人工智慧和機器學習簡介 | AI 與 Python 編碼課程 1 級

This introduction to artificial intelligence and machine learning allows learners to start exploring the foundations of these exciting fields. Learners will complete 4 projects using Python code and the same machine learning tools used by professionals in the field.  Students will complete real 4 machine learning projects utilizing a decision tree, linear regression, nearest neighbors, and a neural network. 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. ****

What Learners will Create

Jump into four hands-on projects where you'll:
Train an AI to distinguish between cats and dogs
Analyze real scientific data from the famous Iris dataset
Build a neural network that can read handwritten numbers
Design and code your own unique AI project based on what interests you most

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 - Cat or Dog Classification

Week 3
Machine Learning Project  #1 - Cat or Dog Classification
What Problems can AI Solve?
Supervised vs. Unsupervised Learning
Types of Supervised Learning 
The Machine Learning Process

Week 4
Working with Data Python
Project 
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
Introduction to Neural Networks
Neural Network Concepts
Supervised Learning Project #3 - Classification: Handwriting Classification
Using Scikit-learn with images
Loss and Determining Accuracy 
Test and Training Data

Week 9
Supervised Learning Project #3 - Classification: Handwriting Classification
Loss and Determining Accuracy 
Review Final Project

Week 10
Final Project and Presentations
Student Presentation
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. 

****Important Note for Adults*****

This isn't a beginner coding course. Learners should already be comfortable with basic programming concepts in any language (Python, Java, JavaScript, C/C++, or Swift). If your learner is new to coding, check out our beginner Python courses first - they'll give your learner the foundation you need to succeed here.