![]() ![]() How to optimize the efficiency of the classification model.How to evaluate the model for a classification problem.How to train the model in a classification problem.Introduction to classification problems, Identification of a classification problem, dependent and independent variables.How to optimize the efficiency of the regression model.How to evaluate the model for a regression problem.How to train the model in a regression problem.Introduction classification problems, Identification of a regression problem, dependent and independent variables.Introduction to scikit-learn, Keras, etc.Other necessary verticals include statistical tests, hypothesis testing, linear algebra that covers the basics of any machine learning problem.These libraries will help you grasp a good command over the various steps involved in the machine learning life cycle like data extraction, loading, transformation, manipulation, visualization, feature engineering, feature selection, standardization, creating machine learning models, optimization, performance metrics, etc. ![]() Machine Learning libraries – You will learn about various libraries in python that supports machine learning like scikit-learn, keras, tensorflow, etc and other supporting libraries like pandas, numpy, pandas, matplotlib, seaborn, scipy, stats, etc. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |