Great Places to Start with Data Science and Machine Learning

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July 28, 2020

(2 min read)

Often times, people find the idea of Artificial Intelligence to be quite intimidating, at the level of strange computer sorcery. I can't do AI, that's sooo complicated! you might be saying. In reality, Machine Learning is an increasingly accessible, promising field that can be used in conjunction with almost any other discipline, from business to the sciences or the language arts.

In my opinion, Python is the best language to learn AI, thanks to its expansive and efficient libraries designed for Data and ML, specifically numpy, pandas, sci-kit learn, etc. Kaggle, a site for data science competitions, offers several courses through Kaggle Learn. These are free, intuitive, and really fast paced. After learning Python, you can start applying simple machine learning models in just hours. (My personal favorite, I love to learn by doing.)

https://www.kaggle.com/learn/overview

If you are more interested in the theory behind AI and Deep Learning, consider taking the gold standard of AI online courses: Andrew Ng's Machine Learning Course with Coursera

https://www.coursera.org/learn/machine-learning/home/welcome

Harvard also offers an AI course with the site edX, which can be found here:

https://courses.edx.org/courses/course-v1:HarvardX+CS50AI+1T2020/course/

All of these are completely free, and great resources for the budding machine learning student. For university level instruction, UC Berkeley's MIDS (the information school) also offers courses in Data Science and ML, some of which are open to high schoolers.

Happy Learning!



Enoch Luk

@enochcluk

​Machine Learning/Data Science/ Computer Science enthusiast and project creator. SciBowl A-Teamer and SciOly captain who avidly pursues science from meteorology to chemistry to physics. Member of the US Earth Science Organization's national camp. Also a passionate volunteer and active member of the community. Interested in the great potential of fields like data and AI to do good, such as eliminating bias and misinterpretation in data collection and computer vision or combating climate change with smart systems. ​

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