“This is the 17th day of my participation in the Gwen Challenge in November. Check out the details: The Last Gwen Challenge in 2021.”
🌊 Author’s home page: Haiyun 🌊 Author profile: 🏆CSDN full stack quality creator, 🥇HDZ core group member 🌊 Fan benefits: fans send four books every week, a variety of small gifts every month
Machine learning is a hot topic today! In general, almost every “tantalizing” new development in computer science and software development has to do with machine learning. Microsoft’s Cortana — Machine learning. Object and Face Recognition — Machine learning and Computer Vision. Advanced User Experience Improvement Program — Machine learning.
Not only that. Generally speaking, machine learning and data science are everywhere. So it’s only natural that anyone with a higher-than-average brain and the ability to distinguish programming paradigms through a first peek will be interested in machine learning.
Machine Learning: What exactly is it?
Machine learning is a subfield of artificial intelligence that has evolved from pattern recognition and computational learning theories. Arthur Lee Samuel defined machine learning as the field of study that enables computers to learn without explicit programming.
So it’s basically the realm of computer science and artificial intelligence, “learning” from data without human intervention.
But this view is flawed. Because of this perception, whenever the term machine learning is thrown around, people usually think of “artificial intelligence” and “neural networks that can mimic the human brain (currently, that’s impossible),” self-driving cars and so on. But machine learning is much more than that. Below we reveal some of the expected and often unanticipated aspects of modern computing where machine learning is at work.
Machine learning: Anticipation
We’ll start with some of the places you might expect machine learning to come into play.
- Speech recognition (the more technical term is natural language processing) : You’re talking to Cortana on a Windows device. But how does it understand what you’re saying? Along came the field of natural language processing, or NLP, which deals with the interaction between machines and humans through linguistics. Guess what’s at the heart of NLP: machine learning algorithms and systems (hidden Markov models are one of them).
- Computer Vision: Computer vision is a subfield of artificial intelligence that deals with machines’ (possible) interpretations of the real world. In other words, all facial recognition, pattern recognition, character recognition are computer vision. Machine learning is once again at the heart of computer vision with its wide range of algorithms.
- Google’s self-driving car: HMM. You can imagine what drives it. More machine learning advantages.
But these are the intended applications. Even naysayers will have a good insight into these technological feats brought to life by “arcane (and fiendishly difficult) computer magic”.
Machine learning: accident
Let’s visit some places that the average person wouldn’t easily associate with machine learning:
- Amazon product Recommendations: Ever wonder that Amazon always has a recommendation that just tempts you to lighten the load on your wallet. Well, it’s a machine learning algorithm called a recommendation system that works in the background. It understands each user’s personal preferences and makes recommendations accordingly.
- Youtube/Netflix: They work the same way!
- Data mining/Big data: This may not be all that shocking to many people. But data mining and big data are just manifestations of studying and learning data on a larger scale. As long as you have the goal of extracting information from data, you will find machine learning nearby.
- Stock markets/housing finance/real estate: All of these areas contain a lot of machine learning systems to better evaluate markets, “back to technology,” for something as banal as predicting house prices, to predicting and analyzing stock market movements.
As you may have seen by now. Machine learning is virtually everywhere. From research and development to improving the business of small companies. It’s everywhere. So it makes up for quite a few career options because the industry is on the rise and benefits aren’t going to stop anytime soon.
That concludes the second session of machine learning. I’ll delve into some of the technical details of machine learning, the tools used in the industry, and how to start your machine learning journey. Go boy!
Attention to the public account [haiyong] reply [get the book] lucky draw to send a book, until the evening of the 18th 20:00
Write it at the end
The author is determined to build a fishing site with 100 small games, update progress: 40/100
I’ve been blogging about technology for a long time, mostly through Nuggets, and this is my article on machine learning from the ground up [section 2]. I like to share technology and happiness through articles. You can visit my blog at juejin.cn/user/204034… For more information. Hope you like it! 😊
💌 welcomes your comments and suggestions in the comments section! 💌