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misho-kr / IBM Cloud -- Deploying Microservices with
Last active Oct 11, 2019
Summary of "IBM Cloud: Deploying Microservices with Kubernetes" course on Coursera.Org
View IBM Cloud -- Deploying Microservices with

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

misho-kr / Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep
Last active Aug 26, 2019
Summary of "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning" course on Coursera.Org
View Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep

Introduction to TensorFlow for AI, ML, and DL

The "Machine Learning" course and "Deep Learning" Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems.

Taught by

Laurence Moroney, AI Advocate, Google Brain

Week 1: A New Programming Paradigm

misho-kr / Introduction to Linear Modeling in
Last active Feb 20, 2019
Introduction to Linear Modeling in Python
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Introduction to Linear Modeling in Python


Jason Vestuto, Data Scientist, University of Texas at Austin

Exploring Linear Trends

We start the course with an initial exploration of linear relationships, including some motivating examples of how linear models are used, and demonstrations of data visualization methods from matplotlib. We then use descriptive statistics to quantify the shape of our data and use correlation to quantify the strength of linear relationships between two variables.

misho-kr /
Last active Nov 19, 2018
Summary of "Conda for Building & Distributing Packages" course at DataCamp.Com

Conda for Building & Distributing Packages


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.

misho-kr /
Last active Jul 22, 2019
Summary of "Conda Essentials" course at DataCamp.Com

Conda Essentials


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.

misho-kr /
Last active Jun 28, 2019
Fedora 28 Installation
misho-kr /
Last active Jul 14, 2018
Summary of "Spreadsheet Basics" and ""Data Analysis with Spreadsheets" courses at DataCamp.Com

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.


Vincent Vankrunkelsven, Spreadsheet instructor at DataCamp

1.1 Getting started

misho-kr / Intro to Financial Concepts in
Last active Mar 27, 2018
Introduction to Financial Concepts in Python
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Introduction to Financial Concepts in Python

Course Objectives

The Time Value of Money
Compound Interest
Discounting and Projecting Cash Flows Making Rational Economic Decisions Mortgage Structures
Interest and Equity
The Cost of Capital
misho-kr / Neural Networks for Machine
Last active Jan 8, 2018
Summary of "Neural Networks for Machine Learning" course at Coursera.Org
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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

misho-kr / Data
Last active Apr 4, 2018
Summary of "Data Structures" course at Coursera.Org
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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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