Learn how to build and interpret linear regression models with more than one predictor variable.

Learn to analyze, plot, and model multivariate data.

This course helps you learn the basics of natural language processing with deep learning and machine learning algorithms.

This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch.

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Data normalisation transforms our data to make it more tractable for downstream applications. This course will teach you the skills of data normalization in Pandas needed to make any dataset usable for machine learning.

Numerical Python for All, Everybody and Anybody

Learn NumPy with 70+ hands_on live running coding examples and exercises

Concept Building, Syntax and Examples of Object Oriented Programming (OOP) in Python including Inheritance

The ability to interpret the truth, accuracy, and objectivity in data visualizations is incredibly important, whether you are creating your own visualizations or are interpreting those you see. This course will teach you these skills.

This course will teach you how to leverage NumPy to up your game in scientific programming, and show you how to use NumPy for numerical processing, including array indexing, math operations, and search.