Machine Learning with Python

Course Overview

This 3 day, instructor lead and hands-on machine learning course advances your data analysis skills into the realm of real-world data science. This course will teach participants how to:

  • Address business needs and identifying new business opportunities using machine learning
  • Work with missing values, outlines, and duplicate records with Python
  • Implement hypothesis testing for model evaluation analysis
  • Utilize both supervised and unsupervised machine learning
  • Build a linear regression model with Python
  • Build a classification model with Python
  • Use the K-means clustering method for cluster analysis with Python


Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.


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Course Outline

Module 1: Overview of Data Science

  • Data Science as a Quantitative Discipline
  • Overview of a Data Mining Process Cycle

Module 2: The Data Foundation

  • Data Sources
  • Types of Data
  • Working with Missing Values
  • Working with Outliers
  • Working with Duplicate Records

Module 3: Sampling and Hypothesis Testing

  • Why Sampling May be Important for Machine Learning
  • Sampling Techniques and Sample Bias
  • Statistical Hypothesis
  • Z-score, T-score and F statistic
  • P-values
  • Implementation of Hypothesis Testing for Model Evaluation Analysis

Module 4: Machine Learning Fundamentals

  • What is Machine Learning?
  • Supervised vs. Unsupervised Learning
  • Overview of Supervised Machine Learning
  • Overview of Unsupervised Machine Learning
  • Overview of Major Steps in Building and Testing Quantitative Models

Module 5: Building a Linear Regression Model with Python

  • Univariate Regression vs. Multiple Regression
  • Mathematical Foundation of Linear Regression Overview: least square method vs. maximum likelihood method
  • Model Assumptions
  • Working with Continuous Attributes
  • Dealing with Collinear Variable
  • Model Subset Selection:
  • Automating Model Selection Procedure
  • Model Parameter Evaluation, R squared vs. adjusted R squared
  • Validating the Model
  • Working with Categorical Variables
  • Considering Input Variable Interactions

Module 6: Example of building a Classification Model with Python

  • Dealing with Imbalanced Training Sets
  • Understanding Confusion Matrix
  • Evaluating Binary Classifiers using ROC / AUC

Module 7: Example of Cluster Analysis with Python

  • Overview of Cluster Analysis Mathematical Foundation
  • K-means Clustering Method

Module 8: Dimension Reduction techniques with Python

  • What is Dimension Reduction?
  • The Practical Goals of Dimension Reduction Implementation
  • Principal Component Analysis vs. Singular Value Decomposition
  • How Many Components to Choose

Module 9: Class Conclusion

  • What was Not Covered in the Class
  • Big Data Analytics – the Future of Machine Learning: Main Tools and Concept
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