Python: Real World Machine Learning
Table of Contents
Python: Real World Machine Learning
Python: Real World Machine Learning
Credits
Preface
What this learning path covers
What you need for this learning path
Who this learning path is for
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
I. Module 1
1. The Realm of Supervised Learning
Introduction
Preprocessing data using different techniques
Getting ready
How to do it…
Mean removal
Scaling
Normalization
Binarization
One Hot Encoding
Label encoding
How to do it…
Building a linear regressor
Getting ready
How to do it…
Computing regression accuracy
Getting ready
How to do it…
Achieving model persistence
How to do it…
Building a ridge regressor
Getting ready
How to do it…
Building a polynomial regressor
Getting ready
How to do it…
Estimating housing prices
Getting ready
How to do it…
Computing the relative importance of features
How to do it…
Estimating bicycle demand distribution
Getting ready
How to do it…
There's more…
2. Constructing a Classifier
Introduction
Building a simple classifier
How to do it…
There's more…
Building a logistic regression classifier
How to do it…
Building a Naive Bayes classifier
How to do it…
Splitting the dataset for training and testing
How to do it…
Evaluating the accuracy using cross-validation
Getting ready…
How to do it…
Visualizing the confusion matrix
How to do it…
Extracting the performance report
How to do it…
Evaluating cars based on their characteristics
Getting ready
How to do it…
Extracting validation curves
How to do it…
Extracting learning curves
How to do it…
Estimating the income bracket
How to do it…
3. Predictive Modeling
Introduction
Building a linear classifier using Support Vector Machine (SVMs)
Getting ready
How to do it…
Building a nonlinear classifier using SVMs
How to do it…
Tackling class imbalance
How to do it…
Extracting confidence measurements
How to do it…
Finding optimal hyperparameters
How to do it…
Building an event predictor
Getting ready
How to do it…
Estimating traffic
Getting ready
How to do it…
4. Clustering with Unsupervised Learning
Introduction
Clustering data using the k-means algorithm
How to do it…
Compressing an image using vector quantization
How to do it…
Building a Mean Shift clustering model
How to do it…
Grouping data using agglomerative clustering
How to do it…
Evaluating the performance of clustering algorithms
How to do it…
Automatically estimating the number of clusters using DBSCAN algorithm
How to do it…
Finding patterns in stock market data
How to do it…
Building a customer segmentation model
How to do it…
5. Building Recommendation Engines
Introduction
Building function compositions for data processing
How to do it…
Building machine learning pipelines
How to do it…
How it works…
Finding the nearest neighbors
How to do it…
Constructing a k-nearest neighbors classifier
How to do it…
How it works…
Constructing a k-nearest neighbors regressor
How to do it…
How it works…
Computing the Euclidean distance score
How to do it…
Computing the Pearson correlation score
How to do it…
Finding similar users in the dataset
How to do it…
Generating movie recommendations
How to do it…
6. Analyzing Text Data
Introduction
Preprocessing data using tokenization
How to do it…
Stemming text data
How to do it…
How it works…
Converting text to its base form using lemmatization
How to do it…
Dividing text using chunking
How to do it…
Building a bag-of-words model
How to do it…
How it works…
Building a text classifier
How to do it…
How it works…
Identifying the gender
How to do it…
Analyzing the sentiment of a sentence
How to do it…
How it works…
Identifying patterns in text using topic modeling
How to do it…
How it works…
7. Speech Recognition
Introduction
Reading and plotting audio data
How to do it…
Transforming audio signals into the frequency domain
How to do it…
Generating audio signals with custom parameters
How to do it…
Synthesizing music
How to do it…
Extracting frequency domain features
How to do it…
Building Hidden Markov Models
How to do it…
Building a speech recognizer
How to do it…
8. Dissecting Time Series and Sequential Data
Introduction
Transforming data into the time series format
How to do it…
Slicing time series data
How to do it…
Operating on time series data
