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. |
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R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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Introduction to Statistics for Data Science - Dec 17, 2018.
This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning. -
Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification - Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model. -
State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community. -
Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes. -
Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook. -
Automated Web Scraping in R - Dec 11, 2018.
How to automatically web scrape periodically so you can analyze timely/frequently updated data. -
Introduction to Named Entity Recognition - Dec 11, 2018.
Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how. -
A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more. -
The Machine Learning Project Checklist - Dec 7, 2018.
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow." -
Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid! -
Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation. -
Four Techniques for Outlier Detection - Dec 6, 2018.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection. -
How to build a data science project from scratch - Dec 5, 2018.
A demonstration using an analysis of Berlin rental prices, covering how to extract data from the web and clean it, gaining deeper insights, engineering of features using external APIs, and more. -
Handling Imbalanced Datasets in Deep Learning - Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
November:
- Deep Learning for the Masses (… and The Semantic Layer)
- Variational Autoencoders Explained in Detail
- Sales Forecasting Using Facebook’s Prophet
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Deep Learning Cheat Sheets
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.
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Intro to Data Science for Managers
This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisions -
An Introduction to AI
We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more. - Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices
- Top 10 Python Data Science Libraries
- Sorry I didn’t get that! How to understand what your users want
- Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU
- Mastering The New Generation of Gradient Boosting
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The 5 Basic Statistics Concepts Data Scientists Need to Know
Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
- Multi-Class Text Classification with Doc2Vec & Logistic Regression
- Deep Learning Performance Cheat Sheet
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10 Free Must-See Courses for Machine Learning and Data Science
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- Introduction to PyTorch for Deep Learning
- Text Preprocessing in Python: Steps, Tools, and Examples
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi
- Mastering the Learning Rate to Speed Up Deep Learning
- Quantum Machine Learning: A look at myths, realities, and future projections
- Machine Learning Classification: A Dataset-based Pictorial
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Top 13 Python Deep Learning Libraries
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Data Representation for Natural Language Processing Tasks
- Multi-Class Text Classification Model Comparison and Selection