Deliver data-driven results and predict the future by building machine learning models that change every aspect of your business. This 3-day course provides the building blocks of machine learning so students can improve revenue, reduce costs, create new opportunities and learn essential skills for this high-demand field.

The Fundamentals of Machine Learning

• Gain familiarity with machine learning, supervised learning, unsupervised learning, regression and classification problems

• Train a machine learning model

• Use Scikit-Learn’s fit and predict methods to build a linear regression model

• Evaluate trained models using mean squared error and coefficient of determination

• Create new features that encode nonlinearities and use linear regression on an enhanced data matrix

• Build a prediction model using real-world data, and understand how this model can be utilized to achieve business goals

Classifications, Overfitting, Variance-Bias Tradeoff and Cross-Validation

• Use Scikit-Learn’s GridSearchCV to find optimal values for and tune hyperparameters

• Evaluate model performance using appropriate classification metrics

• Identify issues with unbalanced classes and improve model performance

• Include categorical features by using a one-hot encoder

• Build a Scikit-Learn pipeline to predict customer churn

• Understand key concepts including in-sampling, out-of-sample errors, variance-bias tradeoff, logistic regression

Datasets & Clustering Algorithms

• Perform principal concept analysis using Scikit-Learn and build a custom Scikit-Learn transformer to use in a pipeline to transform data

• Use PCA-transformed data to build a K-Means clustering algorithm

• Gain familiarity with metrics for clustering such as silhouette coefficient

• Obtain segments and extract information about each segment using techniques learned throughout the course

Classes will be held from 7:00pm-9:00pm ET on Tuesdays and Thursdays.

Classes will be held on the following dates: 10/29,10/31,11/5,11/7,11/12,11/14,11/19 and 11/21