Machine learning expertise is increasingly sought after, with sophisticated algorithms now controlling the majority of Wall Street trading activities and powering product recommendations at major technology and entertainment companies including Amazon, Spotify, and Netflix.
This comprehensive course begins with linear and logistic regression, foundational and time-tested approaches to solving machine learning problems. You will then advance to algorithms based on distinctly different theoretical frameworks, such as k-nearest neighbors, decision trees, and random forest methods. These topics bring crucial statistical principles into focus, including bias, variance, and the challenge of overfitting your models. Throughout the course, you will master techniques for evaluating model performance and develop strategies for selecting the most effective features and algorithms for your specific problems.
The curriculum emphasizes practical, hands-on skills necessary to address real-world machine learning challenges. Mathematical principles underlying each algorithm are presented visually and conceptually, but the course does not require formal mathematical derivations. Students entering the course are expected to have solid experience writing Python programs and working with the Numpy and Pandas libraries for data manipulation.
This course requires students to demonstrate comfort with Python programming and data science libraries including NumPy and Pandas. Students without prior Python experience must complete our Python for Data Science Bootcamp before enrolling.
Learn more about the Python Machine Learning Course offered in a self-paced format at Noble Desktop.
