The best maths tutorials you’ve never seen

We’ve all heard the phrase ‘math is fun’ or ‘math can make you smarter’ but what’s the truth?

How do you make it fun?

Here are some of the best maths courses and resources we’ve discovered for you to keep you on your toes.1.

Basic Math: by Dr Andrew Broughton2.

Simple Math: Math for Beginners by David M. Karp3.

Introduction to Statistics by Robert E. Jones4.

Linear Regression by James B. Anderson5.

The Riemann Hypothesis: A Mathematical Approach by Andrew C. Macdonald6.

Statistics and Computational Biology by Steven Novella7.

How to Draw: An Introduction to Drawing by John M. Ritter8.

Statistical Statistics: An introduction to probability by John Ritter9.

Statistics by Jürgen Habermas10.

Statistical Methods for Engineers by Peter D. Molnar11.

Statistical Analysis by Steven M. Miller12.

Statistical Techniques by Richard K. Smith13.

Introduction the Psychology of Decision Making by Dr John D. Marks14.

Introduction Mathematical Methods in Economics by Richard S. Smith15.

Introduction Computational Geometry and its Applications by David R. Bostrom16.

Introduction Statistical Analysis: Applications and Applications by Steven R. Smith17.

Introduction of Statistical Methods: Applications to Finance by David D. Moore18.

Mathematical Statistics and Data Mining by Ravi B. Sharma19.

Introduction Statistics and Computing by Steven L. O’Connor20.

Introduction Computer Models of Natural Systems: Theory, Methods, Applications by Stephen D. Williams21.

Introduction Applied Probability and Statistics: Applications, Models, and Applications of Bayesian Probability Theory by John P. B. Roberts22.

Introduction Probability in Computer Science by John B. Galt23.

Introduction Machine Learning: An Overview by J. C. P. Watson24.

Introduction Applications of Machine Learning in the Social Sciences by Peter G. D. Miller25.

Introduction Data Mining for Economics and Management: A Practical Guide by John H. Beddoes26.

Introduction Theory and Practice in Machine Learning by John J. Tarski27.

Introduction Introduction to Machine Learning and Statistical Analysis of Data by R. Czernin28.

Introduction Understanding Machine Learning Methods by Michael S. Bader29.

Introduction Quantitative Methods for the Sciences by Jody P. Kiely30.

Introduction Advanced Topics in Statistical Methods and Applications: Data Analysis and Computation, Data Mining, Decision Processes and Methods, Statistics and Statistical Methods by Steven K. Hodge31.

Introduction Methods for Learning Data from Computer Networks by John A. Taylor32.

Introduction Analysis of Complex Data by Alan B. Naylor33.

Introduction Variational Methods in Data Analysis by Michael A. Miller34.

Introduction Optimization Methods for Statistical Analysis and Data Analysis: A Case Study in Multivariate Variational Modeling by John K. A. Van de Ven35.

Introduction Decision Trees: An In-Depth Look at Decision Trees by Alan G. Johnson36.

Introduction RNN Models in the Behavioral Sciences: Applications by Jeffrey M. Tait37.

Introduction Predictability Theory and Applications in Statistical Analysis, Modeling and Data Science by Steven J. Kukowski38.

Introduction Neural Networks in Machine Vision: Applications in Image Processing, Image Classification, and Image Recognition by Andrew B. Coker39.

Introduction Algorithms for Neural Networks and Applications to Neural Networks by David G. Krampe40.

Introduction Complex Decision Trees for Neural Network Applications: An Analysis of Decision Tree Selection, Data Preference, and Optimization by Stephen M. Wojciechowski41.

Introduction Model Selection in Neural Networks: An Application to Classification, Classificational Networks, Neural Networks, and Classificational Neural Networks with Applications by Daniel A. Raskin42.

Introduction Learning with Neural Networks as a Regression: An Advanced Tutorial by Peter J. Van Leeuwen43.

Introduction Visualizing Neural Networks using Convolutional Neural Nets: An Experimental Study by Stephen J. Pritchard44.

Introduction Deep Learning: A Beginners Guide to Deep Learning by Jonathan C. T. Oren44.

An Introduction of Deep Learning and Convolution Neural Nets by Stephen W. Giese45.

An Overview of Deep Neural Nets and Convolved Neural Nets, with Applications to Learning Visualized Datasets by Stephen A. Cottrell46.

Introduction A Basic Guide to Convolution Algorithmic Methods for Data Processing by Christopher L. Anderson47.

Introduction Inference and Classification: An Approach to Decision Making and Decision Algorithmia by Andrew L. Schmucker48.

Learning to Recognize Pattern Recognition Problems by David K. M. Hagen49.

How and Why to Identify Patterns in Neural Data by David A. Burch50.

Machine Learning for Artificial Intelligence by John T. Hensley51.

Introduction How to Use Machine Learning to Improve the Accuracy of Your Software by David H. Anderson