Introduction to Machine Learning

 Introduction to Machine Learning

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INTRODUCTION:

Drink to Machine literacy! This week, we introduce the core idea of tutoring a computer to learn generalities using data without being explicitly programmed. 

 We're going to start by covering direct retrogression with one variable. Linear retrogression predicts a real-valued affair grounded on an input value. We bandy the operation of direct retrogression to casing price vaticination, present the notion of a cost function, and introduce the grade descent system for literacy. 

 We’ll also have voluntary assignments that give a lesson on direct algebra generalities. An introductory understanding of direct algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables. However, feel free to take a break or help other scholars out in the forums, If you feel confident in your understanding of direct algebra. 

Welcome to Machine Learning:

 Machine literacy is one of the most instigative recent technologies. You've presumably used a literacy algorithm dozens of times a day without knowing it. Every time you use a web hunt machine like Google or Bing to search the internet, one of the reasons that work so well is because a literacy algorithm, one enforced by Google or Microsoft, has learned how to rank web runners. 

 For me, one of the reasons I am agitated is the AI dream of someday structuring machines as intelligent as you or me. We are a long way down from that thing, but numerous AI experimenters believe that the stylish way to towards that thing is through learning algorithms that try to mimic how the mortal brain learns. 

 So why is machine literacy so current moment? It turns out that machine literacy is a field that had grown out of the field of AI or artificial intelligence. So, machine literacy was developed as a new capability for computers, and the moment it touches numerous parts of the assiduity and introductory wisdom. 

 This is a sign of the range of problems that machine literacy touches. There are independent robotics, computational biology, and tons of effects in Silicon Valley that machine literacy is having an impact on. There is database mining. Handwriting recognition. Learning algorithms are also extensively used for tone-customizing programs. 

Watch this video:

Introduction to Machine Learning:

Drink to this free online class on machine literacy. Machine literacy is one of the most instigative recent technologies. And in this class, you learn about the state of the art and also gain practice enforcing and planting these algorithms yourself. You've presumably used a literacy algorithm dozens of times a day without knowing it. Every time you use a web hunt machine like Google or Bing to search the internet, one of the reasons that work so well is because a literacy algorithm, one enforced by Google or Microsoft, has learned how to rank web runners. 

 

 Every time you use Facebook or Apple's print codifying operation and it recognizes your musketeers' prints, that is also machine literacy. Every time you read your dispatch and your spam sludge saves you from having to wade through tons of spam emails, that is also a literacy algorithm. For me, one of the reasons I am agitated is the AI dream of someday structuring machines as intelligent as you or me. We are a long way down from that thing, but numerous AI experimenters believe that the stylish way to towards that thing is through learning algorithms that try to mimic how the mortal brain learns. I will tell you a little bit about that too in this class. 

In this class, you learn about state-of-the-art machine literacy algorithms. But it turns out just knowing the algorithms and knowing the calculation is not that good if you do not also know how to get this stuff to work on problems that you watch about. So, we have also spent a lot of time developing exercises for you to apply each of these algorithms and see how they work for you. So why is machine literacy so current moment? It turns out that machine literacy is a field that had grown out of the field of AI or artificial intelligence. We wanted to make intelligent machines and it turns out that there are many introductory effects that we could program a machine to do similar to how to find the shortest path from A to. 

 

 But for the utmost part, we just didn't know how to write AI programs to do the more intriguing effects similar as web hunt or print trailing, or dispatch ants-spam. There was a consummation that the only way to do these effects was to have a machine learn to do it by itself. So, machine literacy was developed as a new capability for computers, and the moment it touches numerous parts of the assiduity and introductory wisdom. 

For me, I work on machine literacy and in a typical week I might end up talking to copter aviators, biologists, and a bunch of computer systems people( so my associates then at Stanford), and comprising two or three times a week I get a dispatch from people in the assiduity from Silicon Valley reaching me who have an interest in applying learning algorithms to their problems. This is a sign of the range of problems that machine literacy touches. There are independent robotics, computational biology, and tons of effects in Silicon Valley that machine literacy is having an impact on. Then are some other exemplifications of machine literacy. There is database mining. One of the reasons machine literacy has so transfused is the growth of the web and the growth of robotization All this means that we've much larger data sets than ever ahead. So, for illustration, tons of Silicon Valley companies are moment collecting web click data, also called clickstream data, and are trying to use machine literacy algorithms to mine this data to understand the druggies better and to serve the druggies better, that is a huge member of Silicon Valley right now. Medical records. With the arrival of robotization, we now have electronic medical records, so if we can turn medical records into medical knowledge, also we can start to understand the complaint more. 

Computational biology

With robotization again, biologists are collecting lots of data about gene sequences, DNA sequences, and so on, and machines running algorithms are giving us a much better understanding of the mortal genome, and what it means to be mortal. And in engineering as well, in all fields of engineering, we've larger and larger, and larger and larger data sets, that we are trying to understand using literacy algorithms. The alternate range of ministry operations is bones that we can not program by hand. So for illustration, I have worked on independent copters numerous times. We just didn't know how to write a computer program to make this copter cover by itself. The only thing that worked was having a computer learn by itself how to fly this copter. 

Handwriting recognition

It turns out one of the reasons it's so affordable moment to route a piece of correspondence across the countries, in the US and internationally, is that when you write an envelope like this, it turns out there is a literacy algorithm that has learned how to read your handwriting so that it can automatically route this envelope on its way, and so it costs us a many cents to shoot this thing thousands of long hauls. And, if you've seen the fields of natural language processing or computer vision, these are the fields of AI about understanding language or understanding images. utmost of the natural language processing and utmost of the computer vision moment is applied to machine literacy. Learning algorithms are also extensively used for tone-customizing programs. Every time you go to Amazon or Netflix or iTunes Genius, it recommends the pictures or products and music to you, that is a literacy algorithm. If you suppose about it they have a million druggies; there's no way to write a million different programs for your million druggies. The only way to have software give these tailored recommendations is to come to learn by itself to customize itself to your preferences. Eventually, learning algorithms are being used momentarily to understand mortal literacy and to understand the brain. We will talk about how experimenters are using this to make progress toward the big AI dream. Many months agone, a pupil showed me a composition on the top twelve IT chops. The chops that information technology hiring directors can not say no to. It was a slightly aged composition, but at the top of this list of the twelve most desirable IT chops was machine literacy. Then at Stanford, the number of babes that communicate to to to me asking if I know any graduating machine literacy scholars is far larger than the machine literacy scholars we graduate each time. So I suppose there's a vast, unfulfilled demand for this skill set, and this is a great time to be learning about machine literacy, and I hope to educate you a lot about machine literacy in this class. In the coming videotape, we'll start to give a more formal description of what's machine literacy. And we'll begin to talk about the main types of machine literacy problems and algorithms. You will pick up some of the main machine learning languages, and start to get a sense of what are the different algorithms, and when each bone might be applicable. 

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