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11
Mar

0

The Impact Of Data Science In Transforming Industries And Changing Lives

The world we live in is being transformed by data science quickly. Data science is becoming increasingly important because it enables businesses to use the information they collect to better their operations, develop new products and services, and enhance their decision-making process. Below are some of the industries that data science has been transforming recently:

22
Feb

0

Supervised Vs. Unsupervised Learning: Understanding The Differences

Algorithms and statistical models are used in the field of machine learning to help computers learn from data. The distinction between supervised and unsupervised learning is essential in machine learning. In this article, we will look at the differences between these two approaches and when to use each one.

20
Feb

0

All Machine Learning Algorithms You Should Know In 2023

The significance of machine learning is only going to rise in the coming years in tandem with the rising complexity of data and the growing demand for automation. In this article, we will discuss a few of the most significant machine learning algorithms you should be familiar with by 2023.

16
Feb

0

Machine Learning Vs. Deep Learning: What Is The Difference?

Two of the most talked-about subfields of artificial intelligence (AI) are machine learning and deep learning. They are not the same thing, even though they are frequently used interchangeably. Businesses and organizations looking to implement AI-based solutions need to know the difference between the two.

15
Feb

0

The Benefits And Limitations Of Cloud Security

Cloud security refers to the measures taken to protect data and applications hosted on cloud computing platforms. It offers several benefits such as scalability, flexibility, cost-effectiveness, and accessibility. However, it also has limitations that need to be considered.

09
Feb

0

How To Tune The Hyperparameters

Usually, knowing what values you should use for the hyperparameters of a specific algorithm on a given dataset is challenging. That’s why you need to explore various strategies to tune hyperparameter values. With hyperparameter tuning, you can determine the right mix of hyperparameters that would maximize the performance of your model.

08
Feb

0

How To Makes Use Of Domain Knowledge In Data Science: Examples From Finance And Health Care

The domains of finance and health care don’t have much in common except for one thing - the involvement of data scientists and machine learning experts, who are changing the way both these domains work. From helping them collect, organize, and process a massive volume of data and making sense of it to letting them make efficient and faster data-driven decisions, a lot is happening to disrupt both these domains. Let’s consider some examples from both the finance and healthcare sectors to understand how the application of data science is helping them.

07
Feb

0

A Brief History Of AI

It’s normal today to talk about the massive computing power of supercomputers, the domain of data science that facilitates data availability and analysis, among others, and AI that can mimic mental actions similar to humans. But the road to the modern world’s AI, big data, and deep learning has been a long one. Let’s take a tour down the historical avenues to find how AI evolved into what it is today.

01
Feb

0

How Many And Which Programs Should I Learn For Being A Skilled Data Scientist?

To become a data scientist, you should have knowledge of a variety of programming languages, which include Python, R, Java, SQL, JavaScript, C/C++, and Scala, to name a few. But why do you need to learn these programming languages? Let’s find out the answers by taking a look at the top programs you should learn to make your career path in data science a smooth-sailing one

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