Statistics for Machine Learning
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Description
Contents
Reviews
Language
English
ISBN
9789388511971
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Introduction to Statistics
Structure
Objectives
Population and Sample
Introduction to Random Variables
Discrete Random Variables
Continuous Random Variables
Other variables
Numerical variables
Categorical Variables
Introduction to Descriptive Statistics
Visualizations
Conclusion
2. Descriptive Statistics
Structure
Objective
Measures of Central Tendency
Mean (Arithmetic)
Median
Mode
Unimodal data
Bimodal data
Multimodal data
Measures of dispersion
Range
Quartile
Standard Deviation
Standard Deviation vs. Variance
The Strength of the relationship between variables
Dependent variables
Independent variables
Covariance
Correlation
Conclusion
3. Random Variables
Structure
Objective
Random Variables
Discrete Random Variables
Continuous Random Variables
Joint Distributions
Independent Random Variables
Marginal and Conditional Distributions
Definition of Mathematical Expectation
Properties of Mathematical Expectation
Chebyshev’s Inequality
Law of large numbers
Conclusion
4. Probability
Structure
Objective
Introduction
Properties of probability
Intersection of sets
Union of sets
The complement of a set
Null set
Subset/superset
Some other terminologies
Mutually exclusive events
Mutually exhaustive events
Commutative laws
Associative laws
Distributive laws
De Morgan’s law
Conditional probability
Dependent and independent events
Bayes’s theorem
Probability distributions
Binomial distribution
Geometric distribution
Poisson distribution
Normal distribution
Conclusion
5. Parameter Estimation
Structure
Objective
Parameter estimation
Point estimate – The mathematics way
Sampling distributions
Central Limit Theorem
Estimators having bias component
The variance of a point estimate
Standard Error of Estimator
Mean Squared Error of Estimator
Methods to Determine Point Estimates
Method of Moments
Maximum Likelihood Method
Confidence Intervals
Conclusion
6. Hypothesis Testing
Structure
Objective
Hypothesis
Hypothesis Testing
Confidence Interval
Types of Hypothesis
Null Hypothesis
Alternative Hypothesis
P-Value
Steps in hypothesis testing
Use Case
Z-test
T-test
One-sample T-test
Two-sample T-test
Paired T-test
Chi-Square test
Test of Goodnessoffit
Independence test
Conclusion
7. Analysis of Variance
Structure
Objective
Introduction to ANOVA
One-way ANOVA test
Calculation of Mean Square due to Error
Calculation of Mean Square due to Treatment
Decision Rule
Tukey test
Two-way ANOVA
Main Effects
Interaction Effects
Multivariate Analysis of Variance (MANOVA)
Wilks’ Lambda test
Lawley Hotelling Trace
Pillai’s Trace
Roy’s Largest Root
Conclusion
8. Regression
Structure
Objective
Simple Linear Regression
Finding the Values of β0 and β1
Standard Error
Confidence Intervals
Unimportant Variable
Accuracy of Prediction
Data Pre-processing
Multiple Linear Regression
Polynomial Regression
Subset Selection Method
Ridge Regression
Lasso Regression
ElasticNet Regression
Logistic Regression
Estimation of Parameters
Understanding Residuals
Patterns of Residuals
Multicollinearity
Conclusion
9. Data Analysis Using Python
Structure
Objectives
Pandas
Importing and Reading a CSV Sheet
Basic Exploration of Data
Converting a Python Data Structure to Data Frame
Numerical Description of a Data Frame
Adding Conditions in Pandas
Extending Extractions – loc and iloc
Understanding the iloc() Function
Understanding the loc() Function
Tackling Null Values
Concatenating Data Frames
Merging Data Frames
Left Join
Right Join
Outer Join
Inner Join
Reading and Writing Excel Sheets
Exploring Groupby
Binning in Pandas
Pandas Series
NumPy
Creating Null Vector
Indexing
Reshaping a Numpy Array
Generating Random Values Using Numpy
Descriptive statistics using Numpy
Mathematical Operations Using Numpy
Other important features in Numpy
Conclusion
10. Non-Parametric Statistics
Structure
Objective
The test for randomness
Sign Tests
One-sample Sign Test
Wilcoxon Test
Mann Whitney Test
Spearman Rank Correlation Test
Kruskal Wallis test
Conclusion
11. Introduction to Machine Learning
Structure
Objective
Machine Learning
Supervised Learning
K-Nearest Neighbour
Naïve Bayes Theorem
Decision trees
Ensemble trees
Support Vector Machines
Python application
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis
Conclusion
Index
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$19.95
$19.95
