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Introduction to AI and Machine Learning | AI & Python Coding Summer Camp
David Sofield
Popular
Average rating:4.9Number of reviews:(704)
This is an introduction to AI and Machine Learning summer camp, introducing learners to the foundations of these exciting fields. Students will complete three projects using real machine learning tools, Python code, and real data.
Class experience
US Grade 5 - 8
Beginner Level
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...
8 lessons//2 Weeks
Week 1Lesson 1Intro to AI and Machine LearningIn this day, we will explore the concept of intelligence and its connection to artificial intelligence. We will delve into the basics of AI, its significance in today's world, and its various real-world applications. Additionally, we will distinguish between artificial intelligence, machine learning, and data science, and understand the importance of machine learning as a subset of AI.Lesson 2Machine Learning FoundationsWe will dive into different types of machine learning algorithms and get acquainted with Code with Mu, a popular code editor. This day also introduces the Python libraries NumPy and Pandas for data manipulation and analysis. We will learn how to work with Pandas DataFrames and start our first machine learning project on predicting favorite music genres.Lesson 3Machine Learning Project Music PredictionContinuing with the music prediction project, we will delve deeper into machine learning concepts. We will discuss the types of problems AI can solve and the differences between supervised and unsupervised learning. The day will also cover various steps involved in a machine learning project.Lesson 4Data Handling in PythonThis day focuses on handling and processing data using Python. We will explore the ethical considerations of data science and the human aspect of AI. The introduction to NumPy and Pandas will be deepened, and we will also learn about Matplotlib, a Python library for data visualization.Week 2Lesson 5Data Preparation and Ethics in AIWe will explore the relationship between data and artificial intelligence, emphasizing the importance of data collection and preparation for machine learning. The day will also cover the potential problems and ethical challenges associated with AI data. Additionally, we will highlight the importance of research and problem-solving skills in coding.Lesson 6Introduction to Scikit-learn and Machine Learning LibrariesWe will get introduced to Scikit-learn, a popular machine learning library in Python, and learn about the nearest neighbor algorithm for classification tasks. This day also marks the start of our second machine learning project, which involves classifying species of iris flowers using a linear regression machine learning model.Lesson 7Machine Learning Project and Advanced ConceptsWe will continue working on the iris classification project and explore decision trees as a method for classification. The day will also cover the concept of loss and how to measure the accuracy of a model. Additionally, we will learn about the importance of splitting data into training and test sets for model evaluation.Lesson 8Advanced Machine Learning Projects and Career PathsIn the final day, we will work on a project to classify images as dogs or cats using machine learning algorithms. We will learn how to use Scikit-learn for image classification tasks and revisit the concepts of accuracy in this context. The day will also discuss the importance of test and training data in evaluating image classification models and explore career opportunities in the field of machine learning and artificial intelligence.
This class is taught in English.
By the end of this class, students will be able to understand the foundational concepts of artificial intelligence (AI) and machine learning (ML), and apply these concepts to solve real-world problems. Students will gain proficiency in using Python programming language and key libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn to manipulate data, implement machine learning algorithms, and evaluate model performance. Through hands-on projects, students will develop the skills to build, test, and refine machine learning models for classification and prediction tasks.
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, AI, and Machine Learning. 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.
Homework Offered
There will be review questions and practice assignments each day, taking approx. 30 mins to 1 hour each day.2 - 4 hours per week outside of class
Assessments Offered
Grades Offered
Students will receive a certificate of completion at the end of the class. Students must attend at least 6 classes to receive the certificate.
This is a coding-class using real Python 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.
Learners will use Code with Mu as a code editor for this class. This is a free code editor that requires a Windows, Mac, or Linux laptop or desktop computer. It is recommended that computers have at least 8 GB of RAM.
In addition to the Outschool classroom, this class uses:
Learners will use Code with Mu as a code editor for this class. This is a free code editor that requires a Windows, Mac, or Linux laptop or desktop computer. Students will also utilize the following Python libraries, including Scikit-learn, NumPy, Matplotlib, and Pandas throughout the class. The documentation (instructions) for these libraries will be used as a reference throughout the course. Students will be provided with datasets and examples as CSV files that can be downloaded from the Outschool classroom. ***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 beginner and intermediate coding classes available on Outschool.
Popular
Average rating:4.9Number of reviews:(704)
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...
Group Class
$113
weekly or $225 for 8 classes4x per week, 2 weeks
60 min
Live video meetings
Ages: 10-14
5-12 learners per class
