Master the math needed to excel in data science and machine learning. If you're a data scientist who lacks a math or scientific background or a developer who wants to add data domains to your skillset, this is your book. Author Hadrien Jean provides you with a foundation in math for data science, machine learning, and deep learning. Through the course of this book, you'll learn how to use mathematical notation to understand new developments in the field, communicate with your peers, and solve problems in mathematical form. You'll also understand what's under the hood of the algorithms you're using. Learn how to: Use Python and Jupyter notebooks to plot data, represent equations, and visualize space transformations Read and write math notation to communicate ideas in data science and machine learning Perform descriptive statistics and preliminary observation on a dataset Manipulate vectors, matrices, and tensors to use machine learning and deep learning libraries such as TensorFlow or Keras Explore reasons behind a broken model and be prepared to tune and fix it Choose the right tool or algorithm for the right data problem

Author Biography

Hadrien Jean is a machine learning scientist at Ava Accessibility in the domain of speech transcription. He completed his Ph.D. in cognitive science at the Ecole Normale Superieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https: //hadrienj.github.io/posts/).