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. Learners will learn 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
What is Machine Learning?
Artificial Intelligence vs. Machine Learning vs. Data Science

Week 2
Types of Machine Learning Part 1
Visual Studio Code Introduction
Python NumPy and Pandas Introduction
Working with Panda DataFrames
Machine Learning Project Introduction #1 - Favorite Music Prediction

Week 3
Machine Learning Project  #1 - Favorite Music Prediction
What Problems can AI Solve?
Supervised vs. Unsupervised Learning
Types of Supervised Learning 
The Machine Learning Process

Week 4
Working with Data Python
Data Science and People
NumPy Introduction
Pandas Introduction
Matplotlib Introduction

Week 5
Data and AI
Collecting and Preparing Data
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 #2 Introduction -  Iris Data Set

Week 7
Machine Learning Project #2 -  Iris Data Set
Supervised Learning Algorithm - Decision Trees
Loss and Determining Accuracy 
Test and Training Data

Week 8
Supervised Learning Project #3 - Classification: Is it a Dog or Cat?
Using Scikit-learn with images
Loss and Determining Accuracy 
Test and Training Data
Careers in Machine Learning and AI

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, and objects. 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. There are many excellent Python courses available through Outschool.