Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this free Intro to R for Data Science Tutorial. 
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Learning Python? Take this free Intro to Python for Data Science Tutorial. |
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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An Intuitive Introduction to Gradient Descent - Jun 21, 2018.
This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate. -
Detecting Sarcasm with Deep Convolutional Neural Networks - Jun 21, 2018.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence. -
Deep Learning Best Practices – Weight Initialization - Jun 21, 2018.
In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward. -
The 5 Clustering Algorithms Data Scientists Need to Know - Jun 20, 2018.
Today, we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons! -
Data Science Predicting The Future - Jun 19, 2018.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future. -
Choosing the Right Metric for Evaluating Machine Learning Models — Part 2 - Jun 19, 2018.
This will focus on commonly used metrics in classification, why should we prefer some over others with context. -
Natural Language Processing Nuggets: Getting Started with NLP - Jun 19, 2018.
Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next. -
Step Forward Feature Selection: A Practical Example in Python - Jun 18, 2018.
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset. -
IoT on AWS: Machine Learning Models and Dashboards from Sensor Data - Jun 15, 2018.
I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards. -
Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health - Jun 14, 2018.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv. -
Generating Text with RNNs in 4 Lines of Code - Jun 14, 2018.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code." -
How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning - Jun 13, 2018.
An end-to-end example of how to build a system that can search objects semantically. -
The What, Where and How of Data for Data Science - Jun 12, 2018.
Here we will take data science apart and build it back up to a coherent and manageable concept. Bear with us! -
A Better Stats 101 - Jun 12, 2018.
Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why. -
5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out! -
Packaging and Distributing Your Python Project to PyPI for Installation Using pip - Jun 11, 2018.
This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda. -
DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer. -
Command Line Tricks For Data Scientists - Jun 7, 2018.
Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive. -
Audience Segmentation - Jun 6, 2018.
The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them. -
Introduction to Game Theory (Part 1) - Jun 6, 2018.
Check out this game theory basics post for an introduction to Two-player Sequential games — Dominant Strategies, Nash Equilibrium, and Cooperation vs. Defection. -
Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation - Jun 6, 2018.
A brief introduction into machine learning model interpretation. -
Three techniques to improve machine learning model performance with imbalanced datasets - Jun 5, 2018.
The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem. -
The Keras 4 Step Workflow - Jun 4, 2018.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example. -
Using Linear Regression for Predictive Modeling in R - Jun 1, 2018.
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.
May:
- On the contribution of neural networks and word embeddings in Natural Language Processing
- Introduction to Content Personalization
- Improving the Performance of a Neural Network
- 6 Tips for Effective Visualization with Tableau
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A Beginner’s Guide to the Data Science Pipeline
On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N." -
10 More Free Must-Read Books for Machine Learning and Data Science
Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started. -
Top 20 R Libraries for Data Science in 2018
We have prepared an infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.
- Modelling Time Series Processes using GARCH
- How to tackle common data cleaning issues in R
- Deep Learning With Apache Spark: Part 2
- Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off
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Frameworks for Approaching the Machine Learning Process
This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
- Optimization Using R
- An Introduction to Deep Learning for Tabular Data
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How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
- GANs in TensorFlow from the Command Line: Creating Your First GitHub Project
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Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
- A Brief Introduction to Wikidata
- Simple Derivatives with PyTorch
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PyTorch Tensor Basics
This is an introduction to PyTorch's Tensor class, which is reasonably analogous to Numpy's ndarray, and which forms the basis for building neural networks in PyTorch.
- The Executive Guide to Data Science and Machine Learning
- Deep learning scaling is predictable, empirically
- Top 7 Data Science Use Cases in Finance
- Detecting Breast Cancer with Deep Learning
- How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge
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5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist
Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
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WTF is a Tensor?!?
A tensor is a container which can house data in N dimensions, along with its linear operations, though there is nuance in what tensors technically are and what we refer to as tensors in practice.
- Skewness vs Kurtosis – The Robust Duo
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Boost your data science skills. Learn linear algebra.
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.
- Getting Started with spaCy for Natural Language Processing
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50+ Useful Machine Learning & Prediction APIs, 2018 Edition
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
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Data Science vs Machine Learning vs Data Analytics vs Business Analytics
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI. - Jupyter Notebook for Beginners: A Tutorial
- Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText