
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
View
Keras Basics
Quick overview of Keras, TensorFlow, library. Overview of the API, different models, optimising, tuning, loading and saving.
KerasNeural NetworksPythonTensorFlow
View
SciPy - Statistical Testing
Practical guide as to how to run statistical tests in Python using the SciPy library. No theory, just code examples.
PythonSciPyStatistics
View
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
View
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
View
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
View
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
View
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
View
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
View
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
View
API - Application Programming Interface
Quick overview of what APIs are and what are some of the different type of APIs available.
API
View
NumPy - The Basics
How to install, Creating Arrays, Reshaping, flattening and slicing arrays and universal functions.
NumPyPython
View