Data Science: Machine Learning algorithms in Matlab

Annotate MATLAB plots with xlabel, ylabel, title, and legend to make graphs informative, plotting with x and y vectors and using grid for clarity.

Explore how to define vectors and matrices in Matlab, use ones, zeros, and random functions, create identity matrices, and manage the workspace with clear and who.

Master the basics of linear algebra in Matlab, from scalars and vectors to matrices, and learn transpose, identity, inverse, and determinant computations.

Explore the identity matrix, determinants of square matrices, and the inverse and singularity concepts, and learn to compute matrix inverses in Matlab with built-in commands.

Learn to generate and load data in Matlab for machine learning, create a 50x2 data matrix, load from a text file, change directories, and inspect size, length, and first-column values.

Learn to manipulate data in MATLAB by accessing matrix elements with indices, using colon notation to select all elements, concatenating to add columns, and changing values.

Perform core data computations on matrices in Matlab, from addition, multiplication, and transpose to determinant, inverse, and element-wise operations, using magic, sum, max, and eye to build identity matrices.

Explore the basics of machine learning as learning from data without explicit programming, and how performance improves with experience. Identify representation, evaluation, optimization, and supervised, unsupervised, semi supervised, reinforcement learning.

Explore the four main machine learning types—supervised, unsupervised, semi-supervised, and reinforcement learning—and how inputs X and outputs y drive regression, classification, clustering, and reward-based policy.

Explore how linear regression uses the squared error cost function to evaluate and minimize error by tuning the hypothesis parameters on training data.

Apply linear regression in Matlab to estimate restaurant profit from population data using a six-file exercise and a main program.