Building Machine Learning Systems with Python
Getting Started with Python Machine Learning
Machine learning and Python – the dream team
What the book will teach you (and what it will not)
Our first (tiny) machine learning application
Learning How to Classify with Real-world Examples
Building more complex classifiers
A more complex dataset and a more complex classifier
Binary and multiclass classification
Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as similar number of common words
Latent Dirichlet allocation (LDA)
Comparing similarity in topic space
Classification – Detecting Poor Answers
Learning to classify classy answers
Looking behind accuracy – precision and recall
Classification II – Sentiment Analysis
Introducing the Naive Bayes classifier
Creating our first classifier and tuning it
Taking the word types into account
Predicting house prices with regression
Regression – Recommendations Improved
Classification III – Music Genre Classification
Using FFT to build our first classifier
Improving classification performance with Mel Frequency Cepstral Coefficients
Computer Vision – Pattern Recognition
Other feature selection methods
Multidimensional scaling (MDS)
Using jug to break up your pipeline into tasks
Using Amazon Web Services (AWS)
Where to Learn More about Machine Learning
Where to Learn More about Machine Learning
Where to Learn More about Machine Learning
Where to Learn More about Machine Learning