Ementorhub

What you'll learn

Use the basic components of building and applying prediction functions

Understand concepts such as training and tests sets, overfitting, and error rates

Describe machine learning methods such as regression or classification trees

Explain the complete process of building prediction functions

Skills you'll gain

Applied Machine Learning

Data Processing

Feature Engineering

Supervised Learning

Machine Learning Algorithms

Predictive Modeling

Data Collection

Statistical Machine Learning

Random Forest Algorithm

Machine Learning

Classification And Regression Tree (CART)

Decision Tree Learning

Regression Analysis

There are 4 modules in this course

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Week 2: The Caret Package

Week 3: Predicting with trees, Random Forests, & Model Based Predictions

Week 4: Regularized Regression and Combining Predictors