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Phoenix TS

Machine Learning with Python

Due to Covid-19 safety restrictions PhoenixTS will temporarily be unable to provide food to our students who attend class at our Training Center; however, our Break Areas are currently open where students will find a constant supply of Coffee, Tea and Water. Students may bring their own lunch and snacks to eat in our breakrooms or at their seat in the classroom or eat out at one of the many nearby restaurants.

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

Machine Learning with Python

date
location
price
1/25/22 - 1/27/22 (3 days)

10:00AM - 6:00PM EST

Online
Open
$1,945
3/21/22 - 3/23/22 (3 days)

9:00AM - 5:00PM EST

Online
Open
$1,945
5/18/22 - 5/20/22 (3 days)

12:00PM - 8:00PM EST

Online
Open
$1,945
7/12/22 - 7/14/22 (3 days)

10:00AM - 6:00PM EST

Online
Open
$1,945
9/20/22 - 9/22/22 (3 days)

9:00AM - 5:00PM EST

Online
Open
$1,945
11/07/22 - 11/09/22 (3 days)

12:00PM - 8:00PM EST

Online
Open
$1,945
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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

Due to Covid-19 safety restrictions PhoenixTS will temporarily be unable to provide food to our students who attend class at our Training Center; however, our Break Areas are currently open where students will find a constant supply of Coffee, Tea and Water. Students may bring their own lunch and snacks to eat in our breakrooms or at their seat in the classroom or eat out at one of the many nearby restaurants.

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