Python Programming Fundamentals with AI Insights

Explore semantris AI activity, learn how machine learning predicts responses, and practice Python basics in Jupyter notebook, including cells, comments, and markdown.

Create and run Python code in a Jupyter notebook using code cells and a Python kernel, then execute cells, read the execution order, and use # comments to improve readability.

Learn Markdown, a lightweight markup language for data scientists and analysts to annotate and format text, with headings, lists, links, tables, code blocks, and images.

Explore machine learning basics and hands-on Python programming in Jupyter notebooks, including cells, print commands, comments, and markdown, culminating in a project that tests your knowledge.

Trace artificial intelligence history from Charles Babbage and the first digital computer to the 2010s big data and gpu-powered learning, with Python basics on variables, numeric data, and numeric operators.

Explore variables as named memory locations that store values in Python, and learn how to declare, print, and follow naming rules and conventions for readable code.

Explore Python numeric types, including integers and floats, and apply operators such as +, -, *, /, %, **, and // while using the type() function to check data types.

Explore the history of AI, the technologies powering it, and learn Python basics—variables, naming rules, coding style, numeric types (integers and floats) and operators, with a notebook project in Jupyter.

Explore string data types and functions, then apply supervised learning with Teachable Machine for image classification using webcam samples of pens and tissue boxes. Test accuracy and link to Python.

Explore Python strings as sequences of characters, create them with quotes, access via indexing and slicing (including negative indices), and use escape sequences, substrings, and the input function.

Explore string operations in Python, including concatenation with the plus sign, len, index, count, replace, and lower and upper transformations, plus f string formatting.

Explore machine learning basics with supervised learning and build a simple model. Master string data types, indexing and slicing, and f-strings through a decrypt-this-message project.

Explore how data sets power machine learning by detailing dataset components, instances, and labels, outline training, validation, and testing splits for sentiment analysis, image recognition, and spam detection in Python.

Learn how decision making drives python programming with conditional statements, if and else blocks, relational and boolean operators, and how LFE reduces redundant checks.

Explore nested if statements and advanced conditional logic in Python, using flowcharts to model property discounts for houses and apartments, and practice with tyranny operators and edge-case handling.

Explore how data drives machine learning by examining training, validation, and test sets, then practice Python conditionals, operators, and nested if statements, culminating in implementing a project diagram in Python.

Explore intelligent game development and reinforcement learning to build AI-driven games and agents, inspired by DeepMind's Atari successes and the rise of AI coaching in e-sports.

Master Python lists and tuples to organize data, access items by index and slice, and apply operations like append, pop, and insert. Understand why tuples are immutable and performance-friendly.

Explore dictionaries in Python: define, access, and modify mutable items using keys, handle nesting and lists, and perform common operations like len, keys, values, get, and pop.

Create a nested dictionary for four NBA teams, including players, a date-keyed win/loss map with 1/0, hometowns, and titles, then practice adding, modifying, and deleting dictionary entries.

Explore reinforcement learning, where an agent in an environment learns to maximize rewards and develop policies, illustrated with a self-driving car and Mario.

Learn to use for loops to iterate over lists, dictionaries, sets, tuples, and strings, and apply break, continue, index, and while loops that enforce conditions.

Explore nested loops by embedding one loop inside another to compare totals, multiply numbers, and print matches, then refine code to display a single solution per total value.

Explore ai in gaming and reinforcement learning as the backbone of algorithms, then practice for and while loops, including break and continue, with two projects: robotics word and diamond shape.

Explore unsupervised learning by discovering patterns in data without labeled outcomes. Learn how clustering groups similar data and see Python applications like spam filtering and personalized ads.

Explore functions and modules in Python to organize code, improve readability, and support debugging. Learn defining functions with def, parameters and arguments, return values, default values, and importing modules.

Create and import your module named my underscore module one, then access its functions via the module name; explore modules datetime, math, and random with timedelta, pow, sqrt, and randint.

Explore reinforcement learning in artificial intelligence and video games, including beating an reinforcement learning agent in Super Mario, and apply Python basics—functions, modules, and built-in modules—through three class projects.

Explore artificial neural networks inspired by the brain, with input, hidden, and output layers and weights learned to predict data. Learn Python file I/O and join a melody-playing ml demo.

Learn to read from and write to text files in Python, handle folder paths and openings, close files, and use read, readline, and readlines to process data.

Learn to write to files in Python by creating, overwriting, or appending with open modes x, w, and a; use write and writelines, then close files for the class project.

Explore neural networks and Python file handling as you read and write text files, culminating in a project that appends a final mark and student grid to the assignment file.

Explore recommender systems within data analytics, differentiate data science, data analytics, and AI, and learn how collaborative and content-based methods use user-item interactions and features to recommend items.

Explore NumPy arrays and the ndarray object, learn to create and inspect arrays, understand dimensions, shape, and dtype, and use zeros, ones, empty, arange, and linspace for data processing.

Explore NumPy array operations, including element-wise arithmetic and matrix product, with upcasting rules for mixed types. Learn reshaping, dimension-specific access, indexing, iterating, and slicing in one- and multi-dimensional arrays.

Explore data science, data analytics, and recommender systems, and apply NumPy to a 15 by 15 array to compute row max and min, diagonal sums, and a search without builtins.