Summary

Probability and Statistics are the bedrock on which the most recent advances in machine learning and Artificial Intelligence have been built. A deep and intuitive understanding of Probability Theory and Statistics and how to apply them to each specific situation is a fundamental requirement for any successful Data Science and Machine Learning project.

In this live training, we will cover in a hands-on and incremental fashion the fundamentals of descriptive statistics, the foundations of probability theory, Bayesian Analysis, and practical applications such as Model Fitting and Hypothesis Testing that are commonly used in practical applications in both industry and academia.

Program

  • Descriptive Statistics

    • Big Data and the need for statistics

    • Counting

    • Mean and Standard deviation

    • Quantiles

    • Box and whiskers plots

    • Measures of correlation

  • Fundamentals of Probability

    • Understand what probability is

    • Random variables

    • Calculate the probability of different outcomes

    • Sequences of events

    • Sampling

    • Likelihood

    • Model fitting

  • Probability Distributions

    • Uniform distribution

    • Binomial distribution

    • Gaussian distribution

    • Poisson distribution

    • Central Limit Theorem

    • Power-law distribution

    • Relationships between distributions

  • Bayesian Statistics

    • Monty-Hall Problem

    • Understand conditional probabilities

    • Derive Bayes Theorem

    • Sequential updates

    • Parameter estimation

  • A/B Testing

    • Understand Hypothesis Testing

    • A/B Testing

    • Measure p-values

    • Compare the likelihood of two outcomes.

    • Statistical Power

Resources