Machine learning is one of today's most sought-after technical skills. These algorithms power the majority of financial trading on Wall Street, drive personalized recommendations at industry giants like Amazon, Spotify, and Netflix, and underpin breakthroughs across healthcare, logistics, and beyond.
This bootcamp is built around the practical competencies required to tackle real-world machine learning challenges. You will start with linear and logistic regression, two of the most proven and widely used approaches in the field. From there, the curriculum advances into methods with fundamentally different theoretical underpinnings, including k-nearest neighbors, decision trees, and random forests. Along the way, you will develop a working understanding of core statistical principles such as bias, variance, and overfitting, and you will gain hands-on experience evaluating model accuracy and selecting the right features and algorithms for each problem.
Every algorithm covered in this course is introduced with a visual explanation of its mathematical foundations, making the material accessible without requiring formal math prerequisites. The emphasis throughout is on building real skills you can apply immediately. Students entering this course should be comfortable writing Python programs and working with the NumPy and Pandas libraries.
Proficiency in Python and its data science ecosystem, particularly NumPy and Pandas, is required before enrolling. Students who have not yet developed this foundation are encouraged to complete our Python for Data Science Bootcamp prior to registering for this course. This prerequisite ensures all participants can engage fully with the hands-on exercises and get the most out of every session.
Prerequisite:
Students must be comfortable with Python and its data science libraries, including NumPy and Pandas. Those new to Python are required to complete the Python for Data Science Bootcamp before enrolling in this course.
Learn more about the Python Machine Learning Bootcamp at NYC Career Centers.
