Python: Python Programming for Artificial Intelligence (1)

Learn how to define and use variables in Python, including integers, strings, and decimals, and follow common naming conventions while saving and running code in an integrated environment.

Explore strings in Python by defining variables, using type(), concatenating strings, measuring length, and accessing characters by zero-based indices.

Explore how numbers behave as different variable types, distinguishing integers and floats, viewing them in the variable explorer, and converting integers to floats to reveal decimal points.

Explore built-in Python functions, including print, type, int, and round, and see how they convert strings and numbers, handle decimals, and produce outputs.

Learn to define and use user defined functions in Python with def, parameters, and return to automate calculations; explore docstrings, calling functions with arguments, and function scope.

Explore default and flexible functions in Python, using a circle area example with pi as a default and illustrating flexible args to adapt calculations.

Practice what we've learned so far by defining integers and strings, exploring functions with arguments and default parameters, and converting types from int to float to print a person's details.

learn how to use lambda functions in Python to write concise, anonymous one-line expressions like 2*x+5, understand automatic return, and compare with standard functions for simple calculations.

Explore how lists store integers and strings, access elements by index from zero, and use negative indexing and slicing. Apply built-in list functions like append, remove, reverse, and sort.

Explore tuples as immutable sequences defined with parentheses, not lists, where you access elements by index. Use built-in functions count and index to find occurrences and first positions.

Define dictionaries in Python using curly braces, assign keys and values, and access elements by their keys; explore non-string keys, keys() and updating values.

Master conditionals in Python by applying if-else statements, comparison operators, and logical operators to control flow, and utilize lists and dictionaries for practical examples.

Implement a Python function that converts a year to its century using string manipulation and conditional logic, handling various edge cases.

Learn how for loops and while loops repeat tasks in Python. Iterate ranges and sequences, sum lists with explicit counters, and split strings through practical examples.

Explore while loops in Python by running a variable to a limit, incrementing i, and printing 0 to 4, while comparing while loops to for loops and list indexing.

Practice 3 shows how to write a function to find the smallest list element with a for loop, starting from a large value and updating it as you go.

Learn object oriented programming by building a class with a constructor, using self to set name, last name, email, and salary, and create multiple employee objects.

Explore class variables and instance methods by adjusting a salary through a raise amount, using self and class counters to track employees and access within a class.

Learn a concise object oriented programming example for machine learning, focusing on essential parts, using a list of employees to find the highest salary with if statements and indexing.

Explore object oriented programming as an optional section in python programming for artificial intelligence. Decide whether to watch the object oriented playlist or continue with the machine learning series.

Identify common python syntax errors caused by typos, missing brackets or parentheses, and incorrect variable initialization, and learn how fixing invalid syntax prevents runtime exceptions.

Explore using try-except to handle errors in Python, including type errors from adding strings and numbers and zero division errors, and observe how the interpreter reports syntax and runtime issues.

Learn to handle common Python errors with try-except, covering index out of range, module import errors, file not found, and type mismatches when combining strings and numbers, with try-except-else usage.

Explore numpy basics by creating arrays, reshaping into a 3x5 matrix, inspecting shape, dimension, and dtype, and generating zeros, empty matrices, arange, and linspace.

Learn numpy basic operations: create and manipulate arrays and matrices, perform element-wise and scalar arithmetic, matrix multiplication, dot products, transposition, and compute sums, max, and min along axes.

Learn zero-based indexing and slicing in Python, including selecting single elements, ranges, reversing arrays, and accessing specific rows, columns, and elements in 2d arrays using [row, column] notation.

Explore shape manipulation in Python by flattening matrices, reshaping between 3x3 and 1x9 vectors, transposing arrays, and understanding the differences between reshape and resize for AI workflows.

Learn how to stack arrays using the stacking function, building vertical and horizontal stacks of arrays to create four rows of two columns.

Learn how to convert between arrays and lists in Python, understand memory sharing effects on copies, and use explicit copy to create independent arrays for safe modifications.

Explore the pandas library for fast data analysis with data frames from files, handling missing data, indexing, and time series, including head and tail views and simple graphs.

Learn basic pandas methods to inspect a data frame, including viewing columns and index, and using describe to summarize numerical features like age and salary.

Master indexing and slicing pandas data frames by selecting columns and rows with brackets and loc, including range and reverse order concepts. Use underscores for spaced names.

Learn to filter a pandas data frame with boolean masks (true/false), applying conditions like salary and age, and combine filters to target specific records.

Use list comprehension and conditional logic to create a salary feature by comparing each salary to the average and labeling as high or low, with pandas for column management.

Master pandas techniques: drop columns or rows with drop, control the axis and in-place updates, and practice concatenating data frames by stacking or joining horizontally or vertically.

Transform data in a data frame using list comprehension or the apply function to double ages, create a new column, and compare methods for pandas basics.

Explore pandas for data analysis and visualization by loading csv data into a data frame, inspecting columns and data types, filtering by species, and using describe to compare means.

Visualize data from the iris dataset using pandas to prepare a data frame and create line plots. Customize axes, legends, colors, and grid to compare species and interpret differences.

Learn to create a scatter plot using the Massport library, replacing the default plot and running code to visualize data with dots and lines, and explore the math block library.

Learn how to create a histogram to visualize data frequency, plot the variable, and label axes and title, while adjusting bin width to distinguish discrete bars.

Create simple bar plots by importing a plotting library, defining x and y data, setting a title and axis labels, and optionally comparing values like GDP by country.

Learn to create subplots and arrange two- and four-panel layouts with bar plots and labeled axes. Visualize flowers and species to practice basic data visualization for data analysis.

Conclude the Python programming for artificial intelligence course and set up the next step into data science with Python, featuring Kaggle, exploratory data analysis, and the Titanic competition.

Explore Python basics of integers and strings, define variables, and introduce classes and object oriented programming by building an Employee class with attributes and behaviors.

This lecture defines attributes as characteristics or properties of a class, illustrated by a footballer example; it shows how attributes (like age, club) are defined, accessed, printed, and updated.

Learn to define and use methods in Python by building a Square class with an edge attribute and an area method, using self to access attributes and create an object.

Compare methods and functions using an employee class with ratio calculations and standalone area exercises. Learn how self ties methods to a class, while functions take inputs and return results.

Learn how to create python classes with constructors and initialize methods, define attributes like name and age, and instantiate objects such as dog and cat to explore object oriented programming.

build a calculator class in Python, implementing initialization, addition and multiplication methods, docstrings, and user input with if statements to choose and print results.

Learn how to implement a calculator class in Python, with addition, multiplication, and division operations, handle console input and selections via if/elif, and provide error messages for invalid choices.

Explore encapsulation in Python's object oriented programming by hiding a bank account's money as a private attribute and controlling access with get and set methods.

Explore inheritance in Python: create child classes from a parent class, reuse attributes and methods, use super to initialize, and differentiate methods like run, walk, fly, and climb.

Learn how a parent class named websites is inherited by child classes, sharing attributes like name and surname while accommodating site-specific login data such as email and id.

Explore abstract classes in Python using the abstract base class pattern and abstract methods. See how animal and bird act as templates, enforcing walk and run implementations in subclasses.

Explore overriding in Python by comparing a parent animal class with a monkey subclass, showing how the subclass overrides the parent's __str__ method to print its own class name.

Demonstrates polymorphism by using a superclass and subclasses that override a common raise method, showing how different engineers apply varying raise rates and print results.

Explore object-oriented programming by building an abstract shapes template and concrete square and circle classes, implementing area, perimeter, and string representations with encapsulation and polymorphism.

Explore an object-oriented vehicle rental project with a customer class and a vehicle class hierarchy (cars and bikes), supporting rental, return, stock display, daily hourly weekly monthly, and car discounts.

Design a Python vehicle rental system using a parent class and child classes for cars and bikes. Implement methods such as display stock, rent hourly, rent daily, and return vehicle.

Build and manage a vehicle rental system by implementing a weekly rent class with stock tracking, rent and return methods, and time-based billing for hourly and daily rates.

Learn how a child car class inherits from the parent vehicle class, initializes via super, manages stock in a garage, and applies a discount using a global discount rate.

learn to create a second child class that inherits from the vehicle rent class, define its __init__ using super to pass stock, and instantiate an object to access inherited methods.

Develop the customer class to rent cars or bikes, with an initialize method tracking name and rental basis daily or hourly, plus request vehicle and return vehicle with try-except.

Build a python bike and car rental system with customer and vehicle classes, using objects and methods, plus a menu to view, rent, and return bikes.

Develop a car menu in a Python rental shop app, transitioning from bike to car, displaying available cars and handling rental, return, and billing.

Demonstrates testing a vehicle rental Python program, cycling through bike and car menus, renting and returning vehicles, and applying a discount while debugging input errors and data handling.

Conclude the object oriented programming course after testing the vehicle rant project and finishing a nice project. Move on to the next lessons.