Python Resources:

Standard Library:

posix, pwd, spwd, grp, crypt, termios, tty, pty, fcntl, pipes, resource, nis, syslog
msilib, msvcrt, winreg, winsound
os, io, time, argparse, getopt, logging, getpass, curses, platform, error, ctypes
struct, codecs
threads, multiprocessing, concurrent, subprocess, sched, queue, _thread, _dummy_thread
hashlib, hmac, secrets
datetime, calendar, collections, heapq, bisect, array, weakref, types, copy, pprint, reprlib, enum
boolean, comparisons, numerics, iterators, sequences, text sequences, binary sequences, sets, maps, context managers, more
bdb, faulthandler, pdb, profilers, timeit, trace, tracemalloc
martin heinz tutorial
typing, pydoc, doctest, unittest, 2to3, test
basics, concrete exceptions, warnings, hierarchy
zlib, gzip, bz2, lzma, zipfile, tarfile
csv, configparser, netrc, xdrlib, plistlib
pickle, copyreg, shelve, marshal, dbm, sqlite3
pathlib, os.path, fileinput, stat, filecmp, tempfile, glob, fnmatch, linecache, shutil
turtle, cmd, shlex
itertools, functools, operators
gettext, locale
webbrowser, cgi, cgitb, wegiref, urllib, http, ftplib, poplib, imaplib, nntplib, smtplib, smtpd, telnetlib, uuid, socketserver, http.server, http.cookies, xmlrpc, ipaddress
parser, ast, symtable, symbol, token, keyword, tokenize, tabnanny, pyrlbr, py_compile, compileall, diss, pickletools
html, xml
zipimport, pkgutil, modulefinder, runpy, importlib
audioop, aifc, sunau, wave, chunk, colorsys, imghdr, sndhdr, ossaudiodev
email, json, mailcap, mailbox, mimetypes, base64, binhex, binascii, quopri, uu
asyncio, socket, ssl, select, selectors, asyncore asynchat, signal, mmap
numbers, math, cmath, decimal, fractions, random, statistics
disutils, ensurepip, venv, zipapp
sys, sysconfig, builtins, __main__, warnings, dataclasses, contextlib, abc, atexit, traceback, __future__, gc, inspect, site
optparse, imp
tkinter, more...


datatypes, typecasting, promoting, complex numbers, memory, arrays, indexes, slices, views, fancy indexing, boolean indexing, reshaping, merging, vectorization, math ops, aggregate ops, boolean arrays, conditionals, logic, set ops, matrix ops
arrays, boolean arrays, masking, broadcasting, fancy indexes, sorting, structured data, aggregations, ufuncs, datatypes


TDS article search
series, dataFrames, time series
aggregations, groups, concat/append, hierarchical indexes, merge/join, missing values, pivot tables, time series, vectorized objects
date ranges, merges, save to excel, file compression, histograms, pdfs, cdfs, least squares, timing, display options, pandas 1.0 features


normal distribution, dependent variables, posterior distributions, linear regression, multilevel models
random numbers, distributions, hypothesis testing, kernel density estimation
patsy, categorical variables, linear regression, discrete & logistic regression, poisson distribution, time series

Scientific Computation with SciPy:

symbolic solutions, directional field graphs, laplace transforms, numerical methods, numerical integration
simpson's rule, multiple integration, scikit-monaco, symbolic/multiprecision quadrature, laplace transforms, fourier transforms
polynomials, splines, multivariates
spectral analysis, fourier transforms, frequency-domain filters, windowing, spectrograms, convolutions, FIRs, IIRs
sparse matrices, sparse linear algebra, eigenvalue problems, graphs & networks

Feature Engineering:

setup, tips, caching, regression target transforms
univariate, multivariate, nearest-neighbor, marking imputed values
iris, digits, cal housing, labeled faces, 20 newsgroups, (more)
one-hot encoding, word counts, tf-idf, linear-to-polynomial, missing data, pipelines
CSV, HDF5, h5py, pytables, hdfstore, JSON, serialization, pickle issues
mean removal, variance scaling, sparse scaling, outlier scaling, distribution maps, normalization, category coding, binning, binarization, polynomial features.
bag of words, sparsity, vectorizers, stop words, tf-idf, decoding, applications, limits, the hashing trick, out-of-core ops

Machine Learning:

spectral co-clustering, spectral bi-clustering
MNIST, metrics, confusion matrix, precision & recall, ROC, multiple classes, error analysis, multiple labels, multiple outputs
overview, k-means, affinity propagation, mean shift, spectral, hierarchical, dbscan, optics, birch, metrics
intro, random projections, feature agglomeration, dimensional reduction, noise filter, eigenfaces
pipeline, feature union
empirical, shrunk, sparse invariance, robust estimation
user guide, ROC curves, K-fold, LvO, LpO, stratified, shuffled, group-K-fold
training, viz, predictions, CART, gini vs entropy, regularization
classification, regression, multiple outputs, complexity, tips, algos (ID3, C4.5, C5.0, CART), math, minimal cost-complexity pruning
histograms, spherical KDEs, custom estimators
validation, linear algebra, arrays, random sampling, graphs, testing, multiclass/multilabel, helpers, hashes, warnings, exceptions
curse of dimensionality, projections, manifolds, PCA, explained variance, choosing dimensions, PCA for compression, incremental PCA, randomized PCA, kernel PCA, selecting a kernel, LLE, MDS, isomap, t-SNE, LDA
dimensionality reduction, LDA, math, shrinkage, estimators
cosine similarity, kernels (linear, polynomial, sigmoid, RBF, laplacian, chisqd)
low-variance features, univariate selection, recursive elimination, selecting from a model, pipeline ops
expectation maximization (EM), confidence ellipsoids, bayes info criterion & n_clusters, covariance constraints (spherical, diagonal, tied, full), variational bayes (extension of EM)
regressions, classifiers, kernels
classification, regression, sparse data, complexity, stopping, tips, implementation
user guide, grid search, random parameters, tips, brute force alternatives
noestrem method, std kernels
user guide, OLS, ridge regression, lasso, elastic net, LARS, OMP, bayes, ARD, passive-aggressive algos, robustness, ransac vs theil-sen vs huber, polynomial regression
hello, MDS, non-linear embeddings, tradeoffs, isomap on faces
projections, PCA, principal components, MDS, locally linear embeddings (LLE), modified LLE, hessian eigenmaps, spectral embeddings, LTSA, t-SNE, isomap
PCA, incremental PCA, kernel PCA, sparse PCA, dictionary learning, factor analysis, ICA, NNMF, LDA
label formats, OvR, OvO, ECCs, multiple outputs, classifier chains, regressor chains
definition, as a classifier, as a regressor, regularization, loss functions, complexity, math, tips, warm_start
gaussian, multinomial, complement, bernoulli, out-of-core
unsupervised, KD trees, Ball trees, regressions, nearest centroids, NCA
definitions, methods, novelty detection, outlier detection, elliptic envelope, iso forest, local outlier factor, novelties with LOF
see list of kernels
python vs cython vs c, code profiling, memory profiling, cython tips, profiling compiled extensions, joblib.Parallel, warm_start
parameters, bernoulli RBM, stochastic max likelihood learning
classification, regression, density estimates, novelty detection, complexity, tips, kernel functions, implementation
classification (linear), classification (nonlinear), polynomial features, the kernel trick, similarity functions, gaussian RBF kernels, regression
validation curves, learning curves

Image Processing:

LI thresholds, max trees
contours, convex hulls, canny edge detectors, matching cubes, ridge ops, active contour model, shapes, hough transforms, skeletonize, polygon ops, edges,
RGB to grayscale, RGB to HSV, histogram matching, histogram equalization, cell stain color separation, grayscale filters & RGB images, regional maxima, gamma & log contract adjustments, tinting
DAISY, template matching, corner detection, filling holes, finding peaks, CENSURE, gabor filters, ORB, shape indexes, sliding window histograms, texture classification
hysteresis thresholds, image deconvolution, mean filters, unsharp masks, inpainting, entropy, denoising, wavelets, phase unwrapping, attribute operators
crash course, image datatypes, image adjustments, video files, basic tutorials
cascade classifiers, edge- vs region-based segmentation, image compression, rank filters
normalized cut, RAGs, thresholding, watershedding, local maxima, image region labels, flood fills, morphological snakes
swirl, image pyramids, rescale, resize, downscale, piecewise affine transforms, structural similarities, phase correlation, polar, log-polar transforms, line model estimation, radon transforms

Natural Language Processing (NLP):

similarity queries, text summaries, distance metrics, LDA, Annoy, PDLN, doc2vec, word mover, fasttext
data cleanup, bag of words, classifier fit, metrics, feature pareto, tf-idf, semantic meanings, CNN
tokens, POS tags, dependency parsing, lemmas, sentence boundaries, named entities, similarity, text classification, rule-based matches, training, serialization
tokenizization, stopwords, GloVe embeddings,

Deep Learning with Tensorflow:

gradients, activation functions, batch normalization, gradient clipping, model reuse, layer freeze & cache, model zoos, regularization
intro, sequences, unrolling, simplification, training, deep RNNs, LSTMs, GRU cells, NLP basics
intro, stacked AEs, tying weights, reconstructions
layers, filters, map stacking, padding & pooling, architectures
installation, graphs, gradient descent, momentum, model save-restore, visualization, tensorboard, sharing variables
perceptrons, MLPs, backprop, training,
openAI gym, policies, markov decision processes, q-learning

Deep Learning with PyTorch:

tensors, numpy arrays, cuda, autograd, gradients, neural net design, loss functions, backprop, weight updates, training, CNN definition, testing, GPU training, parallelism

Visualization Tools:

LOTs of plot types

Symbolic Computation:

square vs rectangular, eigenvalues, nonlinear equations, univariate equations
symbols, numbers, rationals, constants, functions, expressions, simplification, expansion, factor, collect, combine, apart, together, cancel, substitutions, evaluations, calculus, sums, products, equations, linear algebra


installation, will it work?, nopython, performance, under the hood, @decorators, groups
numba, numba.vectorize, cython, tips & tricks, cython & C

Various Utilities: