Probability and Statistics for Machine Learning

Mathematics: Familiarity with secondary school-level mathematics will make it easier for you to follow along with the class If you are comfortable dealing with quantitative information--such as understanding charts and rearranging simple equations--then y

Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples

Understand the appropriate variable type and probability distribution for representing a given class of data

Calculate all of the standard summary metrics for describing probability distributions, as well as the standard techniques for assessing the relationships between distributions

Apply information theory to quantify the proportion of valuable signal that's present among the noise of a given probability distribution

Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t-test, ANOVA, and R-squared

Understand the fundamentals of both frequentist and Bayesian statistics, as well as appreciate when one of these approaches is appropriate for the problem you're solving

Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep

Develop a deep understanding of what's going on beneath the hood of predictive statistical models and machine learning algorithms

You use high-level software libraries (eg, scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities

You're a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems

You're a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline

You're a data analyst or AI enthusiast who would like to become a data scientist or data/ML engineer, and so you're keen to deeply understand the field you're entering from the ground up (very wise of you!)