### Mathematics for data science: Linear Algebra Part-1

Introduction Linear algebra is the bedrock of data science and machine learning. Linear algebra provides mathematical insights to solve equations we use to build models. [continue reading..]

Introduction Linear algebra is the bedrock of data science and machine learning. Linear algebra provides mathematical insights to solve equations we use to build models. [continue reading..]

Introduction What motivates people to get into data science fields? Why do people want to become a data scientist? Actually, if you want to find [continue reading..]

“Machine learning involves in adaptive mechanisms that enable computers to learn from experience, learn by examples and learn by analogy. Learning capabilities can improve the [continue reading..]

Introduction Variables are somethings which store data. It reserved memory locations to store data. We can understand it by a different way like in mathematics [continue reading..]

Introduction In the era of big data, we are facing a large amount of data on the daily basis and these data are very important [continue reading..]

Regardless of whether you’re a diehard tech fanatic(means a type of people like me who look forward to the tech development and trends that lie ahead) [continue reading..]

Introduction Tensorflow is an open source software library for numerical computation that enables machine learning practitioners more data-intensive computing. It also provides some robust widely [continue reading..]

Optimization refers to the task of either maximizing or minimizing some function f(x) by altering x. The function we want to minimize or maximize is [continue reading..]

Bayes’s theorem is the fundamental concept behind Bayesian statistics and there are several machine learning and deep learning algorithm depends upon Bayes’s theorem like Naive [continue reading..]

[table id=1 /] These all questions implies the answer would be quantitative or continuous in nature. Linear regression is the fundamental topic for the predicting [continue reading..]

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