
Explain what ai is, and how machine learning and deep learning, including neural networks, enable daily technologies like voice assistants and recommendations. Debunk common ai misconceptions.
Shape ai in its infancy, recognizing that researchers, engineers, critics, and learners influence its development. Engage with ai to influence its direction and explore its branches.
Explore the branches of ai—computer vision, natural language processing, and ai in robotics—covering how computer vision reads chest X-rays and street scenes, how nlp models learn language, and autonomous robots.
Explore the branches of AI, including computer vision, natural language processing, and AI robotics, and see how robots assist in disaster response and surgical procedures.
Trace the history of AI from early neural networks to modern deep learning and transformer models. Highlight milestones like Deep Blue, SVM, and data-driven applications across industries.
Explore AI bias and ethics through real-world examples like the compass courtroom assessment and Amazon's hiring algorithm, examining how data quality and proxies reshape predictions and perpetuate discrimination.
Explore how AI bias leads to discriminatory outcomes and how private data collected by models could be used against you. Consider AI's strengths in programming and implications for computer scientists.
Explore ethical concerns and responsible ai development to avoid future conflicts as you begin your journey into ai. Complete the intro quiz via the qr code or bit.ly.
Learn the basics of Python using notebooks, an interactive environment with code in small cells. Use Colab, Jupyter Notebook, and Kaggle for practical AI experiments and rapid iteration.
Python offers readability and ease of use for AI, a powerful beginner language; this basic intro uses Colab examples to kick off Python learning.
Learn to print text and variables in Python, explore booleans, integers, floats, and strings, and apply safe variable naming rules and simple string concatenation.
Master Python conditionals by using comparison operators, booleans, and logical operators with if, elif, and else, while respecting indentation and the difference between = and ==
Learn Python loops through for and while constructs, printing powers of two with range and increment, and practice generating square values from 1 to 10.
Learn how functions organize and reuse code, define with def, pass parameters, and call functions to compute sums; explore inclusive versus exclusive ranges and prep for lists.
Learn how to create and manipulate Python lists, explore indexing from zero, perform append/insert/remove/pop, slice lists, and use for loops to find minima and maxima.
Explore selection sort, a simple sorting algorithm that repeatedly finds the smallest value and swaps it into place, illustrated with an example and notes on its O(n^2) efficiency.
Explore how Python dictionaries store information as key-value pairs using curly braces and colons, access values by keys, modify or add entries, and iterate with keys or items.
Explore machine learning libraries and how they enable efficient data processing and analysis with Python, focusing on pandas, NumPy, and matplotlib.
Master pandas basics: import libraries, read csvs from local or drive, inspect data with head and loc, and analyze correlations with seaborn heatmaps while cleaning data using dropna, fillna, mean.
*This course is meant for purchase by an adult over 18*
Targeted towards high schoolers, this course covers the basics of AI, including its applications, misconceptions, branches, history, and ethics. The student will learn Python programming essentials, such as variables, loops, data structures, and basic algorithms, along with key Python libraries like pandas, numpy, and matplotlib. Moreover, the course introduces machine learning concepts, including supervised, unsupervised, and reinforcement learning, as well as regression, classification, and various ML models and algorithms. The student will also delve into computer vision, exploring neural networks, convolutional neural networks (CNNs), deep learning, and transfer learning.
This course is designed to be engaging and easy to understand, making complex topics accessible for high school students. Each topic is explained with simple examples and hands-on activities to apply what you learn. By the end of the course, the student will have a solid foundation in AI and Python programming, enabling the student to tackle real-world problems using technology.
Whether the student is interested in using AI for health, education, or any other field, this course will give them the tools and knowledge they need to start their journey. Enroll in this course to build a strong foundation in AI, and acquire the skills and knowledge to create impact in this special field!