How To Learn Data Science From Scratch?
This post will cover full-stack data science, analytics, Python, statistics, and data science courses as well as how to study data science from the beginning.
28
Feb
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22
Feb
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
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
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
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.
13
Feb
The Importance Of Strong Passwords And Password Management
In today’s digital age, the need for strong passwords and effective password management has never been greater. With increasing numbers of online accounts, it is important to take the necessary steps to protect personal and sensitive information from the risk of cyber-attacks.
09
Feb
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
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
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.
02
Feb
The Basics Of Cybersecurity For Non-experts
Cybersecurity is a critical aspect of our digital lives. It refers to the protection of personal and sensitive information from unauthorized access, use, disclosure, disruption, modification, or destruction. With the increasing use of technology in our daily lives, it’s important for everyone to understand the basics of cybersecurity. Here are some essential concepts that every non-expert should be aware of.