Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.

Related Content you might be interested in

Current Title:

Small book image
Machine Learning Quick Reference
Rahul Kumar
Jan, 2019 | 294 pages
Small book image
Practical Time Series Analysis
Avishek Pal | PKS Prakash
Sep, 2017 | 244 pages
Small book image
Ensemble Machine Learning Cookbook
Vijayalakshmi Natarajan | Dipayan Sarkar
Jan, 2019 | 336 pages
Small book image
Hands-On Python for Finance
Krish Naik
Mar, 2019 | 378 pages
Table of Contents (18 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Quantifying Learning Algorithms
Quantifying Learning Algorithms
Statistical models
Learning curve
Curve fitting
Statistical modeling – the two cultures of Leo Breiman
Training data development data – test data
Bias-variance trade off
Regularization
Cross-validation and model selection
Model selection using cross-validation
0.632 rule in bootstrapping
Model evaluation
Receiver operating characteristic curve
H-measure
Dimensionality reduction
Summary
2
Evaluating Kernel Learning
Evaluating Kernel Learning
Introduction to vectors
Linear separability
Hyperplanes 
SVM
Kernel trick
Kernel types
SVM example and parameter optimization through grid search
Summary
3
Performance in Ensemble Learning
Performance in Ensemble Learning
What is ensemble learning?
Bagging
Decision tree
Random forest algorithm
Boosting
Summary
4
Training Neural Networks
Training Neural Networks
Neural networks
Network initialization
Overfitting
Prevention of overfitting in NNs
Vanishing gradient 
Recurrent neural networks
Summary
5
Time Series Analysis
Time Series Analysis
Introduction to time series analysis
White noise
Random walk
Autoregression
Autocorrelation
Stationarity
AR model
Moving average model
Autoregressive integrated moving average
Optimization of parameters
Anomaly detection
Summary
6
Natural Language Processing
Natural Language Processing
Text corpus
TF-IDF
Sentiment analysis
Topic modeling 
The Bayes theorem
Summary
7
Temporal and Sequential Pattern Discovery
Temporal and Sequential Pattern Discovery
Association rules
Apriori algorithm
Frequent pattern growth
Summary
8
Probabilistic Graphical Models
Probabilistic Graphical Models
Key concepts
Bayes rule
Bayes network
Summary
9
Selected Topics in Deep Learning
Selected Topics in Deep Learning
Deep neural networks
Backward propagation
Forward propagation equation
Backward propagation equation
Parameters and hyperparameters
Bias initialization
Generative adversarial networks
Hinton's Capsule network
Summary
10
Causal Inference
Causal Inference
Granger causality
F-test
Graphical causal models
Summary
11
Advanced Methods
Advanced Methods
Introduction
Kernel PCA
Independent component analysis
Compressed sensing
Self-organizing maps
Bayesian multiple imputation
Summary
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

TF-IDF

As we understood the limitation of count vectorization that a highly frequent word might spoil the party. Hence, the idea is to penalize the frequent words occurring in most of the documents by assigning them a lower weight and increasing the weight of the words that appear in a subset of documents. This is the principle upon which TF-IDF works.

TF-IDF is a measure of how important a term is with respect to a document and the entire corpus (collection of documents):

TF-IDF(term) = TF(term)* IDF(term)

Term frequency (TF) is the frequency of the word appearing in the document out of all the words in the same document. For example, if there are 1,000 words in a document and we have to find out the TF of a word NLP that has appeared 50 times in that very document, we use the following:

TF(NLP)= 50/1000=0.05

 

Hence, we can conclude the following:

TF(term) = Number of times the term appears in the document/total number of terms in the document

In the preceding example , comprised of three documents...

Previous Section
End of Section 3
Next Section