Applied Machine Learning

Washington, D.C. campuses

GA D.C., 1776
1133 15th Street NW, 8th Floor
Washington D.C. 20005

GA D.C. (Northern Virginia)
2231 Crystal Drive #1000
Arlington VA 22202

Past Locations for this Workshop

Applied Machine Learning

Washington, D.C.

Washington, D.C. campuses

GA D.C., 1776
1133 15th Street NW, 8th Floor
Washington D.C. 20005

GA D.C. (Northern Virginia)
2231 Crystal Drive #1000
Arlington VA 22202

Past Locations for this Workshop

About this workshop

Every time we interact with an e-commerce site and see a recommendation to buy a product or we interact with our messenger app and see a chat bot in action, we are seeing machine learning in action. Strong mathematical theories underpin these machine learning application. The Machine Learning library eco-system has matured to an extent that it is straight forward to write a few lines of code and have the ML back-end ready for one’s application.

However, the challenge for many beginners is how to structure a business problem as a ML problem, and then go on to build, select and evaluate the right model. This workshop is designed to help learn how to apply machine learning to business problems through real-life case studies and a focus on applications.

This is predominantly a hands-on bootcamp and will be 70% programming/coding and 30% theory.

Takeaways

The bootcamp will be split into four modules:

Module 1: Linear Models (Case 1) - Linear Regression - Logistic Regression

Module 2: Model Evaluation (Case 1 continued) - Feature Engineering and Model Selection - Model Evaluation Metrics - Accuracy, RMSE, ROC, AUC, Confusion Matrix, Precision, Recall, F1 Score - Overfitting and Bias-Variance trade-off - Regularization (L1/L2) - Cross Validation

Module 3: Tree-based Models (Case 2) - Decision Trees - Bagging and Boosting - Random Forest - Gradient Boosting Machines

Module 4: Unsupervised Learning (Case 3) - Dimensionality Reduction - Principal Component Analysis - Cluster Analysis

Prereqs & Preparation

  • Programming knowledge is mandatory. Attendee should, at the bare minimum, be able to write conditional statements, use loops, be comfortable writing functions and be able to understand code snippets and come up with programming logic.
  • Participants should have a basic familiarity of Python. Specifically, we expect participants to know the first three sections from this: http://anandology.com/python-practice-book/

Software Requirements We will be using Python data stack for the workshop. Please install Ananconda for Python 3.5 for the workshop. That has everything we need for the workshop. For attendees more curious, we will be using Jupyter Notebook as our IDE. We will be using primarily scikit-learn libraries for most of the machine learning algorithms.

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