I'm sure you've heard of the incredible artificial intelligence applications out there -- from programs that can beat the world's best Go players to self-driving cars. The problem is that most people get caught up on the AI hype, mixing technical discussions with philosophical ones. If you're looking to cut through the AI hype and work with practically implemented data models, train towards a data engineer or machine learning engineer position. Look for them in data engineering or machine learning tutorials. These are the steps I took to build this fun little scraper I built to analyze gender diversity in different coding bootcamps.
With modern technology, such questions are no longer bound to creative conjecture. You have just found Keras. Today i will give a brief introduction over this topic which created headache for me when i was learning this. All video and text tutorials are free. I use Anaconda package that almost wraps up all the Python packages including Jupyter notebook.
The best trained soldiers can't fulfill their mission empty-handed. Data scientists have their own weapons -- machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that's why we interviewed data science practitioners -- gurus, really --regarding the useful tools they choose for their projects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics).
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Many developers (including myself) have included learning machine learning in their new year resolutions for 2018. Even after blocking an hour everyday in the calendar, I am hardly able to make progress. The key reason for this is the confusion on where to start and how to get started. It is overwhelming for an average developer to get started with machine learning.