indigenous.engineering research takes place on ohlone land | ᎣᏪᏅᏒ / home
scraping and building datasets
practice coding:
Khan Academy Algebra Courses (in order): Pre-Algebra (start here & skip if the concepts are familiar), Algebra 1, Algebra 2
EdX Pre-Calculus Course (free course, college credit eligible for a fee)
MIT Single Variable Calculus (Calculus 1) (free full course)
Essence of Linear Algebra (video series)
MIT Linear Algebra (free full course)
supervised, unsupervised, and reinforcement learning
gradient descent
algorithms
support vector machines
linear regression
logistic regression
random forests
An Implementation and Explanation of the Random Forest in Python
Random Forest Simple Explanation note: whether an explanation is ‘simple’ or not depends on a lot of factors that can have nothing to do with the person learning, so don’t let the title intimidate you if this is not the explanation for you!
Random Forest in Python: A Practical End-to-End Machine Learning Example
An Introduction To Building a Classification Model Using Random Forests In Python
bayesian algorithms
k-means clustering
K-means Clustering in Python (code-heavy demo in python, followed by a simpler demo using scikit-learn)
jupyter notebooks in the “Machine Learning with scikit-learn” series, by Jake Vanderplas:
Deep Learning (MIT Press, complete book online), by Ian Goodfellow, Yoshua Bengio & Aaron Courville
Neural Networks & Deep Learning (complete book online) by Michael Nielson
Recurrent Neural Network (RNN) basics and the Long Short Term Memory (LSTM) cell
Recurrent neural networks and LSTM tutorial in Python and TensorFlow, code in this repo
Natural Language Processing: From Basics to using RNN and LSTM
Ultimate Guide to Understand and Implement Natural Language Processing (with codes in Python)