This is another specialization program offered by Coursera. This specialization program is for both computer science professionals and healthcare professionals. In this specialization program, you will learn how to identify the healthcare professional's problems that can be solved by machine learning. You will also learn the fundamentals of the U.S. healthcare system, the framework for successful and ethical medical data mining, the fundamentals of machine learning as it applies to medicine and healthcare, and much more. This specialization program has 5 courses. Let's see the details of the courses-
By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you've completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization.
Specifically, Huerta and his then graduate student Daniel George pioneered the use of so-called convolutional neural networks (CNNs), which are a type of deep-learning algorithm, to detect and decipher gravitational-wave signals in real time. Roughly speaking, training or teaching a deep-learning system involves feeding it data that are already categorized--say, images of galaxies obscured by lots of noise--and getting the network to identify the patterns in the data correctly. After their initial success with CNNs, Huerta and George, along with Huerta's graduate student Hongyu Shen, scaled up this effort, designing deep-learning algorithms that were trained on supercomputers using millions of simulated signatures of gravitational waves mixed in with noise derived from previous observing runs of Advanced LIGO--an upgrade to LIGO completed in 2015. For instance, Adam Rebei, a high school student in Huerta's group, showed in a recent study that deep learning can identify the complex gravitational-wave signals produced by the merger of black holes in eccentric orbits--something LIGO's traditional algorithms cannot do in real time. In a preprint paper last September, Nicholas Choma of New York University and his colleagues reported the development of a special type of deep-learning algorithm called a graph neural network, whose connections and architecture take advantage of the spatial geometry of the sensors in the ice and the fact that only a few sensors see the light from any given muon track.
Deep learning software is revolutionizing the technology space by bringing in more accuracy and speed for data processing and making predictions and classifications. It uses the concept of AI and ML to help businesses, organizations, research facilities, and universities gain intelligence from data and use it to drive their innovations. The reason it's evident in this modern era is that people find solutions to ease their lives and perform tasks faster. Also, automation is taking over the world. That said, advanced products and services created using AI, Ml, and deep learning can fulfill this demand. Deep learning is an excellent emerging technology that can transform your business by accelerating your data analysis and predictive intelligence. In this article, we will explore the topic more and find the best deep learning software to include in your tool kit.
In this course, you will learn the basics of deep learning and how to build your first deep learning model using Keras. This course will teach supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library. The course material of this course is available free, but for a certificate, you have to pay. In this course, you will also learn how do neural networks learn and what are activation functions.
From their early days at MIT, and even before, Emma Liu '22, MNG '22, Yo-whan "John" Kim '22, MNG '22, and Clemente Ocejo '21, MNG '22 knew they wanted to perform computational research and explore artificial intelligence and machine learning. "Since high school, I've been into deep learning and was involved in projects," says Kim, who participated in a Research Science Institute (RSI) summer program at MIT and Harvard University and went on to work on action recognition in videos using Microsoft's Kinect. As students in the Department of Electrical Engineering and Computer Science who recently graduated from the Master of Engineering (MEng) Thesis Program, Liu, Kim, and Ocejo have developed the skills to help guide application-focused projects. Working with the MIT-IBM Watson AI Lab, they have improved text classification with limited labeled data and designed machine-learning models for better long-term forecasting for product purchases. For Kim, "it was a very smooth transition and … a great opportunity for me to continue working in the field of deep learning and computer vision in the MIT-IBM Watson AI Lab." Collaborating with researchers from academia and industry, Kim designed, trained, and tested a deep learning model for recognizing actions across domains -- in this case, video.
To mark the occasion, we sat down with course designer and software engineer Sourabh Bajaj (previously Neeva, Google, Coursera) to talk about the evolution of the ML role, how he designed the course to connect with today's business needs, and how he thinks students can apply the covered topics at the end of each course! Sourabh: A few big changes that have happened in the space is that early on, ML engineers were spending a ton of time in model development. And in some sense, the ML engineer role itself didn't exist--it was more common that you could find a ML researcher role, where you would be responsible for cleaning data, productionizing your models, and building models and iterating of them. This role was primarily driven by a lack of infrastructure, where there was not great tooling for ML. And, even if the tooling existed, it was much harder to productionize these models.
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
When I started my first job out of college, I thought I knew a fair amount about machine learning. I had done two internships at Pinterest and Khan Academy building machine learning systems. I spent my last year at Berkeley doing research in deep learning for computer vision and working on Caffe, one of the first popular deep learning libraries. After I graduated, I joined a small startup called Cruise that was building self-driving cars. Now I'm at Aquarium, where I get to help a multitude of companies deploying deep learning models to solve important problems for society.
In this course, you will learn NLP (natural language processing) with deep learning. This course will teach you word2vec and how to implement word2vec. You will also learn how to implement GloVe using gradient descent and alternating least squares. This course uses recurrent neural networks for named entity recognition. Along with that, you will learn how to implement recursive neural tensor networks for sentiment analysis. Let's see the topics covered in this course-