In this repository, you will find the foundations of Probability and Statistics necessary for Machine Learning.
Probability and statistics play a fundamental role in machine learning and data science by providing the tools to analyze and interpret data, make informed decisions, and build predictive models. In data science, statistical techniques enable the exploration, summarization, and visualization of data, helping to uncover patterns and relationships within datasets. Probability theory is leveraged in machine learning algorithms to model uncertainty and randomness, allowing for the creation of probabilistic models that can make predictions, classify data, and estimate unknown parameters. These statistical methodologies form the backbone of many machine learning algorithms, aiding in the development of robust models that can learn from data and make intelligent decisions.
Confidence Intervals and Hypothesis testing
For the hands-on Jupyter Notebook labs, please refer to my Github repository.