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Machine Learning in Python: From Data Collection to Model Deployment

Data Analytics

ISBN #978-1-952023-87-3

Machine Learning in Python: From Data Collection to Model Deployment

Mark Keith, PhD

This resource covers the latest and most common techniques for both descriptive and predictive data analytics using practice-based video tutorials. Students use dashboard design and storytelling in Tableau to describe the current state of an organization based on measurable data. Students will learn basic predictive methods in Excel for multiple regression and the assumptions of linear regression, and then turn to advanced algorithms and techniques using Microsoft Azure Machine Learning Studio.

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Course Objective

This resource teaches students descriptive and predictive data analytics techniques through video tutorials, focusing on dashboard design in Tableau, basic predictive methods in Excel, and advanced algorithms in Microsoft Azure Machine Learning Studio.

Table of Contents

Foreword: What Is Machine Learning?
Chapter 1: Data Mining Project Methodology
Chapter 2: Python: DataFrames
Chapter 3: Python: Data Wrangling in Pandas
Chapter 4: Python: Reading/Writing
Chapter 5: Retrieving Data: Web Scraping
Chapter 6: Retrieving Data: Inspect Element
Chapter 7: Retrieving Data: Web Service APIs
Chapter 8: Automated Exploratory Data Analysis: Univariate
Chapter 9: Automated Data Cleaning and Preparation
Chapter 10: Automated Exploratory Data Analysis: Bivariate
Chapter 11: Modeling: Regression
Chapter 12: Modeling: Forecasting
Chapter 13: Modeling: Classification
Chapter 14: Modeling: Clustering
Chapter 15: ML: Intro to Pipelines
Chapter 16: ML: Feature Selection
Chapter 17: ML: Algorithm Selection
Chapter 18: Text Analytics: Linguistic Features
Chapter 19: Text Analytics: Topic Modeling
Chapter 20: Image Classification
Chapter 21: Recommendation: Collaborative Filtering
Chapter 22: Recommendation: Content Filtering
Chapter 23: Deployment in Azure Machine Learning (in progress)
Chapter 24: Final Project
Supplement: Datasets
Supplement: Case Studies

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