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Break through language barriers with Amazon Transcribe, Amazon Translate, and Amazon Polly

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Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens to your business when you're no longer bound by language? It's common today to have virtual meetings with international teams and customers that speak many different languages. Whether they're internal or external meetings, meaning often gets lost in complex discussions and you may encounter language barriers that prevent you from being as effective as you could be.


Hands-on Random Forest with Python

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. One model may make a wrong prediction.


AI at Rescue: Claims Prediction

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The insurance industry is one of the early adopters of vanilla algorithms such as Logistic Regression.


5 Advantages and Disadvantages of Python and its Applications - Infosarea

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Python is a widely used and well-understood programming language, which has a large number of libraries and frameworks. Python is often used because of its simple syntax, fewer bugs, and flexibility. Python is a revolutionary and very versatile computer programming language. Many big firms like Google and NASA are using this language. Python is a cool programming language, so much so that Google uses it to build its own software. Not only is it a great language for beginners to learn, but it's also a solid language for experts to use too.


Machine Learning for OpenCV 4: Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn, 2nd Edition: Sharma, Aditya, Shrimali, Vishwesh Ravi, Beyeler, Michael: 9781789536300: Amazon.com: Books

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Michael Beyeler is an Assistant Professor at the University of California, Santa Barbara, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis ("bionic eye"). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. Michael is the author of four programming books focusing on computer vision and machine learning. He is also an active contributor to several open-source software projects, and has professional programming experience in Python, C/C, CUDA, MATLAB, and Android. Michael received a Ph.D. in Computer Science from the University of California, Irvine as well as a M.Sc. in Biomedical Engineering and a B.Sc. in Electrical Engineering from ETH Zurich, Switzerland.


Netflix Recommendation System using Python

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Netflix is a subscription-based streaming platform that allows users to watch movies and TV shows without advertisements. One of the reasons behind the popularity of Netflix is its recommendation system. Its recommendation system recommends movies and TV shows based on the user's interest. If you are a Data Science student and want to learn how to create a Netflix recommendation system, this article is for you. This article will take you through how to build a Netflix recommendation system using Python.


TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models: Tung, KC: 9781492089186: Amazon.com: Books

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The TensorFlow ecosystem has evolved into many different frameworks to serve a variety of roles and functions. That flexibility is part of the reason for its widespread adoption, but it also complicates the learning curve for data scientists, machine learning (ML) engineers, and other technical stakeholders. There are so many ways to manage TensorFlow models for common tasks--such as data and feature engineering, data ingestions, model selection, training patterns, cross validation against overfitting, and deployment strategies--that the choices can be overwhelming. This pocket reference will help you make choices about how to do your work with TensorFlow, including how to set up common data science and ML workflows using TensorFlow 2.0 design patterns in Python. Examples describe and demonstrate TensorFlow coding patterns and other tasks you are likely to encounter frequently in the course of your ML project work.


[100%OFF] Python For Machine Learning: The Complete Beginner's Course

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To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.


Build your first machine learning model with python

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Hi Folks, Machine learning and Artificial intelligence feel like an alien from another planet or rocket science which we never wanted to explore. Let me make it easy for you like your first "Hello World" program. In this blog, we will create a simple model to predict the price of a house depending on the size of the house. There are some rules to follow while building a model. We will start with the first step of preparing our data to train our model.


R vs Python for machine learning

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Machine learning (ML) is one of the most profitable sectors of software development right now. That's because of how useful machine learning techniques are in the rapidly growing field of data science. Data science, a field of applied mathematics and statistics, gleans useful information by the analysis and modeling of large amounts of data. Machine learning involves developing computer systems that learn and adapt using algorithms and statistical models. Applying ML techniques to data science makes it possible to advance from insights to actionable predictions.