can u please give me the highlighted points from the below pasted text just give what are the things i'll be learning and their definitions ignore the tutor name and all that stuff

>> I want you to meet my friends, Younes and Lukasz, the instructors of this specialization. >> Hi, everyone. >> Welcome to the specialization. >> I know Younes for many years. He is an instructor at Stanford University and has also helped me create the deep learning specialization. Over the years, Younes unice has taught hundreds of students learning AI and machine learning and NLP and other topics for the first time and he's also mentor students on a huge variety of AI related projects. So I really thrilled to have you with us. >> Thanks for the introduction and it is always great working with you. >> Lukasz is a member of the Google Brain team where he does research on deep learning and NLP. So everything from machine translation to parsing, he is the co-author of Google's TensorFlow system as well as of the transformer network. And so his work has had a huge influence on all of NLP and AI. I'm really excited to also have Lukasz as an instructor of the specialization. >> Thanks for the introduction, Andrew. >> Younes and Lukasz will walk you through the most important concepts in natural language processing. NLP has changed a lot over the last several decades. The field has started off using primarily rule-based systems where someone might code of a rule like you see the word good, then assume this is a positive customer review to then using probabilistic systems that perform much better. But still require a lot of hand engineering to know where NLP relies much more on machine learning and deep learning. >> More recently with the rise of powerful computers, we can now train end-to-end systems that would have been impossible to train a few years ago. We can now capture more complicated patterns and we can use these models in question answering in chatbots, and then other applications. >> Many of these applications are built with attention models, which you are going to learn in this specialization. A few years ago, these models would take weeks or even months to train. But with attention, you can train these models in just a few hours. >> In this specialization, you learn to build NLP technologies including the same technologies as what's deployed in many large commercial systems. In the first course, you learn about classification and vector spaces. Younes, can you tell us a bit about the first course? >> Sure, in course one, you will learn to distinguish between pieces of text with positive sentiments and negative sentiments. You will do so using logistic regression and naive Bayes classifiers. You will also learn to represent words, queries and documents including other pieces of text as numbers in vectors. You will build your first machine translation system and you will learn about locality sensitive hashing which is just a method, that will help you with efficient search. >> Thanks Younes. After the first course, you then go on to the second course in which you learn probabilistic models in NLP. So these are things like given a few words, what is the probability of the next word being the word the versus of, verus something else. So these are algorithms that all of us are maybe using almost every day.
Play video starting at :3:22 and follow transcript3:22
And in this specialization, you will learn how to build these algorithms yourself. After the first two courses with the foundation that that gives you, you then go on to the third course which teaches you sequence models. And finally, in the fourth course, you learn about attention models and this will take you up to state of the art NLP models useful things like chatbots question answering and text summarization. Younes, you want to say a few words about that? >> By the time you complete course four, you will be able to implement state of the art neural machine translation, summarization, question answering and chatbots. We will show you how you can combine attention and parallel computing to build these systems from the ground up. These models have high impact in industry. They could be used, for example, to automate call centers or businesses can use them to make sense of large volumes of data. >> I'm really excited for everyone to get started to take these courses, learn these technologies and to go build some great NLP systems. >> Let's get started. >> Awesome, see you in the classroom. >> Let's jump in.