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
Schedule
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