In another article, we discussed how to simplify your Python code for optimal readability. That piece covered why writing simpler code will help with the reader’s comprehension of your syntax and force you to think about problems in a way that is easier to explain to those who may not be as technically savvy. This article will…

In a previous article, you learned how to format your Python code for optimal readability. That piece went into detail about spacing, number of characters on a line, and more. In this article, you will learn how to keep your Python code explicit to ensure others fully understand your work and eliminate any possible confusion. This good practice will…

Naming Conventions 

During a recent workshop, bootcamp instructor Alex Baransky shared some of the best practices in writing Python code for better readability. Through this series of articles, we will summarize and give you a few examples of those best practices to help you write more elegant Python code, benefiting those who may read and use your…

The demand and the supply gap for a data scientist are ever-increasing. In fact, in one of its surveys, IBM predicts increment in data science jobs to be 364,000 to 2720,000 in 2020 which is only going upwards in the subsequent years. Python, as a programming language, is immensely popular for building data science-based applications owing to its simplicity, and large community support and ease of deployment.

Our Data Science with Python online course has been…

Since its advent in 1991, Python has been the choice of coders due to its simple features that include ease of understanding and readability of code but not limited to just that. There have been lots of changes in the codebase over time due to the contribution from developers worldwide. Simple syntax and faster execution make Python a favorable language among programmers. It is used widely to create websites and primarily in big data operations. To make the task of running…

25 Experts have compiled this list of Best Python for Machine Learning Course, Tutorial, Training, Class, and Certification available online for 2020. It includes both paid and free resources to help you learn Python for Machine Learning and these courses are suitable for beginners, intermediate learners as well as experts.

1.Machine Learning with Python (Coursera) 

If you are interested in getting started with the field…

Introduction

Are you looking to learn python for data science but have a time crunch? Are you making your career shift into data science and want to learn python? In this blog, we will talk about learning python for data science in just 30 days. Also, we will look at weekly schedules and topics to cover in python.

Before directly jumping to python, let us understand about the usage of python in data…

There is a library called threading in Python and it uses threads (rather than just processes) to implement parallelism. This may be surprising news if you know about the Python's Global Interpreter Lock, or GIL, but it actually works well for certain instances without violating the GIL. And this is all done without any overhead -- simply define…

Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required under the GNU public license.

But which one is better? And, more importantly, which one should you learn?

Both are widely used and are standard tools in the hands of every data scientist.

The answer may surprise you – because as a professional data scientist, you should be ready to deal with…

There has been much hype surrounding deep learning and data science learning in recent times, and one of the cornerstones of deep learning is the neural network. In this article, we will look at what a neural network is and get familiar with the relevant terminologies.

In simplest terms, a neural network is an interconnection of neurons. Now the question arises, what is a neuron? To understand neurons in deep learning, we first…

The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published (Packt).

From the back cover:

Machine learning has gained tremendous popularity for…

Artificial Intelligence with Python

By Prateek joshi

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you

What you will…

Pandas dataframe is making life a lot easier if you are working with data. A lot of data comes in CSV format. It's possible to read CSV files to a Pandas dataframe. In fact, it's quite easy using read_csv. In the video below we will learn the basics of just loading a CSV file to a Pandas dataframe object. …

Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …

The book "Mastering Machine Learning Algorithms" has been published by Packt 

From the back cover:

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more…

R Deep Learning Essentials

By Joshua F. Wiley

Get everything you need to know to enter the world of deep learning when it comes to R with this book. Get started from the packages you need to have for your side,…

I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In…

Now that Christmas and the New Year are behind us the nights are becoming a little longer with each passing day. Nevertheless, there's still loads of cold winter nights left to endure (unless you're in the Southern Hemisphere, in which case – throw me a shrimp on the barbie!).

It's time to dust off your New Year resolutions from last year (remember those?) and get ready for a new start, a new you and learn some new data skills.

I’ve thrown together a collection of five…

In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.

It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods.  If you have been following the latest technical developments you probably know that CNN’s are…