{"id":1534,"date":"2018-11-22T15:01:04","date_gmt":"2018-11-22T13:01:04","guid":{"rendered":"https:\/\/clients.triloogia.ee\/proekspert\/wp-new\/?p=1534"},"modified":"2023-08-11T09:39:00","modified_gmt":"2023-08-11T07:39:00","slug":"python-applied-to-machine-learning-and-data","status":"publish","type":"post","link":"https:\/\/clients.triloogia.ee\/proekspert\/wp-new\/blog\/bi-data-analytics\/python-applied-to-machine-learning-and-data\/","title":{"rendered":"Python Applied to Machine Learning and Data"},"content":{"rendered":"

An overview by Travis E. Oliphant.<\/em><\/p>\n

Most tech companies today are struggling to figure out how they can best work with their data. Travis E. Oliphant, the founder of startups Quansight<\/a> and Anaconda<\/a>, and the primary developer of NumPy and SciPy packages for Python, gave a talk at North Star AI conference, powered by Proekspert<\/a> on how Python can be used effectively for machine learning and data \u2013 the heart of ML-driven technology.<\/p>\n

Oliphant\u2019s connection to Python dates as far back as 1997, when he was working with version 1.4. The very first problem he focused on was actually a data problem, and this led him to understand that in order to do anything with machine learning you have to get the data right.<\/p>\n

While data seems to be everywhere these days, said Oliphant, the biggest problem is how you gain access to and utilize that information.<\/p>\n

Applying Python to machine learning problems<\/h3>\n

In Oliphant\u2019s mind, Python is one of the best languages to apply to machine learning problems. During his talk, he gave a thorough overview as to why and presented how he imagines artificial intelligence might develop in the future.<\/p>\n

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Not Artificial but Augmented Intelligence<\/h2>\n

When Oliphant thinks of AI, he thinks of \u201caugmented\u201d rather than \u201cartificial intelligence.\u201d For at least the next fifty years this technology will be more about \u201cempowering people rather than replacing them,\u201d he said. To be sure, AI might take over some tasks that you are doing today, but then you will shift to doing something more important, while the machine takes care of more mundane assignments.<\/p>\n

What can AI be used for? Oliphant noted a multitude of possibilities.<\/p>\n

Any time you have a complex function with many variables for which you want to have an understandable input and output\u2014you can apply AI. For example, self-driving cars, medicine, and geophysics are a few of the fields where AI technology can make a big difference.<\/p>\n

Big companies using Python in machine learning<\/h4>\n

In order to apply ML and be successful with your applications, Oliphant noted, you will need to work with people who have domain expertise, who know the business. The good news is that every major tech company is now involved in the AI field. Microsoft, Google, Apple, IBM, and Amazon are pioneering machine learning and artificial intelligence research.<\/p>\n