Building Machine Learning Models in Python with scikit-learn

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on building machine learning models in Python with scikit-learn. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on realtime collaborative editing in Google Docs, and I hold four patents for it's underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. This course is a beginner's course for engineers and data scientists who want to understand and learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. This course covers scikit-learn support for data processing and feature extraction. You'll learn how to use libraries for working with continuous, categorical, ex as well as image data. This course goes beyond ordinary regression models. You'll understand and learn to implement specialized regression models such as lasso and ridge regression. Classification algorithms such as support vector machines and ensemble learning techniques such as gradient boosting and scikit-learn are also covered. In addition to supervised learning techniques, you'll also understand and implement unsupervised models such as clustering using the mean shift algorithm and dimensionality reduction using principle component analysis. At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered, and you'll be extremely comfortable using the Python scikit-learn library to build and preen your models.