December 19, 2019

LINK TO THE VIDEO: https://www.youtube.com/watch?v=5Iq7tcrTnWA

In this video, I describe the different levels of probability, and which is the most important for machine learning.

Unlike calculus, where high school-level calculus is not all that different from college-level calculus, the differences between high school / college / graduate probability are vast.

For machine learning, the most appropriate level of probability is college-level.

Watch the video to learn more about why:

December 10, 2019

Welcome to another episode of Data Science Interview Questions! In this episode, I discuss the Random Walk Hypothesis and Stock Price Prediction.

Why is stock price data often considered to be a random walk?

If your data is best modeled as a random walk, how can you do a time series forecast into the future?

How can you draw a confidence interval around the forecast?

What does this mean for stock price predictions?

Find out here:

What you will learn:

• How to make the best forecast possible if your data is from a random walk model
• How to find the confidence bounds for your forecast (also called confidence limits or prediction intervals)
• Why pretty much all the “data science” instructors out there are really just marketers who have been selling you lies for years
• Hint: No, LSTMs will not help you predict stock prices and in fact perform worse than the simple model described above