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Showing 26–50 of 56 items
Use machine learning and NLP to extract meaningful patterns from free-text data, including names, locations, and complex real-world entities.
Apply machine learning techniques in Python to build, validate, and optimize predictive models using scikit-learn.
Apply debugging strategies and data science tools to analyze real-world datasets and document your coding process in a capstone project.
Apply Python and scientific libraries like NumPy and SciPy to solve statistical problems and explore probability, distributions, and relationships in data.
Learn to access, parse, and work with web data using Python, including APIs, HTML, XML, and JSON formats.
Learn structured debugging techniques in Python using loops, control structures, and the OILER framework to write and troubleshoot code effectively.
Build foundational Python skills for data analysis, web applications, and practical computing tasks.
Develop data science skills in Python, including data wrangling, visualization, machine learning, and applied analytics.
Learn Python 3 programming fundamentals for problem-solving, software development, and automation.
Use Python to perform statistical analysis, test hypotheses, and interpret real-world data effectively.
Use Python for creative coding projects that blend programming, visual art, interactivity, and computational thinking.
Strengthen Python programming with data structures, problem-solving strategies, and effective debugging techniques.
Advance Python data science skills with deeper machine learning, text mining, and applied analytics techniques.
Learn web application fundamentals using Django, covering HTTP, HTML, MVC, and live deployment techniques.
Learn NumPy and pandas to manipulate arrays and write efficient Python code for numerical computing and data analysis.
Learn to use AI tools like ChatGPT to analyze, visualize, and interpret data for real-world applications and projects.
Deepen your understanding of C programming and build portable, efficient algorithms using types, operators, recursion, and preprocessing.
Use logistic regression and Python to model and predict sports outcomes while examining analytics in gambling and society.
Apply machine learning techniques to real sports data to analyze, predict outcomes, and enhance performance analytics.
Learn to use Python in Rhino 3D to create complex geometric forms through procedural logic and design computation.
Use Python and sports datasets to explore team performance and become a hands-on producer of sports analytics.
Explore database modeling, SQL, and Django’s ORM to build scalable, interactive web applications using Python.
Learn to write and understand object-oriented programs in Python using real-world modeling and creative coding projects.
Master debugging techniques using tools and unit testing to quickly identify, fix, and prevent programming errors in Python.
Use Python to analyze baseball performance data and explore the evolution of Moneyball-era statistics through hands-on coding.
