Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this Intro to R for Data Science Tutorial. 
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Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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On the sensationalism of artificial intelligence news - Nov 15, 2019.
With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening. -
Tips for a cost-effective machine learning project - Nov 15, 2019.
Spoiler: you don’t need a VM running 24/7 to handle 16 requests a day. -
Python Lists and List Manipulation - Nov 15, 2019.
In Python, lists store an ordered collection of items which can be of different types. This post is an overview of lists and their manipulation. -
How to Visualize Data in Python (and R) - Nov 14, 2019.
Producing accessible data visualizations is a key data science skill. The following guidelines will help you create the best representations of your data using R and Python's Pandas library. -
Topics Extraction and Classification of Online Chats - Nov 14, 2019.
This article provides covers how to automatically identify the topics within a corpus of textual data by using unsupervised topic modelling, and then apply a supervised classification algorithm to assign topic labels to each textual document by using the result of the previous step as target labels. -
Testing Your Machine Learning Pipelines - Nov 14, 2019.
Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines. -
Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop. -
Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn. -
Understanding NLP and Topic Modeling Part 1 - Nov 12, 2019.
In this post, we seek to understand why topic modeling is important and how it helps us as data scientists. -
Research Guide for Depth Estimation with Deep Learning - Nov 12, 2019.
In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning. -
The Complete Data Science LinkedIn Profile Guide - Nov 11, 2019.
With so many Data Scientists showing up on LinkedIn, it's time to make sure your profile is top-notch because your talent is still highly sought after. Recruitment specialists want to find you fast, and this guide will help you create the best profile to feature your expertise. -
Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal - Nov 11, 2019.
ReAgent is a new framework that streamlines the implementation of reasoning systems. -
Understanding Boxplots - Nov 8, 2019.
A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed. -
Orchestrating Dynamic Reports in Python and R with Rmd Files - Nov 8, 2019.
Do you want to extract csv files with Python and visualize them in R? How does preparing everything in R and make conclusions with Python sound? Both are possible if you know the right libraries and techniques. Here, we’ll walk through a use-case using both languages in one analysis -
Data Cleaning and Preprocessing for Beginners - Nov 7, 2019.
Careful preprocessing of data for your machine learning project is crucial. This overview describes the process of data cleaning and dealing with noise and missing data. -
Set Operations Applied to Pandas DataFrames - Nov 7, 2019.
In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets. -
How to Create a Vocabulary for NLP Tasks in Python - Nov 7, 2019.
This post will walkthrough a Python implementation of a vocabulary class for storing processed text data and related metadata in a manner useful for subsequently performing NLP tasks. -
An Eight-Step Checklist for An Analytics Project - Nov 6, 2019.
Follow these eight headings of an audit sheet that business analysts should address before submitting the results of their analytics project. One recommended approach is to rewrite each step as a question, answer it, and then attach it to your project. -
Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
This guide explores research centered on a variety of advanced loss functions for machine learning models. -
10 Free Must-read Books on AI - Nov 5, 2019.
Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work. -
Probability Learning: Maximum Likelihood - Nov 5, 2019.
The maths behind Bayes will be better understood if we first cover the theory and maths underlying another fundamental method of probabilistic machine learning: Maximum Likelihood. This post will be dedicated to explaining it. -
Designing Your Neural Networks - Nov 4, 2019.
Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture. -
Customer Segmentation Using K Means Clustering - Nov 4, 2019.
Customer Segmentation can be a powerful means to identify unsatisfied customer needs. This technique can be used by companies to outperform the competition by developing uniquely appealing products and services. -
Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch - Nov 4, 2019.
The new release of PyTorch includes some impressive open source projects for deep learning researchers and developers. -
Top Machine Learning Software Tools for Developers - Nov 1, 2019.
As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out. -
Build an Artificial Neural Network From Scratch: Part 1 - Nov 1, 2019.
This article focused on building an Artificial Neural Network using the Numpy Python library. -
What is Machine Learning on Code? - Nov 1, 2019.
Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.
October:
- How to Build Your Own Logistic Regression Model in Python
- AutoML for Temporal Relational Data: A New Frontier
- Research Guide for Transformers
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5 Statistical Traps Data Scientists Should Avoid
Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid. - How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow
- Data Sources 101
- How Bayes’ Theorem is Applied in Machine Learning
- DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models
- 5 Advanced Features of Pandas and How to Use Them
- Harnessing Semiotics and Discourse Communities to Understand User Intent
- Introduction to Natural Language Processing (NLP)
- Feature Selection: Beyond feature importance?
- Convolutional Neural Network for Breast Cancer Classification
- Intro to Adversarial Machine Learning and Generative Adversarial Networks
- How to Measure Foot Traffic Using Data Analytics
- Time Series Analysis: A Simple Example with KNIME and Spark
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Everything a Data Scientist Should Know About Data Management
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions. - How to Write Web Apps Using Simple Python for Data Scientists
- Anomaly Detection, A Key Task for AI and Machine Learning, Explained
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How YouTube is Recommending Your Next Video
If you are interested in learning more about the latest Youtube recommendation algorithm paper, read this post for details on its approach and improvements.
- This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training
- Building an intelligent Digital Assistant
- Writing Your First Neural Net in Less Than 30 Lines of Code with Keras
- Data Anonymization – History and Key Ideas
- How to Easily Deploy Machine Learning Models Using Flask
- Probability Learning I: Bayes’ Theorem
- The 5 Classification Evaluation Metrics Every Data Scientist Must Know
- Research Guide for Video Frame Interpolation with Deep Learning
- Three Things to Know About Reinforcement Learning
- Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search
- An Overview of Density Estimation
- Beyond Word Embedding: Key Ideas in Document Embedding
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Activation maps for deep learning models in a few lines of code
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code. - Contributing to PyTorch: By someone who doesn’t know a ton about PyTorch
- Introduction to Artificial Neural Networks
- Know Your Data: Part 2
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The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization
As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.
- OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned
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10 Free Top Notch Natural Language Processing Courses
Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.
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The Last SQL Guide for Data Analysis You’ll Ever Need
This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.
- Research Guide for Neural Architecture Search
- 5 Fundamental AI Principles
- Recreating Imagination: DeepMind Builds Neural Networks that Spontaneously Replay Past Experiences
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Choosing the Right Clustering Algorithm for your Dataset
Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.
- Data Preparation for Machine learning 101: Why it’s important and how to do it
- Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained
- Sentiment and Emotion Analysis for Beginners: Types and Challenges
- Clustering Metrics Better Than the Elbow Method
