep Learning Basics

• Perceptrons: Start with the simplest form of a neural network to understand the building blocks.
• Feedforward Neural Networks: Learn how these networks work, including forward propagation and backpropagation.
• Activation Functions: Study different activation functions like sigmoid, tanh, ReLU, and their properties.

5. Tools and Frameworks