Machine learning is having a huge impact in almost all the industries. From Google brain to self-driving cars all are made using a machine learning algorithm. It is currently growing and expanding rapidly and more and more people are learning it.
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Useful tips to get started writing about your data science projects
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Many companies are using machines for promoting, advertising and ultimately finally selling products this has also given the marketers more time to focus on other challenges.
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How do you think data science will change over the next 10 years? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
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R is an open source language. R is one of the most popular programming languages used for statistical analysis and graphics. It provides the variety of statistical and graphical techniques, and it is highly extensible. This language is used by statisticians and data miners for developing statistical software and data analysis.
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Deep Learning is one of the hottest technologies out there. There are many research papers in Deep Learning, and it can be really overwhelming to keep up.
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The first trash can with artificial intelligence and something ever actors want is the same thing.
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The growth and success of the organization totally depend on your ability to collect good amount of data, managing those data which leads into business insights and outcomes. Data is all around only matter is that we have to collect good structured data which is further helpful for the organizations.
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Data Science is quite a large and diverse field. As a result, it is really difficult to be a jack of all trades. Traditionally, Data Science would focus on mathematics, computer science and domain expertise.
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As someone who has been there, I'd like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.
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We asked our LinkedIn group members what their greatest challenges were to becoming fully fledged data scientists. Some of the most common frustrations were:
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Data Science, Machine Learning, Artificial Intelligence and data analytics are quite buzzword. No doubt these technologies will transform all the industries but most likely the technology that will change our profession in the short run will be Robotic Process Automation (RPA).
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AI is on radar next for most of the organisations and it is transforming all the business functions and software development as well. Not only machine learning techniques are used to accelerate the traditional software development lifecycle (SDLC) and this all represent new boundaries for inventing technology.
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Knowing more about what's coming next can be a matter of life or death for communities reeling from a large quake. The aftershocks can often cause further injuries and fatalities, damage buildings, and complicate rescue efforts.
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It has been a while even since I posted on Medium. Having been in Data Science for almost half a year, I've made a lot of mistakes and learned from the mistakes along the wayâ?¦ through the hard way.
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The deep learning market is expected to reach the heights by the year ending 2023. Deep Learning is experiencing a rapid increase in its application across various industries. The marketing industry is one of those sectors that are leveraging deep learning for improvements.
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When I heard about the work at UNDP Global Pulse, I thought it was something straight out of a movie. Data science for social impact sounds like the coolesting thing I can do with mathematics.
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AI tools can significantly reduce the workload of the humans. Or, as with what we did, it is the same level of human effort but with a better effect, with better conservation efficiency.
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When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not!
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MIT's new model is able to detect depression in people just by the way they talk.
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We examine what's important for data scientists in their careers, including challenging work, networking with peers, foreseeing their career path and creating a good work-life balance.
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Check out part 2 of this excellent series of articles on becoming a data scientist, written by someone who spends their day recruiting data scientists. This installation focuses on learning.
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Artificial intelligence and machine learning are being adopted at a rapid clip, and the management headaches are just about to begin.
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Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.
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I would like to live in a world whose systems are build on rigorous, reliable, verifiable knowledge, and not on alchemy. Simple experiments and simple theorems are the building blocks that help understand complicated larger phenomena.
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Sometimes you open a big Dataset with Python's Pandas, try to get a few metrics, and the whole thing just freezes horribly.
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Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.
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Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.
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Getting the most from our model, figuring out what it all means, and experimenting with new techniques
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I wrote my first line of R code in 2010 for a class at the University of Washington (UW). I was hooked once I realized how much more powerful coding is than spreadsheets. Over the past decade, I witnessed the term 'data science' come into widespread use and saw the rise and fall of buzzwords like big data, business intelligence, analytics, and now artificial intelligence.
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Gain some perspective on the Netflix interview process, and on ways to prepare for just such an industry interview.
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With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
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In data science, computer science and statistics converge. As data scientists, we use statistical principles to write code such that we can effectively explore the problem at hand.
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Solving a complete machine learning problem for the societal benefit
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Much of the AI capability working its way into BI tools today isn't entirely new consumer technologies have been successfully implementing them for years. In fact, this is one of the strengths of artificial intelligence when applied to business intelligence the fact that users already understand intuitively how to use products like Google and Amazon making it easier for them to adopt those same technologies and interaction paradigms in BI tools
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Developments in computer vision, speech to text and natural language processing algorithms are changing the face of media creation, distribution and most importantly, media consumption.
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Image recognition is not something new for the law enforcements, but there is for sure room for improvement. Government and law enforcement has turned to AI for the help with facial recognition.
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Companies moving towards Internet of Things (IoT) for their expansion and growth as well as increasing the scope and complexity of industrial control networks that frequently with traditional IT networks. Network operators are discovering a host of associated direct and indirect costs that can often exceed the initial investment in networking hardware.
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