Online Data Science II: Practical Machine Learning

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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

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