Statistics for Machine Learning: Implement Statistical methods used in Machine Learning using Python
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem.
- Develop a Conceptual and Mathematical understanding of Statistics
- Get an overview of Statistical Applications in Python
- Learn how to perform Hypothesis testing in Statistics
- Understand why Statistics is important in Machine Learning
- Learn how to process data in Python
This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics – Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.
What you will learn
- Understand the basics of Statistics
- Get to know more about Descriptive Statistics
- Understand and learn advanced Statistics techniques
- Learn how to apply Statistical concepts in Python
- Understand important Python packages for Statistics and Machine Learning
Who this book is for
This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.
About the Authors
Himanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management.
LinkedIn Profile: https://www.linkedin.com/in/himanshu-singh-2264a350/
Blog links: https://medium.com/@himanshuit3036
Facebook Profile: https://www.facebook.com/silli23
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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