Julia for Data Science – Learn Julia’s Programming Language for Data Science
Julia is an easy, fast, open source language that works well, as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing a high performance performance without the sacrifice of human convenience. Julia is a fresh approach to technical computing, combining expertise from various fields of computational and computer science.
This video course will walk you through all the steps involved in applying the Julia ecosystem to your own data science projects. We start with the basics and show you how to design and implement some of the general purpose features of Julia. Is fast development and fast execution possible at the same time? Julia provides the best of both worlds with its wide range of types, and our course covers this in depth. You will have a structured and readable code by the end of the course by learning how to write Lisp style macros and modules.
The course demonstrates the power of the DataFrames package to manage, organize, and analyze data. It allows you to work with data from various sources, perform statistical calculations on them, and visualize their relationships in different types of plots through live demonstrations.
Julia for Data Science takes you from zero to hero, leaving you with the know-how required to apply
Table of Contents:
1: The Groundwork – Julia’s Environment
2: Data Munging
3: Data Exploration
4: Deep Dive into Inferential Statistics
5: Making Sense of Data Using Visualization
6: Supervised Machine Learning
7: Unsupervised Machine Learning
8: Creating Ensemble Models
9 : Time Series
10: Collaborative Filtering and Recommendation System
11: Introduction to Deep Learning
Producer: Packt Publishing
Language of instruction: English
Teacher: Anshul Joshi
Level of training: Elementary, Secondary, Advanced
Training time: 2 hours + 41 minutes
File size: 474 MB