Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
Comprehensive Guide to Learning Python for Data Science
Latest:
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Beginner Data Visualization & Exploration Using Pandas - Oct 22, 2018.
This tutorial will offer a beginner guide into how to get around with Pandas for data wrangling and visualization. -
The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes. -
Apache Spark Introduction for Beginners - Oct 18, 2018.
An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more. -
The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches. -
Adversarial Examples, Explained - Oct 16, 2018.
Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities. -
Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3 - Oct 16, 2018.
In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling. -
GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools. -
5 “Clean Code” Tips That Will Dramatically Improve Your Productivity - Oct 15, 2018.
TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs -
Machine learning — Is the emperor wearing clothes? - Oct 12, 2018.
We take a look at the core concepts of Machine Learning, including the data, algorithm and optimization needed to get you started, with links to additional resources to help enhance your knowledge. -
We Sized Washington’s Edible Marijuana Market Using AI - Oct 12, 2018.
As Canada legalizes marijuana, it might look to Washington to understand how pot sells. With the RAND Corp., we used machine learning to estimate how much THC- in pot is sold in Washington. -
Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors. -
10 Best Mobile Apps for Data Scientist / Data Analysts - Oct 10, 2018.
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more. -
Preprocessing for Deep Learning: From covariance matrix to image whitening - Oct 10, 2018.
The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions! -
Building an Image Classifier Running on Raspberry Pi - Oct 9, 2018.
The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images. -
A Concise Explanation of Learning Algorithms with the Mitchell Paradigm - Oct 5, 2018.
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms. -
Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more. -
Society of Machines: The Complex Interaction of Agents - Oct 4, 2018.
In this new series we’ll focus on collective interaction of two or more machines. This interaction of machines can be among each other or with the environment. -
Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors. -
Sequence Modeling with Neural Networks – Part I - Oct 3, 2018.
In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework. -
Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more. -
More Effective Transfer Learning for NLP - Oct 1, 2018.
Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.
September:
- Basic Image Data Analysis Using Python – Part 3
- Introduction to Deep Learning
- Visualising Geospatial data with Python using Folium
- Raspberry Pi IoT Projects for Fun and Profit
- Introducing Path Analysis Using R
- Power Laws in Deep Learning 2: Universality
- Introducing VisualData: A Search Engine for Computer Vision Datasets
- The Whys and Hows of Web Scraping – A Lethal Weapon in Your Data Arsenal
- Unfolding Naive Bayes From Scratch
- “Auto-What?” – A Taxonomy of Automated Machine Learning
- Deep Learning Framework Power Scores 2018
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6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study
Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process. - Power Laws in Deep Learning
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection
- SQL Case Study: Helping a Startup CEO Manage His Data
- Everything You Need to Know About AutoML and Neural Architecture Search
- Iterative Initial Centroid Search via Sampling for k-Means Clustering
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Machine Learning Cheat Sheets
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus. - Machine Learning for Text Classification Using SpaCy in Python
- Object Detection and Image Classification with YOLO
- Training with Keras-MXNet on Amazon SageMaker
- 5 Things to Know About A/B Testing
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Ultimate Guide to Getting Started with TensorFlow
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
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Essential Math for Data Science: ‘Why’ and ‘How’
It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.
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Data Science Cheat Sheet
Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.
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Deep Learning for NLP: An Overview of Recent Trends
A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.
- Financial Data Analysis – Data Processing 1: Loan Eligibility Prediction
- OLAP queries in SQL: A Refresher
- An End-to-End Project on Time Series Analysis and Forecasting with Python