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… Continue
Added by Divya Singh on January 11, 2019 at 8:30am —
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… Continue
Added by Michael Li on November 29, 2018 at 6:30am —
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… Continue
Added by Divya Singh on October 2, 2018 at 6:15am —
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… Continue
Added by Divya Singh on September 20, 2018 at 4:00am —
The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published (Packt).
From the back… Continue
Added by Giuseppe Bonaccorso on September 2, 2018 at 7:18am —
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… Continue
Added by Packt Publishing on August 26, 2018 at 9:01pm —
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. …
Added by Erik Marsja on July 17, 2018 at 6:01am —
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 … Continue
Added by Ahmet Taspinar on July 5, 2018 at 11:48am —
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… Continue
Added by Giuseppe Bonaccorso on June 15, 2018 at 6:30am —
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,… Continue
Added by Packt Publishing on May 15, 2018 at 10:00pm —
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… Continue
Added by hadrienj on March 28, 2018 at 1:30pm —
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… Continue
Added by Ahmet Taspinar on December 4, 2017 at 5:00am —
In this post, we learn about building a basic search engine or document retrieval system using Vector space model. This use case is widely used in information retrieval systems. Given a set of documents and search term(s)/query we need to retrieve relevant documents that are similar to the search query.
The problem statement explained above is represented as in below… Continue
Added by dataperspective on November 15, 2017 at 1:30am —
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.
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… Continue
Added by Michael Li on October 17, 2017 at 12:00pm —
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… Continue
Added by Venkatesan M on October 6, 2017 at 9:00pm —
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… Continue
Added by Michael Grogan on August 26, 2017 at 6:30am —
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,… Continue
Added by Dipanjan Sarkar on July 14, 2017 at 4:00am —
Graphs belong to the field of mathematics, graph theory. For data analysis that requires searches of particular patterns, graph-based data mining becomes an important technique. Indeed, in real life, most of the data we have to deal with can be represented as graphs. A typical graph consists of vertices (nodes, cells), and of edges that…
Added by jwork.ORG on June 19, 2017 at 5:30pm —
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:
- Import kmeans and PCA through the sklearn…
Added by Michael Grogan on June 17, 2017 at 8:00am —
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. Continue
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…
Added by Ahmet Taspinar on May 26, 2017 at 4:30am —