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AI 및 머신러닝 소개 | AI & Python 코딩 수업 2급
보고계신 지문은 자동 번역 되었습니다
수업 소개
영어레벨 - B2+
미국 8 - 11 학년
레벨 Intermediate
Welcome to the exciting world of Artificial Intelligence, Data Science, and Large Language Models! Over the course of 10 weeks, students will dive deep into the fundamentals of AI, explore the power of data analysis and visualization, and gain hands-on experience with the latest language models that are revolutionizing how we interact with technology. This course offers a unique opportunity for young minds to engage with concepts that are driving innovation across industries, preparing them...
10 lessons//10 Weeks
Week 1Lesson 1Week 1: Foundations of AI and DataIn this introductory week, students will gain a broad understanding of Artificial Intelligence and its significance in today's world. We'll explore various types of AI and their applications. The second half of the week focuses on the fundamentals of data analysis. Students will learn to use Python and the pandas library to manipulate and analyze datasets. The week concludes with a hands-on lab where students will explore a real-world dataset and create their data visualizations.Week 2Lesson 2Week 2: Data Visualization and Data ProcessingBuilding on the previous week, students will dive deeper into data visualization techniques. They'll learn to use matplotlib and seaborn libraries to create various types of plots and charts, including scatter plots, line charts, and heatmaps. The latter part of the week introduces the concept of Neural Networks. Students will learn about neurons, layers, and basic network architectures. They'll visualize simple neural networks and complete a mini-project creating an infographic.Week 3Lesson 3Week 3: Natural Language Processing BasicsThis week focuses on the foundations of Natural Language Processing (NLP). Students will learn about the challenges of processing human language and the basic techniques used to prepare text data for analysis. Topics include tokenization, stemming, and lemmatization. Students will practice visualizing text data using word clouds and frequency plots. The week culminates in a lab where students build a simple text classifier using basic NLP techniques.Week 4Lesson 4Week 4: Advanced NLP and Sentiment AnalysisBuilding on the previous week, students will delve into more advanced NLP concepts. They'll learn about bag-of-words models and TF-IDF (Term Frequency-Inverse Document Frequency). The main project this week is sentiment analysis, where students will build a model to classify text as positive, negative, or neutral. They'll also create visualizations to interpret and present their model's results, reinforcing both NLP and data visualization skills.Week 5Lesson 5Week 5: Word Representations and EmbeddingsThis week introduces how computers represent and understand words. Students will learn about one-hot encoding and its limitations, leading to the concept of word embeddings. They'll explore popular embedding techniques like Word2Vec and GloVe. Practical exercises will include visualizing word embeddings in 2D space, allowing students to see how these models capture semantic relationships between words.Week 6Lesson 6Week 6: Recurrent Neural NetworksBuilding on their knowledge of neural networks and sequential data, students will learn about Recurrent Neural Networks (RNNs) this week. They'll understand why traditional neural networks fall short for sequential data like text or time series. The course will cover basic RNN architecture, the concept of hidden states. The hands-on component includes implementing a simple RNN for text generation, giving students practical experience with these powerful models.Week 7Lesson 7Week 7: Introduction to TransformersThis week introduces students to the groundbreaking Transformer architecture. They'll learn about the key innovations in Transformers, including self-attention mechanisms and positional encodings. Students will understand why Transformers have revolutionized NLP tasks. The practical session includes visualizing attention mechanisms to help students grasp this complex but powerful concept.Week 8Lesson 8Week 8: Large Language ModelsBuilding on the previous week, students will explore Large Language Models (LLMs) like GPT. They'll learn about the scale of these models, their training process, and their diverse capabilities. Students will get hands-on experience using pre-trained models for text generation. The week concludes with a mini-project where students create a simple chatbot using a pre-trained model, providing practical experience with cutting-edge AI technology.Week 9Lesson 9Week 9: AI Applications and EthicsThis week broadens students' understanding of AI applications, particularly focusing on the capabilities of LLMs in tasks like translation, summarization, and question-answering. Students will be introduced to prompt engineering, learning how to effectively communicate with AI models. Through case studies and group discussions, students will explore the societal impacts of AI, issues of bias and fairness, privacy concerns, and the importance of responsible AI development.Week 10Lesson 10Week 10: Final Projects and PresentationsThe final week is dedicated to completing and presenting group projects. Students will work in teams to design an AI-powered application that incorporates data analysis and visualization. They'll create mock-ups of their application, explain the AI/ML techniques their app would use, and discuss potential ethical considerations. The week includes project presentation sessions, where each team will showcase their work to the class.
이 수업은 영어로 진행됩니다.
- Explain key concepts in AI, machine learning, and natural language processing
- Perform basic data analysis and create compelling visualizations using Python
- Use pre-trained language models for tasks like text generation and sentiment analysis
- Use pre-trained language models for tasks like text generation and sentiment analysis Critically evaluate the ethical implications and societal impact of AI technologies
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.
수업 외 주당 1 - 2 시간
과제
빈도: 7 or more throughout the class피드백: 포함됨세부 내용:Certificate of Completion
빈도: 1 after class completion세부 내용:
이 수업에서는 아웃스쿨 교실 외에도 다음의 툴을 사용합니다:
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 are many excellent beginner Python courses available through Outschool.
교사 전문성 및 자격증
학사 학위 Mount St. Mary's University 부터
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...
리뷰
그룹 수업
매주
₩100
또는 10 회 수업에₩35010주 동안 주당 1회
60분
실시간 화상 수업
연령: 13-18
수업당 학습자 5-12 명