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Photo by h heyerlein on Unsplash

The words, ‘Machine learning’ can sound either very exciting or very complex to a beginner. For me personally, it has changed from something complex on the surface to exciting when explored more over a period of time. It is very important to understand the meaning of machine learning in simple terms when you start learning about machine learning algorithms altogether.

Machine learning has been growing over the last two decades. You have used machine learning and have been living in it without even knowing when it was already there. Simple things like tagging your friends on Facebook photos or maybe searching a simple thing over the web, which would give results based on the ranking of the pages or your email client such as Yahoo, Gmail filtering out your spam mail in the spam folders are some classic examples of machine learning. …


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Photo by Kelly Sikkema on Unsplash

One of the main reasons Python is being so vastly used in machine learning and AI is the fact that it has a great choice of libraries. These libraries provide the base to the data scientists and developers who don’t have to code from the scratch their projects and in fact, can concentrate more on the complex algorithms and work with large datasets.

One such library is the “NumPy” library. It is short for Numerical Python and is the fundamental package for scientific computing with Python. NumPy guarantees great execution speed comparing it with python standard libraries. It comes with a great number of built-in functions. It provides a high-performance multidimensional array object and tools for working with these arrays. …


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Just as the trees are a vital part of human life, tree-based algorithms are an important part of machine learning. The structure of a tree has given the inspiration to develop the algorithms and feed it to the machines to learn things we want them to learn and solve problems in real life. These tree-based learning algorithms are considered to be one of the best and most used supervised learning methods. Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. …


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Living in this pandemic has made almost all of us very frequent online shoppers on platforms like Amazon, some of us more frequent Netflix viewers, and some of us spending more time watching Youtube videos if we weren’t doing all of that already(as in my case ;-) and we can’t help but notice the recommendation systems that these e-commerce websites/apps use. Besides increasing sales for these companies, it has actually helped us(the customers) a lot especially now, when we have to manage everything from home, alongside working all day from home.

That means there is no need for making grocery lists anymore or wasting a great deal of time searching for the right kind of clothes for your babies. You make a click at one item, other items pop up recommending us to buy which other customers like us have bought. …


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Linear regression Model

When learning about the linear regression model, the first thing that comes to mind is a linear equation. So it does not matter If you are from a computer science background or not, we all have some understanding of this mathematical concept, as anybody who has passed out middle school is capable of understanding the simple linear regression model.

To understand the linear regression, let us first explore the meaning of regression. By definition, regression is a measure of the relation between the mean value of one variable and the corresponding values of other variables. It is a method of modeling the relationship between the dependent variable and independent variables. …

About

Mamta Singhal

Software Engineer, Machine Learning Enthusiast

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