Statistics for Machine Learning

Build Machine Learning models with a sound statistical understanding.

Statistics for Machine Learning

Pratap Dangeti

5 customer reviews
Build Machine Learning models with a sound statistical understanding.
Mapt Subscription
FREE
$29.99/m after trial
eBook
$20.00
RRP $39.99
Save 49%
Print + eBook
$49.99
RRP $49.99
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$20.00
$49.99
$29.99p/m after trial
RRP $39.99
RRP $49.99
Subscription
eBook
Print + eBook
Start 30 Day Trial
Subscribe and access every Packt eBook & Video.
 
  • 5,000+ eBooks & Videos
  • 50+ New titles a month
  • 1 Free eBook/Video to keep every month
Start Free Trial
 
Preview in Mapt

Book Details

ISBN 139781788295758
Paperback442 pages

Book Description

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.

By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

Table of Contents

Chapter 1: Journey from Statistics to Machine Learning
Statistical terminology for model building and validation
Machine learning terminology for model building and validation
Machine learning model overview
Summary
Chapter 2: Parallelism of Statistics and Machine Learning
Comparison between regression and machine learning models
Compensating factors in machine learning models
Machine learning models - ridge and lasso regression
Summary
Chapter 3: Logistic Regression Versus Random Forest
Maximum likelihood estimation
Logistic regression – introduction and advantages
Random forest
Variable importance plot
Comparison of logistic regression with random forest
Summary
Chapter 4: Tree-Based Machine Learning Models
Introducing decision tree classifiers
Comparison between logistic regression and decision trees
Comparison of error components across various styles of models
Remedial actions to push the model towards the ideal region
HR attrition data example
Decision tree classifier
Tuning class weights in decision tree classifier
Bagging classifier
Random forest classifier
Random forest classifier - grid search
AdaBoost classifier
Gradient boosting classifier
Comparison between AdaBoosting versus gradient boosting
Extreme gradient boosting - XGBoost classifier
Ensemble of ensembles - model stacking
Ensemble of ensembles with different types of classifiers
Ensemble of ensembles with bootstrap samples using a single type of classifier
Summary
Chapter 5: K-Nearest Neighbors and Naive Bayes
K-nearest neighbors
KNN classifier with breast cancer Wisconsin data example
Tuning of k-value in KNN classifier
Naive Bayes
Probability fundamentals
Understanding Bayes theorem with conditional probability
Naive Bayes classification
Laplace estimator
Naive Bayes SMS spam classification example
Summary
Chapter 6: Support Vector Machines and Neural Networks
Support vector machines working principles
Kernel functions
SVM multilabel classifier with letter recognition data example
Artificial neural networks - ANN
Activation functions
Forward propagation and backpropagation
Optimization of neural networks
Dropout in neural networks
ANN classifier applied on handwritten digits using scikit-learn
Introduction to deep learning
Summary
Chapter 7: Recommendation Engines
Content-based filtering
Collaborative filtering
Evaluation of recommendation engine model
Chapter 8: Unsupervised Learning
K-means clustering
Principal component analysis - PCA
Singular value decomposition - SVD
Deep auto encoders
Model building technique using encoder-decoder architecture
Deep auto encoders applied on handwritten digits using Keras
Summary
Chapter 9: Reinforcement Learning
Introduction to reinforcement learning
Comparing supervised, unsupervised, and reinforcement learning in detail
Characteristics of reinforcement learning
Reinforcement learning basics
Markov decision processes and Bellman equations
Dynamic programming
Grid world example using value and policy iteration algorithms with basic Python
Monte Carlo methods
Temporal difference learning
SARSA on-policy TD control
Q-learning - off-policy TD control
Cliff walking example of on-policy and off-policy of TD control
Applications of reinforcement learning with integration of machine learning and deep learning
Further reading
Summary

What You Will Learn

  • Understand the Statistical and Machine Learning fundamentals necessary to build models
  • Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
  • Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages
  • Analyze the results and tune the model appropriately to your own predictive goals
  • Understand the concepts of required statistics for Machine Learning
  • Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
  • Learn reinforcement learning and its application in the field of artificial intelligence domain

Authors

Table of Contents

Chapter 1: Journey from Statistics to Machine Learning
Statistical terminology for model building and validation
Machine learning terminology for model building and validation
Machine learning model overview
Summary
Chapter 2: Parallelism of Statistics and Machine Learning
Comparison between regression and machine learning models
Compensating factors in machine learning models
Machine learning models - ridge and lasso regression
Summary
Chapter 3: Logistic Regression Versus Random Forest
Maximum likelihood estimation
Logistic regression – introduction and advantages
Random forest
Variable importance plot
Comparison of logistic regression with random forest
Summary
Chapter 4: Tree-Based Machine Learning Models
Introducing decision tree classifiers
Comparison between logistic regression and decision trees
Comparison of error components across various styles of models
Remedial actions to push the model towards the ideal region
HR attrition data example
Decision tree classifier
Tuning class weights in decision tree classifier
Bagging classifier
Random forest classifier
Random forest classifier - grid search
AdaBoost classifier
Gradient boosting classifier
Comparison between AdaBoosting versus gradient boosting
Extreme gradient boosting - XGBoost classifier
Ensemble of ensembles - model stacking
Ensemble of ensembles with different types of classifiers
Ensemble of ensembles with bootstrap samples using a single type of classifier
Summary
Chapter 5: K-Nearest Neighbors and Naive Bayes
K-nearest neighbors
KNN classifier with breast cancer Wisconsin data example
Tuning of k-value in KNN classifier
Naive Bayes
Probability fundamentals
Understanding Bayes theorem with conditional probability
Naive Bayes classification
Laplace estimator
Naive Bayes SMS spam classification example
Summary
Chapter 6: Support Vector Machines and Neural Networks
Support vector machines working principles
Kernel functions
SVM multilabel classifier with letter recognition data example
Artificial neural networks - ANN
Activation functions
Forward propagation and backpropagation
Optimization of neural networks
Dropout in neural networks
ANN classifier applied on handwritten digits using scikit-learn
Introduction to deep learning
Summary
Chapter 7: Recommendation Engines
Content-based filtering
Collaborative filtering
Evaluation of recommendation engine model
Chapter 8: Unsupervised Learning
K-means clustering
Principal component analysis - PCA
Singular value decomposition - SVD
Deep auto encoders
Model building technique using encoder-decoder architecture
Deep auto encoders applied on handwritten digits using Keras
Summary
Chapter 9: Reinforcement Learning
Introduction to reinforcement learning
Comparing supervised, unsupervised, and reinforcement learning in detail
Characteristics of reinforcement learning
Reinforcement learning basics
Markov decision processes and Bellman equations
Dynamic programming
Grid world example using value and policy iteration algorithms with basic Python
Monte Carlo methods
Temporal difference learning
SARSA on-policy TD control
Q-learning - off-policy TD control
Cliff walking example of on-policy and off-policy of TD control
Applications of reinforcement learning with integration of machine learning and deep learning
Further reading
Summary

Book Details

ISBN 139781788295758
Paperback442 pages
Read More
From 5 reviews

Read More Reviews

Recommended for You

Machine Learning for Developers Book Cover
Machine Learning for Developers
$ 39.99
$ 28.00
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase [Video] Book Cover
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase [Video]
$ 32.99
$ 28.05
Scala for Machine Learning - Second Edition Book Cover
Scala for Machine Learning - Second Edition
$ 47.99
$ 33.60
Spark for Machine Learning [Video] Book Cover
Spark for Machine Learning [Video]
$ 124.99
$ 106.25
MATLAB for Machine Learning Book Cover
MATLAB for Machine Learning
$ 39.99
$ 28.00
Machine Learning for OpenCV Book Cover
Machine Learning for OpenCV
$ 39.99
$ 28.00