Ementorhub

Skills you'll gain

Regression Analysis

Scikit Learn (Machine Learning Library)

Feature Engineering

Machine Learning Algorithms

Statistical Modeling

Applied Machine Learning

Data Manipulation

Performance Metric

Data Cleansing

Classification And Regression Tree (CART)

Predictive Modeling

Business Analytics

Random Forest Algorithm

Machine Learning

Sampling (Statistics)

Supervised Learning

Data Processing

There are 6 modules in this course

By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

K Nearest Neighbors

Support Vector Machines

Decision Trees

Ensemble Models

Modeling Unbalanced Classes