Learn Machine Learning And Data Science With Python In 2021

由于新的计算技术，今天的机器学习不像过去的机器学习。它诞生于模式识别和计算机无需编程即可执行特定任务的理论；对人工智能感兴趣的研究人员想看看计算机是否可以从数据中学习。机器学习的迭代方面很重要，因为当模型暴露于新数据时，它们能够独立适应。他们从以前的计算中学习，以产生可靠、可重复的决策和结果。这是一门并不新鲜的科学——而是一门获得新动力的科学。
Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 5.51 GB | Duration: 15h 38m

What you’ll learn
Learn About Machine Learning Algorithms
Improve your data science and coding resume
Create robust data science medk

Description
ecause of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car? The essence of machine learning.

Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.

Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.

Fraud detection? One of the more obvious, important uses in our world today.