DeepLearning.AI NLP Learner Community Event ft. Mo Rebaie

Hello everyone and welcome TO Deep Learning AI's online event series for NLP Learner Community. I'm very honored and excited to be your guest speaker today. I'll be speaking for the first 20 minutes and then I'll be answering your questions on Slack the rest of the time. So don't forget to post comments or questions and upvote questions on Slack. I'll be presenting my personal AI journey. Then I talk about the NLP Deep Learning AI specialization at Coursera and then I'll share with you some recommendations. So let's start presenting. Okay, so welcome to the nlp learner community event by deep learning AI. Okay, so on the agenda I'll cover my personal journey and then my experience in Deep learning. After that I'll talk about Deep Learning AI NLP specialization and I'll give you some recommendations and then I'll tell you about how you would start your career path in AI. So let's start by my personal journey. I'm very excited to share with you my journey. My educational background is in mathematics. I studied in Lebanese American University in Mathematics and Computer Science, a Bachelor's degree in Mathematics and Computer Science. Also I studied in the American University of Culture and Education with a Bachelor's degree in Management Information Systems. I graduated in 2012. Regarding my learning path, it's like a real life story. I started with AI on 2012, 2015 I made some AI researchers teaching mathematics and reading AI blogs and AI papers. And in 2016 I started to take machine learning courses and accomplished traditional machine learning courses from various platforms. Then in 2017 I started with a deep learning research. It was the kickstart in deep learning and I started to apply my knowledge to make researches in deep learning. With The Rise of AI as you know between 2016 and 2017 many new deep learning deep learning techniques were released by this time. So I started to make some deep learning research and in 2018 I continued to take machine learning and deep learning courses and accomplished machine learning courses at Coursera. And also I accomplished the Deep Learning specialization by Deep Learning AI taught by Andrew Ng. In 2019 I continued this amazing journey, also continue taking machine learning and deep learning courses and accomplished more than 30 machine learning courses at Coursera and I took the AI for Everyone course by deep learning AI in 2020 I continued my journey with deep learning AI. I accomplished the TensorFlow and practice specialization. Also I accomplished the TensorFlow data and deployment specialization to learn how to deploy deep learning models and put them into production and those specializations were offered by Are Offered by Deep Learning AI on Coursera platform. So regarding my career path, I started in 2016 at Rebuy Analytics Group as a data scientist. I've started to build predictive models by utilizing machine learning techniques and in 2019 I was promoted to a deep learning researcher and I start building end to end deep learning services and systems and prototype real applications in deep learning and also deployment those models to put them into production. In 2019 I started my career at Coursera. It was very amazing and still I'm working with Coursera. I started as a beta tester at Coursera reviewing Coursera courses before they are open to the public and give feedback to instructors to optimize some content in the courses. And in 2020 I started with AI readiness as a community mentor and I have right now a huge activity in AI communities on deep learning AI also Coursera also on AI readiness. So I started as a community mentor at AI readiness helping enterprises, startups and individuals adopt to AI transformation. Also later on in 2020 I started to work at Workera as an alpha tester reviewing AI questions in machine learning and deep learning and mathematics and data science for tests and data assessments and give feedback before the data assessments launched on the platform on Workera AI platform. Also in 2020 I've started my journey as an instructor at Coursera after taking a huge number of courses. After accomplishing a lot of machine learning and deep learning courses, I was selected to work honorably as an instructor at Coursera in creating and delivering AI and data science guided project courses built using RHINE platform and I'm still working as an instructor at coursera. Also in 2020 at the beginning of this year I started to work as AI ambassador at Deep Learning AI managing and hosting local and online by and AI events and I've started to work as a mentor for the deep learning specializations. I'm currently working as a mentor for the NLP deep learning AI specialization and this is just, just the start and this journey is like a life cycle. I'll continue my journey with AI and I will continue growing my career. Okay, so my for my experience in deep learning I started to build neural networks from scratch and computer vision also in NLP and computer vision I've worked with image analysis like convolutional layers and image processing. Also I'm working with machine vision and created a lot of computer vision applications and amazing deep learning models like object detection, like image classification, also like pose estimation, like age detection and facial recognition and more. In the NLP field I work with information extraction from text and documents and also speech processing and I build speech recognition system. Also I created and developed various models using different concepts like text pre processing like feature extraction from texts, like linear models or sentiment analysis like span filtering for example and sequence tagging with the probabilistic models and many more. So this is my experience in deep learning and currently also I'm growing my career and trying to develop more deep learning algorithms. Moving on to deep learning AI NLP Specialization Many learners are asking about okay, what we will benefit from taking this NLP specialization. What I would like to share with you is that you will benefit a lot from taking this specialization because after accomplishing this NLP specialization, a learner will be able to apply NLP algorithms in the real world. You can apply NLP as enhancement for grammar checking software like Grammarly and Grammarly is now being a famous software for grammar checking. It's based on NLP algorithms. Also we can apply NLP as a better human computer interface that could convert from a natural language into a computer language and vice versa. You will be able to implement those techniques after accomplishing this deep learning NLP specialization. Also for a travel agent to use in making reservations. Also we can apply NLP as an add on for language translation program that could translate from one human language to another, for example from English to Italian. Also you'll be able after accomplishing this NLP specialization to apply a computer to work with a computer that could understand and process human language. And when we say that a computer understanding a human language, this is an advanced level in AI and nowadays NLP is being analyzed because. It's working with the communication between people and machines. So after accomplishing this specialization you will apply NLP algorithms in real world. Also you can contribute to creating a robot which will communicate with people and like read your sentiment for example. Also I'll mention some NLP project ideas that will help you take a step forward in the right direction after you accomplish this NLP specialization. Like building a customer support bot on a website or on an online platform, for example. One of the best ideas is to start experimenting hands on NLP projects for students who are working on customer support bot and you will see it in many online platforms and various businesses. Another example is creating a language identifier. Have you noticed that Google Chrome for example can detect which language in which a web page is written? That's related to NLP also. Also you can work with a machine learning powered autocomplete feature. With the rise of automated system, you can leverage your learnings And NLP with machine learning and build automated systems also like for example the trigger word detection and speech recognition. Also you can build a predictive, for example predictive text generator which is one of the interesting NLP projects. So you can apply your NLP algorithms after learning nlp. Through taking this specialization in real world, you can also build powerful NLP applications. One of these applications is text classification. You will learn how to build a text classifier and it's to classify the topic or the theme of a document. And for example, like spam filtering email text is in the emails text is spam or not spam. Also language identification, classifying the language of a source text that you may have. So here we are talking about text classification. Another example or application of NLP is language modeling. And language modeling is really a subtask of more interesting NLP problems. Specifically those that condition the language model on some other inputs. And here you will predict the next word given the previous words. As you can see on LinkedIn or on WhatsApp, if you are typing a message, you will see that the algorithm is predicting the next word while you are typing a message for someone. Also, one of top NLP applications is speech recognition. As you know, we all heard about speech recognition which is letting the machine understanding what we are talking and also communication communicating with us. Also we have the caption generation for example which is problem of describing the contents of an image. Giving let's say a digital image such as a photo, it will generate a textual description of the contents of the image. Moreover, we have a famous example which is machine translation as being one of the most in demand NLP applications. And we have many more NLP applications in the real world. Also the third point, after accomplishing this NLP specialization, you will be able to build a strong portfolio and apply for jobs. So let's move on to my recommendations for learners who are taking this NLP specialization. Also for learners who are taking online courses. Always embrace curiosity and learn new things. As you know, with the rise of AI you have to stay like up to date. To improve your skills and knowledge. Keep learning new concepts in AI and deep learning. The other recommendation is to take online courses by deep learning AI on Coursera. Also there are many online platforms providing AI courses. Coursera is one of the top platforms. And with Coursera you can audit any course for free. You can apply for financial aid if you can't afford to pay and earn your certificate for for free. Also feel free to learn any tool or framework. You might find yourself familiar with Python or R or matlab. Just learn the tool that you find yourself familiar working with. Don't learn many tools just to save time. Learn the favorite tool you would like to work with. Also, don't just collect certificates. Share your successes and achievements with others, but don't just collect certificates. You also need to really understand what you are learning and put that into practice. Also read continuously and don't stop reading. Blogs and articles are even very good to understand. Understand some concepts that you may not understand while taking online courses. Also, I would like to encourage you to leverage social media, follow and interact with leaders, with experts, with your colleagues. Always meet new people. Also, time management is essential if you want to take a course, but your time. If you want to read a blog, to read a paper, make time management. Also join famous AI communities like Coursera Community and Deep Learning AI Community. So those are my recommendations. Okay now if you want to start your career path, I just highlighted some titles job titles in nlp. After you accomplish this NLP specialization, you can work as NLP Data scientist. Also can work as NLP engineer and NLP engineer is responsible for the interaction between everyday human language and computers ability to process and analyze natural language data. You can work as NLP researcher to do some researches and develop some existing algorithms and leverage NLP algorithms in real industries. Also you can work as NLP Data analyst and that NLP data analysts are experienced in NLP and able to work with text analysis and text classification. Who can work as NLP expert having a vast experience in creating and developing NLP algorithms. Also you can work as NLP lead but you can lead a data science team in developing NLP algorithms. Okay so I would like to share with you some remote work opportunities for NLP engineers. You can apply through LinkedIn to remote work opportunities and NLP. Also you can apply through simplyhire.com There are many job opportunities and they are all you can find remote work opportunities. Also through indeed.com and through nuvo.com also you can apply for freelance remote work with NLP on upwork. Okay, if you want to prepare for the interviews, I've just collect for you Top questions asked for interviews. One of them is possible features of a text corpus. You will be able to extract features from any type of text. You will learn it in the NLP specialization. Also, if they ask you to list some NLP use cases, you will be able to list them after taking this specialization. One question is what is gated Recurrent Unit language model? You can answer this question after accomplishing course three of this Specialization. Also explain Transformer architecture. You will take it in the fourth course of this specialization how you choose your NLP model. After taking the assignments of the four courses and the specialization, you will be able to answer this question. Explain for example permutation, language models, dependency parsing and text summarization. You will be able to answer those these questions after taking the courses of this specialization. Also, when we use Naive Bias algorithm in nlp, it's covered in course one. So as you can see, all the top questions for NLP engineers who are seeking for jobs are covered in this specialization. For NLP best practices, you can practice text classification and some other projects as shown. Like sentiment analysis, like building a chatbot as we mentioned, like machine translation to prepare for interviews. Okay, so that's it for my presentation. Now I would like to invite you to post your Questions in the August 12 online event channel on Slack in the deep learning AI NLP Slack workplace. So I start reading your questions on Slack and answering them right now. Okay, We have a question. I move just one second. We have a question from Sven Chilton. Okay. Hi Mo, what can you tell us about upcoming Deep Learning AI specializations? Several other learners have been asking if Deep Learning AI will release a computer vision specialization. I personally am more curious about Reinforcement learning since I know very little about it. I heard from someone at Facebook. Okay, Pytorch and Practice specialization. Is that true? What are your other specializations can we look forward to? Okay, Sam, thanks for asking. I'm sure that Deep Learning AI will definitely create new specializations and Deep Learning and keep following Deep Learning AI updates on social media channels to stay up to date with the future announcements Regarding your question about Reinforcement learning, I would recommend you to take the Reinforcement Learning specialization by University of Alberta on Coursera for Pytorch in Practice specialization. I have no idea about it. Indeed, I'm sure that TensorFlow practice specialization by deep learning AI covers almost all deep learning concepts with Keras and TensorFlow. Let's take another question.