Book description
Build Machine Learning models with a sound statistical understanding.
About This Book
Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.
Who This Book Is For
This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.
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
In Detail
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.
Style and approach
This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Table of contents
- Preface
- Journey from Statistics to Machine Learning
-
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
- Logistic Regression Versus Random Forest
-
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
-
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
-
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
- Recommendation Engines
- Unsupervised Learning
-
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
Product information
- Title: Statistics for Machine Learning
- Author(s):
- Release date: July 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788295758
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