This is a study guide for understanding machine learning and gpt3]]

Built from the GPT3 prompts in machine learning study guide gpt

Over time I will make notes on these subjects and link to them here.

Mathematics

Linear algebra

Vector Spaces

Linear Transformations

Matrices

Determinants

Eigenvectors and Eigenvalues

Systems of Linear Equations

Vector operations

Addition

Subtraction

Scalar multiplication

Dot product

Cross product

Linear independence and span

Orthogonality

Projections

Eigendecomposition

Singular value decomposition

Principal component analysis

Calculus

Algebra

Trigonometry

Limits

Continuity

Derivatives

Integrals

Multivariate Calculus

Partial Derivatives

Gradient Descent

Vector Fields

Optimization

Convex optimization

Higher order equations

conjugate gradient

Newton’s Method

probability

statistics

basic statictics

random variables

events

probability density functions

”Introduction to Probability” by Joseph K. Blitzstein and Jessica Hwang.

advanced statistics

Markov chains

hidden Markov models

Bayesian inference

machine learning specific statistics

estimation

hypothesis testing

”The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

Regularization

L1 and L2 regularization

sparsity

Computer Science

Algorithms

data structures

software engineering

Machine Learning

Supervised learning

Linear Regression

Logistic Regression

Support Vector Machines

Decision Trees

Neural Networks

Unsupervised learning

Dimensionality reduction

Clustering

Feature selection

Data preprocessing

Predictive modeling

Regularization

deep learning

Artificial neural networks

Convolutional nerual networks

Recurrent nueral networks

Long short-term memory networks

autoencoders

Ensembles

Data Mining

Data pre-processing

feature engineering

model evaluation

Software Packages:

Python

Pandas

TensorFlow

scikit-learn

keras