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Predicting Diabetes Progression Based on BMI, Using Linear Regression

In this tutorial, we will consider a very simple linear regression model, which is the backbone of several time series and high dimensional models (VAR, Lasso, Adalasso, Boosting, Bagging, to name a few).

Word Embeddings in Python with Spacy and Gensim

Word embeddings are vector representations of words, which can then be used to train models for machine learning.

Neural Networks in Python: From Sklearn to PyTorch and Probabilistic Neural Networks

This tutorial covers different concepts related to neural networks with Sklearn and PyTorch. Neural networks have gained lots of attention in machine learning (ML) in the past decade with the development of deeper network architectures (known as deep learning). 

Introduction to Text Classification

In this tutorial, we will explore a basic workflow to train and evaluate a model to classify text. Note that there are many important aspects not covered in what follows, such as exploratory data analysis (EDA) or hyper-parameter optimisation.

Implementing your own recommender systems in Python

Nowadays, recommender systems are used to personalize your experience on the web, telling you what to buy, where to eat or even who you should be friends with. People's tastes vary, but generally follow patterns. 

Data Processing with Pandas Dataframe

The purpose of this tutorial is to teach you how to process data with Pandas DataFrame. At the end of this tutorial, you will be able to: load a dataset, explore data and rename columns, check and select columns, change columns’ names, describe data, identify missing values, iterate over rows and ...

An Introduction to Plotting

Introduction I'm a teaching assistant for several MSc courses, covering natural language processing, natural language understanding, information retrieval and Python programming. Students on these courses, as part of their coursework, often have to produce visualisations of data. Usually, for these ...

Build your own Skip-gram Embeddings and use them in a Neural Network

Practical Introduction to Recommender Systems

Deploying a machine learning model to the web

Data scientists often have to communicate results to other people. In my case, my supervisors might want to see some numbers or I have to write up the main insights of some work for a paper.

Collecting web data without an API

In this tutorial, we’ll show you how to extract data from Wikipedia pages. If you’ve ever gone through an online machine learning tutorial, you’re likely to be familiar with standard datasets like Titanic casualties, Iris flowers or customer tips.

EDA and interactive figures with Plotly

A crucial step of any machine learning attempt is getting a good impression of your dataset. Exploratory data analysis (or EDA) is one way to do this. It consists of summarizing the data with descriptive statistics and often involves extensive plotting. The web is full of plotting libraries that ...

Quick Panda tricks every Data Scientist should know

If you are new to the world of data science, Python's Pandas libraries are some of the best tools for quick data analysis. Pandas are built on Numpy, another popular Python library. 

Getting started with regression and decision trees

Regression analysis is one of the approaches in the Machine Learning toolbox. It is widely used in many fields but its application to real-world problems requires intuition for posing the right questions and a substantial amount of “black art” that can't be found in textbooks. While practice and ...

Implementing your own knearest neighbour algorithm using python

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients.

Getting started with XGBoost

A Beginners Guide to Kaggle Competitions