Your logical, linear guide to the fundamentals of data science programming
Data science is exploding―in a good way―with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.
Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time.
Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life―and everyone else’s!
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John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.
Your complete guide to data science programming
This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear and logistic regression, ensembles of learners, deep neural networks, recommenders, optimization and validation of models. However, it isn't all about using math to manipulate the data. You use the math to perform the very same machine learning and deep learning tasks that make doctors more efficient, reduce traffic accidents and solve business problems, as well as many other problems of daily life. Knowing the math also enables you to do things like analyze image and audio data, create graphics that allow others to understand the data, and even possibly become an artist.
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