This introduction to artificial intelligence, machine learning, and data science allows learners to start exploring the foundations of these exciting fields. Learners will complete 3 projects using Python code and the same tools used by professionals in the field. Learners will start learning about the types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. Then we will learn about the steps of successful machine learning projects. These steps include data collection, data preparation, model training, accuracy determination, and model improvement. 

 ****This is a coding-class using real code and the same tools used by professional AI and Machine Learning engineers. Please, review the coding requirements listed at the end of this description or the parental guidance section.****

Class Syllabus

Week 1
What is Intelligence?
What is AI? 
AI in Our World
The History of AI
What is Machine Learning?
Artificial Intelligence vs. Machine Learning vs. Data Science
Google Colab Introduction
Python Review - Conditional Statements, Functions, Lists

Week 2
3 Types of Machine Learning
Jupyter Notebook Introduction
Machine Learning Model - Decision Trees
Machine Learning Project #1 - Favorite Music Prediction
What Problems can AI Solve?
Python Review - Loops, Advanced Lists

Week 3
Introduction to Matplotlib
Data Sciene Project #2 - Matplotlib Graphs
NumPy Introduction
Pandas Introduction
Pandas DataFrames
Matplotlib Graphing Projects and Challenges

Week 4
Data Science Project #2 - Height/ Weight Comparison
Why use NumPy
Working with Data Python
Data Science and People
Six Steps of Every Machine Learning Project
Numpy Practice Questions

Week 5
Data and AI
Supervised vs. Unsupervised Learning
Supervised Learning Algorithm- Linear Regression
Machine Learning Project #2 - Hours Studied
Introduction to Mean Absolute Error, Mean Squared Error
Potential problems with AI Data
Coding Skills: A Good Coder is a Good Searcher

Week 6
What is Scikit-learn?
Supervised Learning Algorithm - Nearest Neighbor 
Machine Learning Project #3 -  Iris Data Set
Supervised Learning Algorithm - Decision Trees
Test and Training Data
Review

Week 7
Introduction to Neural Networks
MNIST Handwriting Data Set
Review Machine Learning Concepts
Start Neural Network Project
Introduction to Seaborn Visualization Library

Week 8
Supervised Learning Project #4 - Neural Network
Scaling Data
Loss and Determining Accuracy 
Test and Training Data
Careers in Machine Learning and AI
Next Steps

Interactive Groups Build the Foundation of Code Skills

Every learner is strongly encouraged to post questions, sample code, and their projects every step of the way. This gives students the chance to learn from each other and start practicing reading code. The instructor will also be providing feedback and guidance regularly throughout the course. 

***Required Coding Knowledge****

Learners should have an excellent understanding of the foundations of coding, including conditional statements, functions, loops, arrays/lists. Learners should have completed comprehensive beginner level coding classes before starting this course. Any programming language is fine, such as Python, Java, JavaScript, C / C++, or Swift. There will be a brief review during the first few classes using Python.