Machine Learning Foundations: Build Expert-Level AI Models

Define and call Python functions using def, with parameters as placeholders and arguments as values. Explore default, positional, and keyword parameters, and understand pass-by-object-reference for mutable vs immutable objects.

Explore Python modules, files that define functions, classes, and variables to organize and reuse code, including the standard library and user-created modules. Learn to import modules, use math functions like pi and sqrt, and use randint and choice from the random module, then create a custom calculation module.

Explore lambda functions, anonymous one-line functions in Python, to write concise code, support functional programming, enable inline, callback, and higher-order usage, and power sorting, filtering, and data manipulation.

Explore analytical and aggregate functions in Python, including eval, len, factorial, sort, and mean, median, and mode from the statistics module for data analysis tasks.

Explore Python string methods to manipulate text, including len, upper, lower, replace, strip, split, starts with, ends with, count, and join for building strings.

Master built-in regular expression functions in Python: find all, search, split, and sub from the re module, to locate, split, and replace patterns in text.

Explore Python's conditional statements, including if, else, and else if, to control program flow and make decisions based on conditions, with attention to indentation and examples like divisibility checks.

Explore break and continue statements in Python loops, learning how break exits a loop when a condition is met and how continue skips the current iteration.

Explore nested loops and nested conditional statements in Python, showing outer and inner loops, range usage, parity checks, and how these structures enable complex control flow.

Explore the basics of classes and objects in Python and how a class serves as a blueprint for instances. Initialize attributes with init, using self and dot notation.

Explore measures of dependence, including correlation, to quantify the strength and direction of relationships between variables. Apply these concepts to data analysis, forecasting, and decision making, with the income–years example.

Discover how z scores express a data point's distance from the mean in standard deviation units. Use them for standardization, outlier detection, percentile ranking, and comparing distributions, with Python SciPy.

Discover Bayes theorem, a foundation for updating probabilities with prior beliefs and new evidence through prior, likelihood, and posterior. It underpins Bayes inference in medicine and data science.

Explore how skewness and kurtosis quantify distribution shape and tail behavior, with the monthly income data showing a skewness of 1.37 and kurtosis of 1.0.

Learn to perform maximum likelihood estimation by assuming a data generating process, deriving the likelihood as an objective function, and maximizing it to infer model parameters.

Explore how confidence intervals estimate a population parameter from sample data using the x̄ ± z s/√n formula, the margin of error, and the central limit theorem underpinning large-sample accuracy.