8.Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning скачать в хорошем качестве

8.Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning

This video titled "Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning" explains outliers i.e most common causes of outliers on the dataset, how to detect these outliers and thereafter how to handle them using various methods such as Z-Score, probabilistic and statistical models etc. This is the continuation of the previous video where we were detecting and handling outliers. Previously we covered box plot and this video is to extend the discussion with the usage of scatterplot and techniques like Z-Score and Inter Quartile Range for outlier detection. This is a machine learning & deep learning Bootcamp series of data science. You will also get some flavor of data engineering as well in this Bootcamp series. Through this series, you will be able to learn each aspect of the Data science lifecycle right from collecting data from disparate data sources, data preprocessing to doing visualization as well as model deployment in production. You will also see how to perform data preprocessing and build, regression, classification, clustering as well as a recurrent neural network, convolution neural network, autoencoders, etc. Through this series, you will be able to learn everything pertaining to Machine and Deep Learning in one place. Content & Playlist will be updated regularly to add videos with new topics. *******Git Hub Link for DataSet and Python Code******** https://github.com/nitinkaushik01/Mac...