Machine Learning with TensorFlow on Google Cloud Platform Coursera

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What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models.


Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

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Background: Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents. Objective: We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology. Methods: We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space.


Logistic Regression with UCI Heart Disease Data

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Continuing my series on the UCI Heart Disease Data, is a tutorial on how to transform a logistic regression into a human transformable test. The tutorial can be found here. I talk about what logistic regression is, in addition to how to use it to make predictions.


Machine Learning Practical: 6 Real-World Applications

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Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience. If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter's eyes, then you came to the right place! This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science. What you'll learn You will know how real data science project looks like You will be able to include these Case Studies in your resume You will be able better market yourself as a Machine Learning Practioneer You will feel confident during Data Science interview You will learn how to chain multiple ML algorithms together to achieve the goal You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib You will learn Logistic Regression You will learn L1 Regularization (Lasso) You will learn Random Forest Classifier Udemy Promo Coupon 95% off Discount Machine Learning Practical: 6 Real-World Applications


Classification and Regression Analysis with Decision Trees

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A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. As the name suggests, we can think of this model as breaking down our data by making a decision based on asking a series of questions. Let's consider the following example in which we use a decision tree to decide upon an activity on a particular day: Based on the features in our training set, the decision tree model learns a series of questions to infer the class labels of the samples. As we can see, decision trees are attractive models if we care about interpretability. Although the preceding figure illustrates the concept of a decision tree based on categorical targets (classification), the same concept applies if our targets are real numbers (regression).


Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models IntechOpen

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The first chapter is the Introductory chapter. The second chapter aims to provide an update of the recent advances in the field of rational design of PDE inhibitors. The third chapter includes designing a series of peptidic inhibitors that possessed a substrate transition-state analog and evaluating the structure-activity relationship of the designed inhibitors, based on docking and scoring, using the docking simulation software Molecular Operating Environment. The aim of the forth chapter is to develop structure-property relationships for the qualitative and quantitative prediction of the reverse-phase liquid chromatographic retention times of chlorogenic acids.


Inside a Data Scientist's ToolBox: Top 9 Data Science Algorithms - DataFlair

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In a Data Science interview, the interviewer asked me, how would you explain top data science algorithms to a non-tech person. I told him that Data science is…..(read the article to know:D). The explanation is too simple that you can easily understand. We will discuss mostly machine learning algorithms that are important for data scientists and classify them based on supervised and unsupervised roles. I will provide you an outline for all the important algorithms that you can deploy for improving your data science operations.


Samsung's new 'living picture' technology is so good it's scary

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The latest research, conducted by Samsung's AI Centre, looked specifically at making a system that can recreate lifelike motion from only one single frame of a person's face. This basically means, using a still image of either a painting or just a normal photograph to make it appear as if it is speaking.


Graph Convolutional Networks for Geometric Deep Learning

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Graph convolutions are very different from graph embedding methods that were covered in the previous installment. Instead of transforming a graph to a lower dimension, convolutional methods are performed on the input graph itself, with structure and features left unchanged. Since the graph remains closest to its original form in a higher dimension, the relational inductive bias is therefore much stronger. There is a type of inductive bias in every machine learning algorithm. In vanilla CNNs for example, the minimum features inductive bias states that unless there is good evidence that a feature is useful, it should be deleted.


Indian startup RealTell launches gamification for Retailers

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Offline retail is here to stay," says founder of in-store experiential service. Retail tech startup RealTell is launching its flagship product "Realtell Retail" for fashion and lifestyle brands. "Offline retail is here to stay," said Ashish Mittal, chief mentor at Turning Ideas Ventures (which incubated RealTell) "and this startup helps retailers, primarily the fashion and apparel brands, to drive footfall and increase basket size by gamifying the offline shopping experience." The company was started by two young college entrepreneurs from Shri Ram College of Commerce (SRCC) Delhi University, Sanyam Gupta and Shivendra Misra. In a statement, the company said its kiosk solutions will help shoppers to discover new combo prices for the fashion and other retails products every day, based on artificial intelligence and machine learning, driving footfall to stores and turning the buying experience into a game. Press materials released by the brand observed that while online retailers have detailed information about shoppers – because every click can be tracked – offline retail stores lose out on valuable shopper insights due to the lack of proper infrastructure and technology. With its patented technology, RealTell Retail gamifies the shopping experience by letting shoppers discover a dynamic price for products of their choice. "Imagine walking into a store, scanning your items at a kiosk and saving money at each visit," said Misra. "What's exciting is that the prices and the offers change every day.