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…

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…

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…

Michael Li is founder and CEO at The Data Incubator. The company offers curriculum based on feedback from corporate and government partners about the technologies they are using and learning, for masters and PhDs. 

The Rankings

Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The…

Technology has remarkably changed the way we live today, there is no denial to it. Compared with our ancestors, we stand far away from them in using different technologies for our day-to-day works.

So many technologies are developed in the past couple of years that have revolutionized our lives, and it’s impossible to list each of them. Though technology changes fast with time, we can observe the trends in which it changes. Last year, 2016 had bought so many fresh…

The numpy, scipy, and statsmodels libraries are frequently used when it comes to generating regression output. While these libraries are frequently used in regression analysis, it is often the case that a user might choose different libraries depending on the data in question, among other considerations. Here, we will go through how to use each of the above to generate regression output.

Linear Regression using numpy and…

Text Analytics with Python -- A Practical Real-World Approach to Gaining Actionable Insights from your Data

Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" published by Apress\Springer, is a book packed with 385 pages of useful information based on techniques, algorithms,…

The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm.

The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective.

This post explains how to:

1. Import kmeans and PCA through the sklearn…

For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. It is ideal for beginners because it has a really simple interface, it is well documented with many examples and tutorials.

Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, feature extraction and engineering,  and pre-processing the data. The interface is consistent over all of these methods, so…