Guides

Technology

Machine Learning & AI

Quick ML: machine learning on your iPhone and iPad

A walkthrough of Quick ML, the on-device data studio for iPhone and iPad. Import data from files, the web, Kaggle and PostgreSQL, clean it with an undoable recipe, train regression, classification, clustering and image models, predict new values, build dashboards and export to Core ML or Python.

ChartsClusteringImagesK-MeansLinear RegressionLogistic RegressionMachine LearningNeural NetworksRandom ForestsStatisticsSupervised LearningUnsupervised Learning

Python - A Quick Start for existing Programers

Quick start guide for people with existing programming skills in other languages, that want to start using Python. Focuses more on the need to know and syntax, rather than how to program that most tutorials do.

Python

NumPy - The Basics

How to install, Creating Arrays, Reshaping, flattening and slicing arrays and universal functions.

NumPyPython

Pandas Basics

A guide to get you started with using the Pandas Python library. How to install, working with series and data frames, importing and exporting data, manipulating and cleaning data and visualising data.

PandasPython

Feature-engine, a Python library for feature engineering

Quick start guide to using Feature-engine Python library, displaying what it is, how to use it and what transformers are available.

Feature EngineeringFeature-engineMachine LearningPandasPythonscikit-learnTransformers

Matplotlib Basics

Quick overview of the Matplotlib Python library that is used for creating graphs and charts in Python. Examples of common charting options and the different customisations and styling options.

ChartsGraphsMatplotlibNumPyPandasPythonVisualisation

Seaborn basics

Quick overview of the Seaborn Python library that is used for creating graphs and charts in Python. Examples of common charting options and the different customisations and styling options.

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

Maths: Statistics for machine learning

Overview of essential maths statistics that are required for machine learning and data science. Covering population and samples, probability, descriptive statistics, Inferential statistics, distributions and hypothesis testing

Machine LearningMathsNumPyPandasPythonStatistics

Machine Learning Fundamentals with Python

Introduces the core concepts of machine learning, linear and logistic regression, K-Means clustering, Decision Trees, Random Forests, neural networks, text and image analysis and evaluating model performance

ClusteringImagesK-MeansLinear RegressionLogistic RegressionMachine LearningNeural NetworksNLPNumPyPythonRandom Forestsscikit-learnSupervised LearningUnsupervised Learning

Scikit-learn Basics

Introduces the core concepts of Scikit-learn, API overview, data preperation and feature engineering, supervised and unsupervised learning, model eveluation, hyperparameter tuning, pipelines and saving and loading models

ClusteringFeature EngineeringK-MeansLinear RegressionLogistic RegressionMachine LearningNumPyPythonRandom Forestsscikit-learnSupervised LearningUnsupervised Learning

SciPy - Statistical Testing

Practical guide as to how to run statistical tests in Python using the SciPy library. No theory, just code examples.

PythonSciPyStatistics

Keras Basics

Quick overview of Keras, TensorFlow, library. Overview of the API, different models, optimising, tuning, loading and saving.

KerasNeural NetworksPythonTensorFlow

End-to-End Machine Learning: Titanic Survival Prediction

A short end to end look at the Titanic dataset, making a logistic regression, random forest and neural network prediction model to predict if a person will survive based on the parameters provided.

KerasMachine LearningMatplotlibNumPyPandasPythonscikit-learnSciPySeabornTensorFlow