Build a Robo Advisor with Python (From Scratch): Automate your financial and investment decisions teaches you how to construct a Python-based financial advisor of your very own!
This article delves into how Machine Learning algorithms interact with data and the importance of preserving data privacy.
Read it if you’re a machine learning engineer, or a developer building around machine learning.
If you want to excel in ML and deep learning, you need to know more than how to implement the algorithms—you need to know them inside-out. This book delves into selected algorithms and teaches you how to build your own from scratch.
This article introduces the concept and definition of differential privacy.
Read it if you’re a machine learning engineer, or a developer building around machine learning.
An excerpt from Causal Inference for Data Science by Aleix Ruiz de Villa
This article explains:
· Why and when we need causal inference
· How causal inference works
And how the book approaches the topic.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
This article dives into working with data in dataframes with Julia.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
· Storing values on keys in dictionaries.
· Working with pair objects.
· Using tuples to create dictionaries.
· Comparing dictionaries and arrays.
Read it if you’re interested in the Julia language or in how it handles dictionaries.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
Many languages designed for doing data science provide ways to perform vectorized operations, which is also often called broadcasting. In Julia, broadcasting is also supported. In this article you will see how to use it.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.
From Causal Inference in Data Science by Aleix Ruiz de Villa
Causal inference models predict why something will happen, i.e. causal effects, rather than the outcomes themselves. This is useful in many instances and is a budding field in machine learning and data science.
Read on to see how it works and what you will learn from this book.
From Pandas Workout by Reuven Lerner
This article discusses using multi indexes in Pandas.