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人工智慧和機器學習簡介 | AI 與 Python 編碼課程 1 級
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課堂經歷
英語程度 - B2+
美國等級 7 - 10
Beginner 等級
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...
10 lessons//10 Weeks
Week 1Lesson 1Introduction to AI and Machine LearningStudents will explore the concepts of intelligence and AI, discovering how AI is integrated into our daily lives. They'll learn the distinction between AI, Machine Learning, and Data Science, and get hands-on experience with Google Colab, a powerful tool for coding and analysis.Week 2Lesson 2Fundamentals of Machine Learning and PythonThis session introduces different types of Machine Learning and dives into essential Python libraries like NumPy and Pandas. Students will begin their first ML project, predicting favorite music, which will give them a practical understanding of ML applications.Week 3Lesson 3Machine Learning Concepts and Data VisualizationStudents will complete their first ML project and explore the problems AI can solve. They'll learn about supervised and unsupervised learning, key ML terminology, and the overall ML process. The day concludes with an introduction to data visualization using Matplotlib.Week 4Lesson 4Working with Data and PythonThis day focuses on handling data in Python. Students will work with Pandas DataFrames and synthetic data, preparing them for their second ML project comparing height and weight data.Week 5Lesson 5Introduction to Scikit-learn and Linear RegressionStudents will finish their height vs. weight comparison project and delve into the crucial role of data in AI. The class will introduce Scikit-learn, a powerful ML library in Python.Week 6Lesson 6Machine Learning and Linear RegressionStudents will work on a prediction project using linear regression to predict study hours and grades deepening their understanding of supervised learning algorithms.Week 7Lesson 7Nearest Neighbors and Model EvaluationStudents will learn about the Nearest Neighbors algorithm and various distance measurements used in ML. They'll also discover the importance of splitting data into test and training sets and how to evaluate model performance using confusion matrices.Week 8Lesson 8Working with Real-world DatasetsThis session introduces the famous Iris dataset and teaches students how to evaluate datasets. They'll apply their knowledge to a Nearest Neighbors project and learn about loss functions and determining model accuracy.Week 9Lesson 9Neural Networks and Handwriting ClassificationStudents will begin an exciting project on handwriting classification using neural networks. They'll learn about the history of neural networks, understand concepts like weights and nodes, and review requirements for their final project.Week 10Lesson 10Completing Neural Networks Project and Final ReviewThe final day will be dedicated to completing the neural networks handwriting classification project. Students will further explore loss functions and accuracy determination. The course will conclude with a review of key concepts and final project work.
- The course will introduce learners to the exciting fields of AI and machine learning. Students will complete 3 projects, using Python and the same tools used by professionals. T
- he goal of this class is to encourage learners to get excited about the possibilities of AI and ML and ready to continue learning more.
Over 5,000 students from nearly 100 countries across a variety of platforms have started coding in one of my classes. I offer classes covering the foundations of Python and AI. I am the author of the soon-to-be released book All About Python for Kids. Before teaching, I worked as a software developer for nearly 10 years. I've worked for organizations including Apple, Dell, and Best Buy. I believe the best way to learn is by doing and all my classes are based around hands-on projects that progressively build in difficulty. I'm a graduate of Mount St. Mary's University in Emmitsburg, Maryland. I can't wait to meet your learner in the class and get started soon.
1 - 2 每週課外時間
Homework
頻率: 1-2 per week回饋: 包括細節: There will be review questions and practice assignments each week, taking approx. 1 to 2 hours. Learners are also strongly encouraged to learn on their own outside of class time. Students will also have an optional final project introduced in week 7 of the class and presented during a optional 3 to 5-minute presentation.Assessment
頻率: 1-2 throughout the class細節: All the learners in the last 3 weeks can work on their own machine learning project and complete a optional 3 to 5-minute presentation to the class. Learners will receive feedback from the instructor and other learners for project.Certificate of Completion
頻率: 1 after class completion細節: Students will receive a certificate of completion at the end of the class that is fully verifiable online. Students must attend at least 8 classes to receive the certificate.
To succeed in this challenging class, learners need a strong understanding of coding fundamentals including conditional statements, functions, loops, and arrays/lists. Please, read detailed prerequisites included in the class description.
Students will use Google Colab during this class and will need a Google Account to access Colab. Colab is a browser-based code editor and there are no minimum hardware requirements for student computers. Students will need a reliable Windows, Mac, or Linux laptop or desktop for this class. *** 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.
Learners will use Google Colab during this class and will need a Google Account to access Colab. Students will also utilize the following Python libraries, including Scikit-learn, NumPy, Matplotlib, Seaborn, and Pandas throughout the class. The documentation (instructions) for these libraries will be used as a reference throughout the course. UC Irvine ML Repository and Kaggle datasets will be used for practice datasets throughout the class. Teachable Machine and ML Playground are low code platforms for machine learning projects. Python.org and W3schools will be used as Python reference sources throughout the class. ***Intro to AI and ML Class Prerequisites **** To succeed in this class, learners should have a strong grasp of coding fundamentals including 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.
教師專業知識和證書
學士學位 由 Mount St. Mary's University
With over a decade of coding experience and a passion for education. I have helped over 5,000 students from nearly 100 countries start their coding journey. I offer classes covering the foundations of Python, AI and Machine Learning. I aim to...
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團體課
US$35
每週或US$350 用於 10 課程每週上課 x 1 次, 10 週
60 分鐘
有26 為學習者完成此課程
即時視訊會議
年齡: 12-17
6-14 每班學員人數