Python Programming: Learn Python from Scratch to AI and ML

Explore Python arithmetic operations such as addition, division, floor division, modulus, and exponentiation, using variables and print statements to display results.

Learn to perform mathematical operations in Python by importing the math module and using functions like round, abs, floor, ceil, square root, exp, cos, and pi.

Master conditional statements in Python using if, else if, and else to act on temperature, converting input to int and printing actions like turning on the air condition or heater.

Learn how and, or, and not operators manage multiple boolean conditions in Python. Use real-world examples like age over 18 and license to determine driving eligibility and understand if-else outcomes.

Demonstrate the Python or operator with boolean conditions in if statements, showing that true or true yields true, and apply it to a senior citizen or student discount.

Learn how the not operator in python inverts boolean values within if statements, with practical examples like access denied for failed user authentication.

Explore Python's comparison operators, including equals, greater than, less than, greater than equals to, less than equals to, and not equals to, how they form boolean expressions in if statements.

Review key Python concepts by demonstrating input, print, typecasting, and math expressions to convert Celsius to Fahrenheit, compute averages, determine pass/fail, and classify triangles.

Master the Python while loop to repeat code driven by a boolean expression. Use a counter to prevent infinite loops and apply the concept to password authentication with access granted.

Master the for loop in Python by iterating over strings, lists, and matrices, using range and step size, and applying sums and max value logic.

Explore how to store data in lists, print items, access by index including negative indices, slice ranges, modify elements, and concatenate and slice lists.

Learn how to manipulate lists in Python using append, extend, insert, remove, clear, and pop; locate indices, count occurrences, search with in and not in, remove duplicates, and sort.

Learn how to create and manipulate dictionaries in Python, print values by key, access and update entries, handle missing keys, and map digits to words with practical examples.

Master Python exception handling with try and except blocks to validate input, convert to integers, and prevent crashes; learn to handle value error and zero division error.

Learn to write reusable python functions using the diff syntax, define and call functions with input arguments, return results, handle input types, and compute circle area with pi.

Explore how Python classes create objects, access attributes and methods with the dot operator, and define your own class using __init__ and self, with practical automobile examples.

Learn how to implement a bank account class in Python using a constructor, balance tracking, and deposit and withdrawal methods with validation and detail display.

Learn how Python inheritance works by creating a vehicle parent class and car and truck children, sharing attributes like model, year, and price and reusing methods.

Learn how to create and import python modules to organize functions and classes, and use dot notation with a module like calculate.py to call add, multiply, and divide.

Learn to organize Python code with packages and modules, import whole modules or specific functions, and create packages with __init__.py to access add and subtract.

Explore Python 3.12's standard library by importing built-in modules like random, date and time, and CSV. Use module index to locate them and employ from import to access their methods.

Explore the Python package index and pip install to manage external packages, install into site-packages, and import modules like Openpyxl for Excel file operations.

Learn how to process and update thousands of spreadsheets in Python using openpyxl, loading workbooks, iterating sheets, applying discounts to prices, and saving the updated results to new Excel files.

Explore machine learning with Python by installing Anaconda, launching Jupyter notebook, and creating Python notebooks to run code, manage environments, and work with machine learning libraries.

learn to download a game sales dataset, load it into a pandas data frame with read_csv in a Jupyter notebook, inspect shape and describe statistics, and prepare for PyTorch-based models.

Discover keyboard shortcuts for an efficient data science workflow in Jupyter notebook, covering editing and command modes, cell insertion and deletion, running cells, and quick documentation via tab and shift+tab.

Load a Bitly music CSV, prepare input features (age and gender) and output (music type), and train a decision tree classifier with scikit-learn, then predict and discuss model accuracy.

Evaluate model accuracy using a train-test split and accuracy score, compare predictions to actual test labels, and optimize by swapping models to improve performance.

Save trained models with joblib, load them in future tasks, and reuse for predictions to save time and avoid retraining.