Introducción a la IA y al aprendizaje automático | Clase de programación en Python e IA, nivel 1

This introduction to artificial intelligence and machine learning allows learners to start exploring the foundations of these exciting fields. Learners will complete 3 projects using Python code and the same machine learning 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. All students in the last 3 weeks are encouraged to work on their own machine learning project and complete an optional 5-minute presentation to the class. 

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


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
Introduction to Google Colab

Week 2
Types of Machine Learning Part 1
Google Colab Introduction
Python NumPy and Pandas Introduction
Working with Panda Data Frames
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
Ethical Issues in Data
Potential problems with AI
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 Introduction -  Iris Data Set
Supervised Learning Algorithm - Decision Trees
Introduction to Final Project
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

Week 9
Supervised Learning Project #3 - Classification: Is it a Dog or Cat?
Loss and Determining Accuracy 
Review Final Project

Week 10
Final Project and Presentations
Student Presentation
Careers in Machine Learning and AI

****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 in the parental guidance section.****


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. 

*** Intro to AI and ML Class Prerequisites ****

To succeed in this class, learners should have a strong grasp of coding fundamentals.
This includes conditional statements, functions, loops, and arrays/lists. Learners should have completed comprehensive multi-week 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 beginner Python courses available through Outschool.