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Essential Math for Data Science Third Edition by Thomas Nield ISBN 9781098102920 1098102924

Name: Essential Math for Data Science Third Edition by Thomas Nield ISBN 9781098102920 1098102924
SKU: EB-36130956
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Essential Math for Data Science (Third Early Release) 1 / 2021-11-05: Third Release Edition Thomas Nield Digital Instant Download

Author(s): Thomas Nield

ISBN(s): 9781098102920, 1098102924

Edition: 1 / 2021-11-05: Third Release

File Details: EPUB, 4.76 MB

Year: 2021

Language: English

SKU: EB-36130956 Category: Mathematics - Mathematical Foundations Tag: Thomas Nield

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Essential Math for Data Science Third Edition by Thomas Nield – Ebook PDF Instant Download/Delivery: 9781098102920 ,1098102924
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ISBN 10: 1098102924
ISBN 13: 9781098102920
Author: Thomas Nield

To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you’ll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you’ll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You’ll get a solid foundation in the math essential for data science, but more importantly, you’ll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you’ve learned to machine learning, including linear regression, logistic regression, and neural networks — Provided by publisher

Essential Math for Data Science Third Edition Table of contents:

1. Basic Math and Calculus Review
Number Theory
Order of Operations
Variables
Functions
Summations
Exponents
Logarithms
Euler’s Number and Natural Logarithms
Natural Logarithms
Limits
Derivatives
Integrals
Conclusion
Exercises
2. Probability
Understanding Probability
Probability versus Statistics
Probability Math
Joint Probabilities
Union Probabilities
Conditional Probability and Bayes Theorem
Joint and Union Conditional Probabilities
Binomial Distribution
Beta Distribution
Conclusion
Exercises
3. Descriptive and Inferential Statistics
What is Data?
Descriptive versus Inferential Statistics
Populations, Samples, and Bias
Descriptive Statistics
Mean and Weighted Mean
Median
Mode
Variance and Standard Deviation
The Normal Distribution
The Inverse Cumulative Density Function (CDF)
Inferential Statistics
The Central Limit Theorem
Confidence Intervals
Understanding P-Values
Hypothesis Testing
The T-Distribution: Dealing with Small Samples
Big Data Considerations and Texas Sharpshooter Fallacy
Conclusions
Exercises
4. Linear Algebra
What is a Vector?
Adding and Combining Vectors
Scaling Vectors
Span and Linear Dependence
Linear Transformations
Basis Vectors
Matrix Vector Multiplication
Matrix Multiplication
Determinants
Systems of Equations and Inverse Matrices
Eigenvectors and Eigenvalues
Conclusion
Exercises
5. Linear Regression
A Basic Linear Regression
Residuals and Squared Errors
Finding the Best Fit Line
Closed Form Equation
Inverse Matrix Techniques
Gradient Descent
Overfitting and Variance
Stochastic Gradient Descent
The Correlation Coefficient
Statistical Significance
Coefficient of Determination
Standard Error of the Estimate
Prediction Intervals
Train/Test Splits
Multiple Linear Regression
Conclusions
Exercises
6. Logistic Regression and Classification
Understanding Logistic Regression
Performing a Logistic Regression
Logistic Function
Fitting the Logistic Curve
Multivariable Logistic Regression
Understanding the Log-Odds
R-Squared
P-Values
Train/Test Splits
Confusion Matrices
Bayes Theorem and the Confusion Matrix
Reciever Operator Characteristics (ROC)/Area Under Curve (AUC)
Class Imbalance
Conclusions
Exercises
7. Neural Networks
When to Use Neural Networks and Deep Learning
A Simple Neural Network
Activation Functions
Forward Propogation
Backpropogation
The Chain Rule
Calculating the Weight and Bias Derivatives
Stochastic Gradient Descent
Using Scikit-Learn
Limitations of Neural Networks and Deep Learning
Conclusions

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