Advance Programming Books

Data Science Algorithms, Models, Theories Notes

Download Data Science theory Algorithm free in PDF. These notes for machine learning with R course. It trace evolution as a data scientists into redundancy. There is a lots of work remaining to be done on this, including adding many more citation, replacing figures, and making sure full attribution is provided for all reference material.

You learn these topics from this notes

1.The Art Of Data science

  • Volume, velocity, variety
  • Machine Learning
  • Supervised and Unsupervised
  • Predictions and Bore casts

2. The very Binging: Got math?

  • Normal Distribution
  • Poisson Distribution
  • Vector Algebra
  • Statistical Regression

3. Open Source: Modeling in R

  • System Commands
  • loading Data
  • Matrices
  • Descriptive Statistically

4. MoRe: Data Handling and Other Useful Thing

  • Using The Merge Function
  • Using The Apply Class of Function
  • Getting Interest Rate Data From FRED

5. Being Mean With Variance: Markowitz Optimization

  • Quadratic (Markowitz) Problem
  • Solution R
  • Tracing Out The Efficient Frontier

6. Learning From Experience: Bayes Theorem

  • Introduction
  • Bayes and Joint Probability Distribution
  • Bayes Net
  • Bayes Rules in Marketing

7. More Than Words: Extracting Information From News

  • Prologue
  • Framework
  • Algorithms
  • Text Classification

8. Virulent Product: The Base Model

  • Introduction
  • Historical Examples
  • The Basic Idea
  • Solving the Model

9. Extracting Dimensions: Discriminant of Factor Analysis

  • Overview
  • Discriminant Analysis
  • Eigne System
  • Factor Analysis

10. Biding it Up: Auction

  • Theory
  • Auction Math
  • Treasury Auction

11. Truncate and Estimate: Limited Dependent Variables

  • Introduction
  • Logit
  • Probit
  • Analysis

12. Riding The Wave: Fourier Analysis

  • Introduction
  • Fourier Series
  • Complex Algebra

13. Making Connection: Network Theory

  • Overview
  • Graph Theory
  • Features of Graph
  • Searching Graph

14. Statistical Brain: Neural Networks

  • Overview
  • Nonlinear Regression
  • Perceptron

15. Zero or One: Optimal Digital Portfolio

  • Modeling Digital Portfolio
  • Implementation in R
  • Portfolio Characteristics

16. Against The Odds: Mathematics Of Gambling

  • Introduction
  • Entropy
  • Kelly Criterion

17. In The Same Boat: Cluster Analysis And Prediction Trees

  • Introduction
  • Clustering Using K-means
  • Hierarchical Clustering
  • Prediction Trees

18. Bibliography




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