It covers a wide range of topics varying from bayes error, linear discrimination to epsilon entropy & neural networks.
Another cheap option, and the books are extremely good, are from N. E. Piskunov.
It provides a convincing explanation to complex theorems with section wise practice problems.If you have innate interest in learning about neural network, this should be your place to start. Hence, I’ve enlist only those books which will help you to connect with data science better.Note: Books which are made free to access by the registered authorities have been mentioned in this article. I had read think stats but the others were gems! A model is said to be statistically significant only when the p-value is less than the pre-determined statistical significance level, which is ideally 0.05.
In fact, Mathematics is … Mathematics & Statistics are the founding steps for data science and machine learning. of Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic.
This is an example of Although the purpose of both these analyses is to provide results, Quantitative analysis provides a clearer picture hence making it crucial in analytics.There are two main categories in Statistics, namely:Descriptive Statistics helps organize data and focuses on the characteristics of data providing parameters.Suppose you want to study the average height of students in a classroom, in descriptive statistics you would record the heights of all students in the class and then you would find out the maximum, minimum and average height of the class.Inferential statistics generalizes a large data set and applies probability to arrive at a conclusion. The focus of this books is kept on connecting statistics concept with machine learning. If not, a link to amazon bookstore is provided.This is a highly recommended book for practicing data scientists. analytics, In simple words, Statistics can be used to derive meaningful insights from data by performing mathematical computations on it.Several Statistical functions, principles, and algorithms are implemented to analyze raw data, build a Statistical Model and infer or predict the result.The field of Statistics has an influence over all domains of life, the Stock market, life sciences, weather, retail, insurance, and education are but to name a few.Moving ahead. It enlists all the necessary chapters such as vectors, linear equations, determinants, eigenvalues, matrix factorization etc in great depth.Matrix and Data frames are essential components of machine learning.
Can you have a post on complete path to learn (or rather brush up) all the maths / stats / probability from scratch. ml,
Thank you for the list.I am much interested in Big data Programme and its implemented in my organization. It begins with scientific methods of data gathering and end up delivering dedicated chapters on bayesian statistics.This book is written by Andy Field, Jeremy Miles and Zoe Field. It’s quite self-explanatory.Our next step is to compare the life expectancy of two places (Ireland and South Africa) and perform the t-test to check if the comparison follows a Null Hypothesis or an Alternate Hypothesis.So, after you apply the t-test to the data frame (df1), and compare the life expectancy, you can see the below results:Notice the mean in group Ireland and in South Africa, you can see that life expectancy almost differs by a scale of 20.