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Python Programming Fundamentals: A Beginner's Guide [Updated 2020]

#artificialintelligence

Python is one of the powerful, high-level, easy to learn programming language that provides a huge number of applications. Some of its features, such as being object-oriented and open source, having numerous IDE's, etc. make it one of the most in-demand programming languages of the present IT industry. According to TIOBE index, as of January 2020, Python is one of the popular programming languages. By looking at the popularity of this programming language, many IT professionals, both beginners as well as experienced alike, are willing to build their career as a Python developer. With an aim to help similar concerns, Simpliv is presenting this blog to discuss about the various fundamental concepts of Python programming and take you along to start writing Python programs on your own. Before proceeding further, at this point, we would like to suggest that you read blog (first blog in this series) on introduction to Python programming language. Let us start by learning the steps to install Python.


Course 'Introduction to Scala'

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Jupyter is a novel way to combine documentation with live code, which might run on powerful distributed systems like Apache Spark, Flink and Scalding. The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. This open source project supports interactive data science and scientific computing with over 40 programming languages. Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. Although Jupyter has its roots in big data, Jupyter is generally useful for all computing needs. This lecture starts with instructions for installing Jupyter on Mac and Ubuntu. This lecture then demonstrates how to install and work with the Jupyter-Scala kernel, so students can use Scala with the Jupyter Notebook as well as the more traditional console REPL. The information provided is also largely applicable to JupyterHub, multi-user server for Jupyter notebooks.


Tutorial: Getting Started with R and RStudio

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If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post. RStudio is an open-source tool for programming in R. RStudio is a flexible tool that helps you create readable analyses, and keeps your code, images, comments, and plots together in one place. Using RStudio for data analysis and programming in R provides many advantages. RStudio can also be used to program in other languages including SQL, Python, and Bash, to name a few. But before we can install RStudio, we'll need to have a recent version of R installed on our computer.


Exploring AWS IoT Udemy

@machinelearnbot

This course explores the basic features of device to web, and device to cloud communication using your Amazons AWS IoT free tier account. In this course we will program inexpensive, WiFi enabled embedded devices, like the ESP8266, ESP32, TI 3200, and Raspberry Pi 3. We will take advantage of free "Internet of Things" (IoT) prototyping platforms, like Mongoose OS in JavaScript, and Zernyth in Python to program our inexpensive devices. The course continues on with programming our embedded devices to send data from the device to the AWS cloud. To transmit our data we will use the built in MQTT broker on our devices firmware, sending JSON encoded sensor data, to the AWS IoT console. From within the IoT console we will explore many of the built in AWS IoT enabled services that are integrated from the IoT console on the AWS cloud.


Top 8 IDEs for Machine Learning and Data ScienceYou Should Know

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If you are a data scientist or a machine learning engineer, then you should be familiar with the most efficient machine learning IDEs. An IDE serves for the next purposes as code validation, a text editor, syntax highlighting, completion, contextual suggestions, debugging tool, method and class specification, resource management, and easy access to help. IDEs generally have extensive collections of features. Because of this, they are beneficial for data science programming too. They make the lives of programmers considerably easier.