a greater extent than any other mathematical discipline, statistics is a product of time. If previous-centuries scientists had had access to actual computational power, or even to computers, their studies and the current field as a whole would paint an entirely different picture.

Although not frequently told, statistics are the foundation of today’s known Machine Learning. Nowadays it’s hard to find a use case in which Machine Learning is not being applied in some form and statistics are sort of left aside. That trend will probably keep on increasing as more people become aware of the benefits of ML applications and have access to its simplicity of deployment.** If you’re fond of learning more of this subject, or you’re simply curious about how it works, this article might be suitable for you.**

---

Table of contents:

1. What is Machine Learning? Why should I use it? (2 min read)
2. Popular Misconceptions (1 min read)
3. How is Machine Learning different from Econometrics? (1 min read)
4. Must-read Machine Learning books (1 min read)
5. Machine Learning in Finance (1 min read)
6. Programming Support Vector Machine and Linear Regression models with Python (4 min read)

#machine-learning #python #data-science #ai