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Neural Networks for Machine Learning

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.

Taught by:

Geoffrey Hinton, Professor, Department of Computer Science, University of Toronto

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Data Structures

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures.

A few examples of questions that we are going to cover in this class are the following:

What is a good strategy of resizing a dynamic array?
How priority queues are implemented in C++, Java, and Python?

How to implement a hash table so that the amortized running time of

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Fedora 20 Installation Notes

This page describes the steps to prepare for and perform the installation of Fedora Linux on my laptop and desktop machines.

The process is fairly straightforward, and thanks to the efforts of the Fedora team really easy to carry on. Still, there are steps (mostly customizations) that require looking up man pages and other documentation, and there is benefit to record that information somewhere. I also tend to forget what custom scripts I have created and used, so unless I remember to preserve them I have to write them again (and again). This notes should serve as reminders to copy over such scripts.

Download and other links

• Get Fedora and choose convenient format, usually Gnome Desktop DVD 64 bit

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Spreadsheet Basics

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Lecturers

Vincent Vankrunkelsven, Spreadsheet instructor at DataCamp

1.1 Getting started

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Conda for Building & Distributing Packages

Summary

In the Conda Essentials course you learned how use the Conda package manager to create and share reproducible environments for data science development.

Anaconda Projects

In this chapter you'll create an Anaconda Project, which is a data science asset that specifies package installs, file downloads, and executable commands. Anaconda projects can be used to run Jupyter notebooks, Bokeh server apps, REST APIs, and command line tools on Windows, Mac OSX, and Linux platforms making deployment easy.

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Fedora 28 Installation Notes

Steps to install and set up Fedora Workstation on my desktop machine.

Download and other links

• Notes from the previous Fedora 20 Installation

Preparations

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Conda Essentials

Summary

Conda packages are files containing a bundle of resources: usually libraries and executables, but not always. In principle, Conda packages can include data, images, notebooks, or other assets.

One of the powerful aspects of conda, both the tool and the package format, is that dependencies are taken care of. That is, when you install any Conda package, any other packages needed get installed automatically.

A Conda package, then, is a file containing all files needed to make a given program execute correctly on a given system.

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IBM Cloud: Deploying Microservices with Kubernetes

In this course, you learn how to deploy and manage Microservices applications with Kubernetes. You also learn about securing and managing a Kubernetes cluster, and how to plan your Kubernetes cluster for deployment to IBM Cloud.

Taught by

Megan Irvine, Technical Enablement Specialist

Week 1: Introduction to Kubernetes

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Introduction to Python

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Python Basics

An introduction to the basic concepts of Python. Learn how to use Python interactively and by using a script. Create your first variables and acquaint yourself with Python's basic data types.

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Intermediate Python for Data Science

Intermediate Python for Data Science is crucial for any aspiring data science practitioner learning Python. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Lead by Filip Schouwenaars, Data Science Instructor at DataCamp

Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.