Python Classes Boston, MA

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a different theoretical basis, such as k-nearest neighbors, decision trees, and random forests. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. Participants also learn how to measure the accuracy of their models, as well as tips for choosing effective features and algorithms.

Learn the essential skills and tools to become a Python Developer. This beginner-friendly course teaches students Python for software development with Django and Django REST Framework, in addition to other developer tools such as Git, and SQL. After completing this certificate, students will be able to apply for the following roles: Python Developer, Back End Developer, Software Engineer, and many more.

Create dashboards and other interactive visualizations with Python and its libraries in this hands-on course. Participants will also learn how to deploy projects online using Heroku servers and GitHub.

The Fullstack Academy Artificial Intelligence & Machine Learning bootcamp provides current and prospective data professionals with the in-demand skills to specialize in this lucrative, dynamic, and rapidly-growing field. Over 26 weeks of part-time study, students will learn practical and theoretical machine learning concepts using real-world tools—graduating with the working knowledge and experience needed to qualify for a range of data roles, including those focused on AI. Students will also receive professional career coaching support for up to a year following graduation to help build and maintain an ideal and specialized career path in the industry.

This course on data wrangling with Python offers a three-day comprehensive introduction. The purpose of this course is to give an immersive foundation that equips students with the most critical data analysis tools. Students will learn how to work with NumPy, Pandas, and Matplotlib to calculate descriptive statistics, work with various data sources, and scrape web pages. The course culminates with a real-life data wrangling project.

This 5-day introductory Python programming course is designed for students with no prior programming experience who are looking to get comfortable with the basic skills needed for Python programming. This skills-centric course will cover foundational Python concepts, including data types, control statements, using functions and modules, handling exceptions and debugging programs, working with lists, and working with a variety of data structures. By the end of the course, students will be familiar with writing programs to create real-world Python applications.

This introductory Python programming course covers data types, writing functions, the math and datetime modules, and string formatting. Once the basics are covered, it dives into flow control structures such as loops, list comprehensions, enumerations, and the "is" and "is not" operators. Finally, it takes a look at using virtual environments, the command line, and file processing to execute Python files.

Advanced Python Programming covers lambda functions, mapping and filtering, sorting, and advanced topics such as mutable and immutable built-in objects. This course also introduces topics such as inheritance, attributes, methods, and properties. You'll apply what you've learned by using an API to get data from a website, saving it as a CSV file, and cleaning it up using Python.

This two-day course on Python with data science is intended for software developers, IT architects, and data scientists. This course will build on their knowledge of statistics and programming, teaching them the theoretical and technical aspects of using Python. Students will learn about the history and purpose of data science, lifecycle phases of data analytics, methods for repairing and normalizing data, and how to aggregate and group data. Students will also complete ten lab exercises.