In this repository, you will find the foundations of Calculus necessary for Machine Learning.
Calculus serves as the bedrock of machine learning, providing indispensable tools to comprehend, optimize, and craft intricate algorithms. Differential calculus, particularly gradient-based optimization, is paramount in fine-tuning machine learning models. For instance, in training neural networks, derivatives help adjust weights and biases, iteratively refining the model's predictive accuracy. Integration plays a pivotal role in probability theory and statistical inference, vital components in modeling uncertainty and making informed decisions in machine learning. Calculus provides the mathematical infrastructure to grasp the behavior of functions, aiding in optimizing models, analyzing data, and enabling algorithms to learn patterns and make predictions from complex datasets. Without calculus, the optimization and modeling capabilities essential for the sophisticated machinery of modern machine learning would be severely limited.
For the hands-on Jupyter Notebook labs, please refer to my Github repository.