Solve design, planning, and control problems using modern machine learning and AI techniques.
Read on to find out more.
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.
With the Statistics Playbook by Gary Sutton
This book will teach you how to use R in a different way than any other book out there. You will approach R concepts using publicly-available NBA statistical data rather than prepared datasets, and learn how to combine various methods and techniques.
Read on to learn more.
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.