This course covers important steps in the pre-processing of data, including standardization, normalization, novelty and outlier detection, pre-processing image and text data, as well as explicit kernel approximations such as the RBF and Nystroem methods.
Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy. In this course, Preparing Data for Modeling with scikit-learn, you will gain the ability to appropriately pre-process data, identify outliers and apply kernel approximations. First, you will learn how pre-processing techniques such as standardization and scaling help improve the efficacy of ML algorithms. Next, you will discover how novelty and outlier detection is implemented in scikit-learn. Then, you will understand the typical set of steps needed to work with both text and image data in scikit-learn. Finally, you will round out your knowledge by applying implicit and explicit kernel transformations to transform data into higher dimensions. When you’re finished with this course, you will have the skills and knowledge to identify the correct data pre-processing technique for your use-case and detect outliers using theoretically robust techniques.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview [Autogenerated] Hi, My name is Jenny Ravi, and welcome to the scores on the bearing Gator for modeling that psychic. Learn a little about myself. I have a master's degree in electrical engineering from Stanford and have opened companies such as Microsoft, Google and Flip Card at Google was one of the first engineers working on a real time collaborative editing in Global Dogs, and I hold four patterns for the tagline technologies. I currently work on my own Start up loony Con, a studio for high quality video contact. Even as the number of machine learning frameworks and libraries in pleases on a daily basis, Psychic's learner's retaining its popularity with ease. Psychic Land makes the common use cases in machine learning, clustering classifications, dimensionality reduction and regression Incredibly easy. In this course, you begin the ability to appropriately the process data, identify outliers and apply colonel approximations. First, you will learn how pre processing techniques such as standardization and scaling health improve the efficacy off Emel algorithms. Next, you will discover how novelty and out fire detection is implemented inside ______. You will then understand the typical set of steps needed to work with both. Next on image data entitled Finally, you'll round out your knowledge by applying implicit and explicit colonel transformations to transform data to higher dimensions. When you finish with scores, you will have the skills and knowledge to identify the correct data. Pre processing technique for your king's case and you'll be able to detect out flyers in your data set using theoretically robust techniques.