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Latest:
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Interpreting a data set, beginning to end - Aug 20, 2018.
An intimate knowledge of a large data set is important to avoiding a “garbage in, garbage out” situation. The author offers an overview of techniques that lead to a better understanding of the data. -
Why Automated Feature Engineering Will Change the Way You Do Machine Learning - Aug 20, 2018.
Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage. -
Introduction to Fraud Detection Systems - Aug 17, 2018.
Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started. -
Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code - Aug 17, 2018.
Auto-Keras is an open source software library for automated machine learning. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. -
Named Entity Recognition: A Practitioner’s Guide to NLP - Aug 17, 2018.
Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes. -
Reinforcement Learning: The Business Use Case, Part 2 - Aug 16, 2018.
In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance. -
A Crash Course in MXNet Tensor Basics & Simple Automatic Differentiation - Aug 16, 2018.
This is an overview of some basic functionality of the MXNet ndarray package for creating tensor-like objects, and using the autograd package for performing automatic differentiation. -
An Introduction to t-SNE with Python Example - Aug 15, 2018.
In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data. -
AutoKeras: The Killer of Google’s AutoML - Aug 15, 2018.
Auto-Keras is an open source "competitor" to Google’s AutoML, a new cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in Neural Architecture Search (NAS). -
How to Set Up a Free Data Science Environment on Google Cloud - Aug 15, 2018.
In this post, we'll walk through how to set up a data science environment on Google Cloud Platform (GCP). Because of the economy of scale that cloud hosting companies provide, individuals or teams can affordably access powerful computers. -
Unveiling Mathematics Behind XGBoost - Aug 14, 2018.
Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model. -
Setting up your AI Dev Environment in 5 Minutes - Aug 13, 2018.
Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes. -
Unsupervised Learning Demystified - Aug 13, 2018.
Unsupervised learning is a pattern-finding technique for mining inspiration from your data. Let's demystify! -
Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP - Aug 10, 2018.
Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization. -
Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice. -
Reinforcement Learning: The Business Use Case, Part 1 - Aug 9, 2018.
At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward. -
Optimization 101 for Data Scientists - Aug 8, 2018.
We show how to use optimization strategies to make the best possible decision. -
GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows - Aug 8, 2018.
This post includes a wide spectrum of data science projects, all of which are open source and are present on GitHub repositories. -
Programming Best Practices For Data Science - Aug 7, 2018.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset. -
Autoregressive Models in TensorFlow - Aug 6, 2018.
This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations. -
Only Numpy: Implementing GANs and Adam Optimizer using Numpy - Aug 6, 2018.
This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved. -
K-Means in Real Life: Clustering Workout Sessions - Aug 3, 2018.
By using the within-cluster sum of squares as cost function, data points in the same cluster will be similar to each other, whereas data points in different clusters will have a lower level of similarity. -
Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP - Aug 3, 2018.
I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects. -
WTF is TF-IDF? - Aug 2, 2018.
Relevant words are not necessarily the most frequent words since stopwords like “the”, “of” or “a” tend to occur very often in many documents. -
From Data to Viz: how to select the the right chart for your data - Aug 1, 2018.
We offer an interactive, decision tree-style tool, which examines the data you have and proposes a set of potentially appropriate visualizations to represent your dataset. -
Basic Statistics in Python: Descriptive Statistics - Aug 1, 2018.
This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python. -
Selecting the Best Machine Learning Algorithm for Your Regression Problem - Aug 1, 2018.
This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!
July:
- Google’s AutoML: Cutting Through the Hype
- Intuitive Ensemble Learning Guide with Gradient Boosting
- DevOps for Data Scientists: Taming the Unicorn
- Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks
- Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP
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How to Build a Data Science Portfolio
This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed. -
Genetic Algorithm Implementation in Python
This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.
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Cookiecutter Data Science: How to Organize Your Data Science Project
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. -
Comparison of Top 6 Python NLP Libraries
Today, we want to outline and compare the most popular and helpful natural language processing libraries, based on our experience.
- Receiver Operating Characteristic Curves Demystified (in Python)
- The ultimate list of Web Scraping tools and software
- Best (and Free!!) Resources to Understand Nuts and Bolts of Deep Learning
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Explaining the 68-95-99.7 rule for a Normal Distribution
This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.
- Efficient Graph-based Word Sense Induction
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5 Quick and Easy Data Visualizations in Python with Code
This post provides an overview of a small number of widely used data visualizations, and includes code in the form of functions to implement each in Python using Matplotlib.
- Clustering Using K-means Algorithm
- Basic Image Data Analysis Using Numpy and OpenCV, Part 2
- BigQuery vs Redshift: Pricing Strategy
- fast.ai Deep Learning Part 2 Complete Course Notes
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Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”
By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.
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Text Mining on the Command Line
In this tutorial, I use raw bash commands and regex to process raw and messy JSON file and raw HTML page. The tutorial helps us understand the text processing mechanism under the hood.
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Dimensionality Reduction : Does PCA really improve classification outcome?
In this post, I am going to verify this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
- Basic Image Data Analysis Using Numpy and OpenCV – Part 1
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Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV
For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips
- fast.ai Deep Learning Part 1 Complete Course Notes
- The 4 Levels of Data Usage in Data Science
- Introduction to Apache Spark
- fast.ai Machine Learning Course Notes
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5 of Our Favorite Free Visualization Tools
5 key free data visualization tools that can provide flexible and effective data presentation.
- Manage your Machine Learning Lifecycle with MLflow – Part 1
- Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors
- Deep Learning Tips and Tricks
- Overview and benchmark of traditional and deep learning models in text classification
- Deep Quantile Regression
- Data Retrieval and Cleaning: Tracking Migratory Patterns
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Automated Machine Learning vs Automated Data Science
Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.