What you'll learn
Basics of AI, including applications, misconceptions, branches, history, and ethics
Basics of python, including variables, loops, data structures, and basic algorithms
Basics of libraries, including pandas, numpy, and matplotlib
Basics of ML, including supervised, unsupervised, reinforcement learning, regression and classification, and various ML models/algorithms
Basics of Computer Vision, including neural networks, convoluted neural networks, deep learning, and transfer learning
Requirements
No programming experience is needed! Beginner friendly course.
Description
*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!
Overview
Section 1: Introduction to AI
Lecture 1 What is AI? (1)
Lecture 2 Boston Dynamics Video
Lecture 3 What is AI? (2)
Lecture 4 Branches of AI (1)
Lecture 5 Tonight Show Video
Lecture 6 Branches of AI (2)
Lecture 7 History of AI
Lecture 8 AI Bias and Ethics (1)
Lecture 9 Algorithmic Bias and Fairness: Crash Course AI Video
Lecture 10 AI Bias and Ethics (2)
Lecture 11 AI Dangers Ted Talk
Lecture 12 AI Bias and Ethics (3)
Section 2: Introduction to Python
Lecture 13 Intro to Python (1)
Lecture 14 Colab Tutorial For Beginners Video
Lecture 15 Intro to Python (2)
Lecture 16 Printing and Variables
Lecture 17 Conditionals
Lecture 18 Loops
Lecture 19 Functions
Lecture 20 Lists
Lecture 21 (Optional) Sorting
Lecture 22 Dictionaries
Section 3: Introduction to Libraries
Lecture 23 Intro to Libraries
Lecture 24 Intro to Pandas
Lecture 25 Intro to Numpy
Lecture 26 Intro to Matplotlib
Section 4: Introduction to ML
Lecture 27 Introduction to ML (1)
Lecture 28 Linear Regression Video
Lecture 29 Introduction to ML (2)
Lecture 30 Logistic Regression Video
Lecture 31 Introduction to ML (3)
Lecture 32 Decision Trees Video
Lecture 33 Introduction to ML (4)
Lecture 34 K-means-clustering Video
Lecture 35 PCA Main Ideas Video
Lecture 36 Introduction to ML (5)
Lecture 37 MSE Video
Lecture 38 Introduction to ML (6)
Lecture 39 R-squared Video
Section 5: Introduction to Computer Vision
Lecture 40 Introduction to Computer Vision (1)
Lecture 41 What is a Neural Network video
Lecture 42 Introduction to Computer Vision (2)
Lecture 43 Deep Learning Video
Lecture 44 Introduction to Computer Vision (3)
Lecture 45 Activation Functions Video
Lecture 46 Introduction to Computer Vision (4)
Lecture 47 Transfer Learning Video
Lecture 48 Introduction to Computer Vision (5)
This course is meant for high schoolers and ambitious middle schoolers who are interested in learning more about AI and making an impact.

https://rapidgator.net/file/41b8656cd867aeccb0ac3335895e9cf5/Beginner_Friendly_Intro_to_AI_Course.rar.html