Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. Machine Learning with Python for Everyone Part 3: Fundamental Toolbox shows you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.

You will learn about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You will see techniques for feature engineering, including scaling, discretization, and interactions. You will learn how to implement pipelines for more complex processing and nested cross-validation for tuning hyperparameters.

About the Instructor

Mark Fenner, PhD, has been teaching computing and mathematics to diverse adult audiences since 1999. His research projects have addressed design, implementation, and performance of machine learning and numerical algorithms, learning systems for security analysis of software repositories and intrusion detection, probabilistic models of protein function, and analysis and visualization of ecological and microscopy data. Mark continues to work across the data science spectrum from C, Fortran, and Python implementation to statistical analysis and visualization. He has delivered training and developed curriculum for Fortune 50 companies, boutique consultancies, and national-level research laboratories. Mark holds a PhD in Computer Science and owns Fenner Training and Consulting, LLC.

Skill Level

• Beginner to Intermediate

• Use fundamental classification methods including decision trees, support vector classifiers, logistic regression, and discriminant analysis
• Recognize bias and variability in classifiers
• Compare classifiers
• Use fundamental regression methods including penalized regression and regression trees
• Recognize bias and variability in regressors
• Manually engineer features through feature scaling, discretization, categorical coding, analysis of interactions, and target manipulations
• Tune hyperparameters
• Use nested cross-validation
• Develop pipelines

This course is a good fit for anyone who needs to improve their fundamental understanding of machine learning concepts and become familiar with basic machine learning code. You might be a newer data scientist, a data analyst transitioning to the use of machine learning models, a research and development scientist looking to add machine learning techniques to your classical statistical training, or a manager adding data science/machine learning capabilities to your team.

Course Requirements

Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also have basic familiarity with the vocabulary of machine learning (dataset, training set, test set, model). They should have a working Python installation that allows them to use scikit-learn, pandas, matplotlib, and seaborn.

Lesson Descriptions

Lesson 1: Fundamental Classification Methods I

In Lesson 1, Mark dives into two important classification methods: decision trees and support vector machines. Decision trees capture the idea of using if-then rules to put examples into buckets. Support vector machines are based on some pretty heavy mathematics, but Mark addresses them from a very graphical point of view.

Lesson 2: Fundamental Classification Methods II

In Lesson 2, Mark continues to add to your classification toolbox. He discusses two methods: logistic regression and discriminant analysis. These methods can classify examples and they can also give us an idea of the certainty of that classification. Mark also compares many classification techniques.
Lesson 3: Fundamental Regression Methods

In Lesson 3, Mark turns his attention toward regression models, also called regressors. He considers modifications of linear regression called penalized regression and a variation of decision trees called decision tree regressors. Decision tree regressors build on the idea of if-then rules but are adapted to numerical outcomes. He also compares many regression techniques.

Lesson 4: Manual Feature Engineering

In Lesson 4, Mark looks at the various ways we can manually modify our learning data. He discusses ways to rewrite features by adjusting their numerical scale, by converting them from numeric to categorical values, or by manipulating the way we encode categories. He also talks about adding new features: feature construction. Finally, Mark covers ways to modify the target to support learning.

Lesson 5: Hyperparameters and Pipelines

In Lesson 5, Mark addresses fine-tuning your models by selecting good hyperparameters and creating longer processing chains with pipelines. Hyperparameters control the inner workings of our models: two models that differ only in their hyperparameters may give very different results. Creating pipelines enables us to couple feature engineering, model selection, and model building into one streamlined object.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video .

Table of Contents

Introduction

Machine Learning with Python for Everyone: Introduction

Lesson 1: Fundamental Classification Methods I

Topics

1.1 Revisiting Classification

1.2 Decision Trees I

1.3 Decision Trees II

1.4 Support Vector Classifiers I

1.5 Support Vector Classifiers II

Lesson 2: Fundamental Classification Methods II

Topics

2.1 Logistic Regression I

2.2 Logistic Regression II

2.3 Discriminant Analysis I

2.4 Discriminant Analysis II

2.5 Bias and Variance of Classifiers

2.6 Comparing Classifiers

Lesson 3: Fundamental Regression Methods

Topics

3.1 Penalized Regression I

3.2 Penalized Regression II

3.3 Piecewise Constant Regression

3.4 Regression Trees

3.5 Bias and Variance of Regressors

3.6 Comparing Regressors

Lesson 4: Manual Feature Engineering

Topics

4.1 Overview of Feature Engineering

4.2 Feature Scaling

4.3 Discretization

4.4 Categorical Coding

4.5 Interactions

4.6 Target Manipulations

Lesson 5: Hyperparameters and Pipelines

Topics

5.1 Models, Parameters, and Hyperparameters

5.2 Tuning Hyperparameters

5.3 Nested Cross-validation

5.4 Pipelines

5.5 Tuning Pipelines

Summary

Machine Learning with Python for Everyone: Summary