Python Programming : Learn Python From Beginner To Advanced

Learn the fundamentals of Python programming and its applications in machine learning, artificial intelligence, and data science, with hands-on setup using Anaconda and libraries like TensorFlow, PyTorch, and scikit learn.

Install and launch the Python integrated development environment from python.org, choose Python 3.13 for Windows after Windows 7, pin the IDE to the start menu, and start programming.

Install and launch PyCharm community edition from JetBrains, create a new project, set its location and name, and access files in the lib folder with the virtual environment.

Install Anaconda on Windows from the Anaconda website, create the Anaconda folder, complete the setup, and launch Jupyter Notebook to start Python coding for AI and machine learning.

Verify the Python installation and its version by opening Anaconda, selecting Jupyter Notebook, creating a new notebook, writing code to verify, and printing the installed version.

Learn how Python comments explain code without execution, prefix lines with the comment symbol, and use tools like Anaconda and Jupyter Notebook to run commented examples.

Define and use Python variables by assigning values, explore string data type as a sequence of characters in quotes, print messages, and check types in a Jupyter notebook on Anaconda.

Explore Python integers with a=58 and b=-98, and see that addition, subtraction, and multiplication stay integers, while division and exponentiation yield floats or integers depending on the exponent.

Explore how Python floats represent real numbers with decimals, perform arithmetic operations as multiplication, division, and exponentiation, and verify that results remain floats using type checks in a Jupyter notebook.

Learn about Python booleans and boolean expressions, including true or false results from A > B, A == B, and A != B, using Anaconda and Jupyter notebooks.

Explore variable casting in Python by enforcing int, float, or string types, check types with print type, and observe arithmetic rules and errors when mixing numbers with strings.

Master Python lists, built-in and ordered data structures that are changeable, support duplicates and multiple item types, with indexing from zero and negative indexing for the last item.

Explore Python tuples, an ordered, immutable collection that allows duplicates and mixed types. Access items by index, check length, and observe that tuples cannot be changed after creation.

Explore Python built-in sets, unordered collections that disallow duplicates and are not indexed, with examples like cars and mixed-type sets; learn type checks, length, and add, remove, and update operations.

Explore Python dictionaries, a mutable key-value data structure that stores ordered items, supports booleans, numbers, and strings, and demonstrates duplication rules, updating and retrieving values.

Explore Python arithmetic operation addition, demonstrating integer and float addition, type results (int vs float), and string concatenation, with hands-on practice in a Jupyter notebook via Anaconda.

Explain Python subtraction with integers and floats, showing integer results for integer operands and float results when a float is involved, with practical type examples.

Learn how Python handles multiplication with integers and floats, observe results, and print types in a Jupyter notebook using Anaconda.

Explore Python's division operation across integers and floats, showing that a / b yields a float, with practical examples and type checks in a Jupyter notebook.

Explore Python's modulus operation, which returns the remainder of division, with 9 mod 4 = 1 and 6 mod 3 = 0, and show that floats yield a float modulus.

Explore floor division in Python, which returns the quotient without the remainder and shows integer results for integers and float results when a float is involved.

Explore Python exponentiation and the a to the power b operation, including zero exponents yielding one. Observe how int and float types affect results in code examples.

Learn python conditional statements with the if statement, using comparisons like equal, not equal, greater than, greater than or equal, less than, and less than or equal, with input examples.

Learn to write Python if-else statements using input floats to compare a and b with greater than or equal, less than, and equal conditions, in Anaconda or Jupiter.

Explore Python conditional statements using if, elif, and else to evaluate scores with user input, compare values, and print outcomes in practical examples.

Explore how Python for loops iterate over lists, strings, tuples, sets, and dictionaries, using print, break, and continue to control flow and process data.

Learn how Python nested loops generate combinations by iterating two lists and printing each pair, using for x in A and for y in B to produce all outcomes.

Explore python loops with the for-else construct, printing elements from lists and nested loops, and see how else executes after loop completion across lists, tuples, sets, and dictionaries.

Learn how to use Python while loops to execute code while a condition is true, with examples counting down and up using step sizes 1, 2, and 5.

Learn Python lambda functions, anonymous single-expression utilities that accept multiple arguments and return values. See compact examples and run code in Jupyter notebook via Anaconda.

Understand how python functions are blocks of code that run when called. See def calculate func and print func with two or more arguments and their return values.

Explore Python functions through an ideal body weight calculator. Define ibew with gender and height inputs, apply lowercase checks, and return calculations or invalid data in a Jupyter notebook.

Learn to use Python's built-in max and min on tuples, lists, and sets, print results. Run code in Anaconda's Jupyter notebook to see 400 as max and -78 as min.

Discover how the Python built-in absolute value function abs returns the positive value of any number, including negatives and floats, with simple examples.

Learn how Python pow function computes a base to an exponent, print examples like 10 to 3 equals 1000, and 10 to -3 equals 0.001, using Anaconda and Jupiter notebooks.