Complete Machine Learning 2026 A-Z™: 10 Real World Projects

Install Python 3 via Anaconda on Windows, set up Python home in environment variables, and verify with python --version; then use Anaconda Navigator to launch Jupyter Lab or Jupyter Notebook.

Learn to run and document Python code in Jupyter Notebook, handle input/output, and explore integers, floating point numbers, strings, variables, and basic arithmetic.

Practice numbers demonstrates reading inputs, type casting to numbers, and performing arithmetic (sum, difference, product, division, modulo) with Python, including divmod and math functions.

Improve Python string skills with practice problems that swap cases, normalize text with split and join, and extract digits to compute sums and averages.

Explore how if conditions control program flow by evaluating cake states, printing baking or not baking messages, and using else, elif, and nested blocks.

Master for and while loops by iterating over lists, strings, and ranges, using continue, break, and an optional else to control flow.

Learn to create reusable functions that perform calculations, like summing a list of numbers, using def and return to modularize code and enable repeated use.

Explore list comprehension to create new lists from iterables, replacing long for loops with concise expressions that map ranges to squares and apply complex operations.

Explore tuples and their immutability, showing how tuple elements cannot be replaced or added, while containing mutable objects like lists can be modified inside. Includes nested tuples and indexing examples.

Master Python lambda functions to create anonymous operations and use them with map, reduce, and filter on iterables.

Practice set operations and dictionary updates in Python, including symmetric difference updates, renaming keys with pop, and updating nested salaries in employee records.

Practice list comprehension to extract names of students with the second lowest grade from a nested list of names and scores, handling ties by sorting alphabetically.

Write a function that sums numbers up to a limit that are multiples of 3 or 5, using a range and a modulo check, and return the result.

Create a speed-checking function with speed as input, assign demerit points per every five kilometers above 70, print the total, and print license suspended if points exceed 12.

Learn to implement a Caesar cipher by shifting letters by n, mapping plaintext to ciphertext with a dictionary, while preserving punctuation and spaces in a text preprocessing function.

learn to implement a function that checks whether a number is perfect by summing its factors excluding the number itself, and apply it to 1 to 1000.

Explore object oriented programming in Python, contrast it with procedural programming, and learn how classes, objects, attributes, and methods define state and behavior for secure, scalable code.

See how without oops you rely on global variables and tedious tracking. Learn to use object oriented programming by defining a person class with attributes and methods accessed via objects.

Learn how methods inside a class manipulate object attributes with self, illustrated by a circle example that computes radius, area, and circumference using class and object attributes.

Practice oops teaches you to build a vehicle class with max speed and mileage, implement inheritance with a bus subclass, override methods, and use class attributes and type checks.

Learn measures of variability such as range, variance, standard deviation, and interquartile range, with quartiles, box plots, and outlier rules for sample and population data.

Learn how to compute z-scores, standardize data using mean and standard deviation, and use the z-table to find probabilities and areas under the normal curve.

Explore confidence intervals as probability ranges that capture a population parameter between two values with a margin of error and varying confidence levels.

Master constructing confidence intervals for a sample mean using the z-score and standard error, and see how sample size, variability, and confidence level affect interval width.

Explore hypothesis testing by contrasting null and alternate hypotheses, understanding significance levels, confidence intervals, p-values, and test statistics, including z and t tests for population comparisons.

Explore Z and T tests, p values, and significance using real examples to decide if sample means prove population differences, reject the null, and understand tails and alpha.

Compare type I and type II errors in hypothesis testing and how the null and alternate hypotheses guide decisions. Learn about statistical significance, p values, and alpha levels.

Explore chi square tests of goodness of fit and independence, using frequency data to assess distribution shapes with observed vs expected counts, degrees of freedom, and p-values.

Explore anova, a single test for comparing multiple populations, including the null and alternative hypotheses, the F statistic, and the steps to compute mean squares and make a decision.

Navigate inferential statistics by choosing the right test—from z tests and chi-square tests for categorical data to t tests and anova—guided by the correlation coefficient and graphpad quick calculators.

Explore how to perform statistical analysis using Graphpad's web calculators, covering categorical and continuous data, chi-square tests, descriptive statistics, t tests, ANOVA, and p-values with confidence intervals.