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Ensemble Machine Learning in Python: Random Forest, AdaBoost

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Free Coupon Discount - Ensemble Machine Learning in Python: Random Forest, AdaBoost, Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python Created by Lazy Programmer Inc. Students also bought Unsupervised Deep Learning in Python Machine Learning and AI: Support Vector Machines in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Deep Learning Prerequisites: Linear Regression in Python Preview this Udemy Course GET COUPON CODE Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Top AI Resources You Must Follow If You Are Into AI

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With the vast amounts of research and development occurring every day in Machine Learning, it can be challenging for industry professionals solving real-life use-cases and academic researchers to keep up with all the recent innovations in their specific field of interest. Having struggled for a long time, after a series of exploration, I finally stumbled upon a handful of resources that I wish to share in this post. The list I am proposing here is not exhaustive. However, I am sure they will be sufficient to keep you updated-- eliminating all the heavy lifting of scanning the list of published papers yourself. The resources I will discuss in this post encompass video, newsletter, and podcast formats.


Hands-on intro to Language Processing (NLP)

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This article discusses three techniques that practitioners could use to effectively start working with natural language processing (NLP). This will also give good visibility to people interested in having a sense of what NLP is about -- if you are an expert, please feel free to connect, comment, or suggest. At erreVol, we leverage similar tools to extract useful insights from transcripts of earnings reports of public corporations -- the interested reader can go test the platform. Note, we will present lines of codes for the reader interested in replicating or using what is presented below. Otherwise, please feel free to skip those technical lines as the reading should result seamless.


How Artificial Intelligence Can Help in Improving Healthcare

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Despite the significant advances made in computer science in the last decades, which are daily improving medical services and research, patient care has always been about human-to-human interaction and empathy. However, through artificial intelligence, medical professionals can obtain more accurate patient information and make better decisions. The application of sophisticated mathematical algorithms is going far beyond the collection of information. Artificial intelligence now has the ability to learn, distinguish patterns and find substantive inconsistencies usually invisible to the human eye. IBM Watson, the supercomputer, is one of the best examples of the practical application of AI in healthcare.


Top Trending Artificial Intelligence AI Technologies in 2022

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A brand-new area of computer science was initially referred to as "artificial intelligence" in 1955. Many daily jobs are being replaced by artificial intelligence, requiring less human involvement. But what precisely is this new AI technology? AI refers to the process of teaching a computer system to function and think like a human brain. This is often accomplished through reinforcement learning, in which the computer learns from past errors and observed patterns.


What Are Transformer Models In Machine Learning - Big Data Analytics News

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Machine learning refers to a data analysis method, automating analytical model building. This artificial intelligence branch is based on the concept that computer systems can learn from data, identifying patterns, and making decisions with minimal to zero human intervention. Intelligent systems are built on machine learning algorithms to learn from historical data or past experience. Machine learning applications include image recognition and speech recognition, valuable in various industries such as medicine, e-Commerce, manufacturing, and education. In this article, you'll learn more about transformer models in machine learning. The transformer refers to a deep learning model, utilizing the mechanism of attention used in natural language processing (NLP), a branch of artificial intelligence (AI) that deals with the interaction between humans and computers using the natural language.


Enriching Customer Service Using Sentiment Analysis - DataScienceCentral.com

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As this century progresses, businesses are discovering that the most incredible way to gain the best customer service is to know them deeply. With AI advancing at an exponential rate, it's become possible for companies to use artificial intelligence (AI) to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading to increased efficiency in customer service throughout different industry vertices. Machine learning and AI-based interactive voice response systems have created a new paradigm for what customers and customer service agents can expect from these technologies. When applied correctly, artificial intelligence will enhance the customer experience in various ways, from identifying their interests through sentiment analysis to gathering data about their preferences. AI is the production and display of intelligence by computers and machines instead of humans.


Theory Behind the Basics of NLP - Analytics Vidhya

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This article was published as a part of the Data Science Blogathon. Natural Language Processing (NLP) can help you to understand any text's sentiments. This is helpful for people to understand the emotions and the type of text they are looking over. Negative and Positive comments can be easily differentiated. NLP wanted to make machines understand the text or comment the same way humans can.


Data Scientist Intern (H/F)

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You should be able to work in a team and show high motivation. This job requires autonomy and curiosity toward a changing environment. Flexibility: Team members can work from home, up to 4 days a week, and have the opportunity to work with colleagues around the world. Work environment: SESAMm is multicultural, with technology, sales, and management teams in the US, France, and Tunisia making important contributions to the company's growth. Career development: SESAMm is growing quickly, which means the opportunities for your own growth are continually expanding, and that you can shape the company's culture and evolution.


data-science-transformers-for-natural.html

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Welcome to Data Science: Transformers for Natural Language Processing. Ever since Transformers arrived on the scene, deep learning hasn't been the same. We've reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and more We've created multi-modal (text and image) models that can generate amazing art using only a text prompt We've solved a longstanding problem in molecular biology known as "protein structure prediction" In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work. This is different from most other resources, which only cover the former. In this section, you will learn how to use transformers which were trained for you.