Machine Learning with Python

This hands-on machine learning course advances your data analysis skills into the realm of real-world data science. If you have a working familiarity with Python, our three-day class equips you to go back to work with real-world predictive modeling and basic machine learning techniques. Led by expert data scientists, you will work with Python to lay your data science foundation and learn techniques that allow you to leverage your data in sophisticated, powerful new ways.

In this Machine Learning Python Course, You Will Learn How to:

• Address business needs and identifying new business opportunities using machine learning
• Work with missing values, outliners 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

Course Outline

Part 1: Overview of Data Science

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

Part 2: The Data Foundation

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

Part 3: Sampling and Hypothesis Testing

Part 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

Part 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

Part 6: Example of building a Classification Model with Python

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

Part 7: Example of Cluster Analysis with Python

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

Part 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

Part 9: Class Conclusion

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

Who should attend

Intermediate level data analysts interested in expanding their data mining processes. We emphasize Data Foundation and Machine Learning concepts. All exercises are performed using Python.

Pre-Requisites

• This machine learning with Python course is for individuals intermediate data analysis skills and basic knowledge of descriptive statistics. Any experience with Python is also beneficial. 
• Technical requirements: Installed Python and some Python packages. 
• Additionally, although it is not mandatory, students who have completed the Introduction to Basic Python Programming course have found it very helpful when completing this course.