Learn to Code in Python 3: Programming beginner to advanced

Learn about strings in Python, a data type and sequence of characters, and master indexing, slicing, length with len, and concatenation with plus, including quotes handling.

Practice string handling in Python by building two exercises: create a program to print user initials from name inputs and slice a lot number into country, product, and batch codes.

Explore numbers in Python by comparing integers and floats, covering automatic conversion, operations, modulus, power, parentheses, and the round function and math module.

Calculate the area and circumference of a circle by prompting for the radius, importing the pi value from the math module, and rounding results to two decimals.

Learn how to use lists and tuples in Python, compare mutability, and perform common operations such as len, indexing, append, insert, pop, remove, and merging to manage sequences of data.

Explore lists and tuples through practical Python exercises: gather a birthday, map a month from a names tuple, and append a user name to a predefined list before printing.

Explore dictionaries in Python: use key value pairs, access by keys, modify and add properties, work with nested data types, and safely retrieve via get.

Explore dictionaries in Python by building a person record, retrieving values with the get method, handling user input with lowercase to avoid case sensitivity issues.

learn how booleans represent true and false, perform comparisons with operators, and use conditionals in Python to drive program logic.

Practice booleans and conditionals by building a program that stores your age, asks for the user's age, converts to int, and prints older, younger, or same.

Learn to implement nested conditionals in Python to evaluate student grades and attendance, compute average and attendance rate, and determine approval based on the 6 and 80% rules.

Learn to use and/or operators in Python to test multiple conditions, such as attendance rates and average grades, and implement nested if logic for pass/fail scenarios.

Create a BMI calculator that asks for height and weight (meters/kilos or feet/inches and pounds), computes body mass index, and prints the BMI with classification (underweight to obesity).

Learn to use while loops to repeat code and iterate over lists with conditions, incrementing variables, breaking out, and building a guessing game.

Explore Python for loops to iterate over lists, strings, and dictionaries, print items, skip empties with continue, and nest loops for categorized blog posts.

Explore Python loops with tasks: build a for-loop using range to collect eight names in a list and print a random winner, then create a guess game with play again.

Validate student data in Python using loops to enforce grades between 0 and 10, total classes positive, and nonnegative absences, preventing crashes from invalid input.

Learn to handle input errors in Python by using try and accept statements to validate user input, safely convert to float, and print an invalid number rather than crashing.

Apply try and except to validate numeric input for grades, ensure values fall within 0 and 10, and continue on invalid input to prevent crashes during absences tracking.

Explore how to define and invoke functions in Python, use arguments and return values, and apply built-in functions like print and round, including Fahrenheit to Celsius and default parameters.

Discover the python time module to get current time, compute unix epoch timestamps for delivery dates, alias modules for clarity, and pause execution with sleep.

Install and import matplotlib to plot x and y data with plt, display charts, and learn to customize with legends, x ticks, labels, and titles.

Measure typing speed by timing five rounds of typing the word, track mistakes, and visualize progress with a matplotlib.pyplot chart of evolution.

Learn to perform http requests in python using the requests module and get method. Inspect status codes such as 200, 403, and 404, and read response headers and html data.

Send HTTP requests to APIs to fetch data, explore RESTful concepts, retrieve quiz questions with Open Trivia DB, view JSON responses, and convert them to Python dictionaries.

Explore how to work with json in Python: fetch an api response, parse the json string with json.loads, access results and category, and convert dictionaries back with json.dumps.

Build a quiz game that fetches trivia questions from the open trivia API using requests and json, handles responses, shuffles answers, and loops until the user quits.

the lecture demonstrates building a Python quiz game that captures user input, validates numeric answers, computes score for correct and incorrect responses, and supports replay while handling invalid input.

Master Python file handling by opening, reading, writing, and appending to text files, using r, w, a, and x modes, in the course folder or via full paths.

Learn to read and analyze Excel data in Python using pandas, loading sheets, accessing data frames, performing sums, indexing, and filtering invoices by value.

Define machine learning as learning from data and illustrate image recognition, AI, and deep learning as its applications. Install Python tools and a notebook environment to run and organize code.

Explore the iris dataset as a machine learning demo: 150 observations with four measurements across three species, using scikit-learn to train models with data and target arrays and their names.

Explore the k nearest neighbors (knn) model, selecting k, using the iris dataset to classify two species. Train with fit, predict, and assess accuracy via a train-test split.

Split the iris dataset into train and test sets with train_test_split, train a k-neighbors classifier, predict species, and measure accuracy with metrics to compare model performance.

Practice looping through k values from 1 to 25 to select the best k for knn, measure performance, and visualize results with a chart to identify when accuracy peaks.

Introduce logistic regression for binary and categoric predictions, and extend to one-vs-all multi-class classifications with iris data and spam detection examples.

Apply logistic regression to the iris dataset, train and predict with accuracy metrics, and compare with k-nearest neighbors to select the best classifier.

Explore next steps in machine learning by contrasting supervised and unsupervised learning, including classification, regression, clustering, and association. Apply concepts with iris and digits data and explore Kaggle resources.

Publish your final Python project online to share a BMI calculator example. Paste code, run it, and post the clickable link in Udemy’s Q&A.