mitchsanders / machine_learning_mastery_in_python

Class I taught to Dell software engineers in early 2017

Machine Learning Mastery in Python

This is the material for a class I taught to Dell software engineers in early 2017. All code is in Jupyter notebook form with much of embedded lecture text included.

This class was a 17 week course. Classes were for one hour once a week. Assumptions for students were to already be sufficient in Python and understand basic statistical practices and concepts. Goal was to bridge the startup gap needed by software engineers and introduce them fast and functionally to machine learning in python.

Textbook for this class - "Machine Learning Mastery With Python: Understand Your Data, Create Accurate Models and Work Projects End-To-End" by Jason Brownlee Code and text migrated to Jupyter with additions for class by Mitch Sanders 5/15/2017

Syllabus

Python Ecosystem for Machine Learning

Python
SciPy
scikit-learn
Python Ecosystem Installation
Summary

Crash Course in Python and SciPy

Python Crash Course
NumPy Crash Course
Matplotlib Crash Course
Pandas Crash Course
Summary

How To Load Machine Learning Data

Considerations When Loading CSV Data
Pima Indians Dataset
Load CSV Files with the Python Standard Library
Load CSV Files with NumPy
Load CSV Files with Pandas
Summary

Understand Your Data With Descriptive Statistics

Peek at Your Data
Dimensions of Your Data
Data Type For Each Attribute
Descriptive Statistics
Class Distribution (Classification Only)
Correlations Between Attributes
Skew of Univariate Distributions
Tips To Remember
Summary

Understand Your Data With Visualization

Univariate Plots
Multivariate Plots
Summary

Prepare Your Data For Machine Learning

Need For Data Pre-processing
Data Transforms
Rescale Data
Standardize Data
Normalize Data
Binarize Data (Make Binary)
Summary

Feature Selection For Machine Learning

Feature Selection
Univariate Selection
Recursive Feature Elimination
Principal Component Analysis
Feature Importance
Summary

Evaluate the Performance of Machine Learning Algorithms with Resampling

Evaluate Machine Learning Algorithms
Split into Train and Test Sets
K-fold Cross-Validation
Leave One Out Cross-Validation
Repeated Random Test-Train Splits
What Techniques to Use When
Summary

Machine Learning Algorithm Performance Metrics

Algorithm Evaluation Metrics
Classification Metrics
Regression Metrics
Summary

Spot-Check Classification Algorithms

Algorithm Spot-Checking
Algorithms Overview
Linear Machine Learning Algorithms
Nonlinear Machine Learning Algorithms
Summary

Spot-Check Regression Algorithms

Algorithms Overview
Linear Machine Learning Algorithms
Nonlinear Machine Learning Algorithms
Summary

Compare Machine Learning Algorithms

Choose The Best Machine Learning Model
Compare Machine Learning Algorithms Consistently
Summary

Automate Machine Learning Workflows with Pipelines

Automating Machine Learning Workflows
Data Preparation and Modeling Pipeline
Feature Extraction and Modeling Pipeline
Summary

Improve Performance with Ensembles

Combine Models Into Ensemble Predictions
Bagging Algorithms
Boosting Algorithms
Voting Ensemble
Summary

Improve Performance with Algorithm Tuning

Machine Learning Algorithm Parameters
Grid Search Parameter Tuning
Random Search Parameter Tuning
Summary

Save and Load Machine Learning Models

Finalize Your Model with pickle
Finalize Your Model with Joblib
Tips for Finalizing Your Model
Summary

Python 2.7