Pages on Python’s basic collections (lists, tuples, sets, dictionaries, queues). Maths functions. Random numbers. Conditional statements (if ,else, elif, while). Loops and iterating. List comprehensions. Unpacking lists and tuples. Lambda functions. Map and filter. Time and date. Saving python objects with pickle.
Pages on handling data in NumPy and Pandas. An introduction to NumPy arrays and Pandas DataFrames. Reading data from CSV. Sorting. Merging. Basic statistics. Subgrouping data. And more!
Python’s most popular charting library. This section shows you how to build common chart types. Line charts, scatter plots, pie charts, bar charts, boxplots, violin plots, 3D wireframe and surface plots, and heatmaps.
Use SimPy to build models of emergency departments or whole hospitals.
Key machine learning concepts for classification and regression using the excellent SciKit Learn library. How to prepare your data. Classification with logistic regression, support vector machines, Random Forests and Neural Nets. Measuring accuracy (including receiver operator characteristic curves). Feature selection, dimension reduction and feature expansion. Clustering data with k-means.
See also the notebooks using Titanic survival to teach classification with machine learning. These cover the essentials of machine learning classification, and include logistic regression. Random Forest, PyTorch and TensorFlow models. See here: https://pythonhealthcare.org/titanic-survival/
A comprehensive introduction to machine learning classification! From logistic regression through to Deep Learning neural nets in TensorFlow and PyTorch. How to measure accuracy. How to adjust and measure sensitivity of your model. How to deal with imbalanced data sets. And much more!
Experiments with creating hospital simulations (built using using SimPy), and using Deep Reinforcement Learning methods (built using PyTorch) to interact with and manage those simulated hospital environments. Watch this area grow!
Some useful statistics methods in Python. Linear regression. T-tests. Wilcoxon rank test. Mann Whitney U-test. ANOVA. Turkey’s and Holm-Bonferroni methods. Kruskal-Wallace test. Confidence intervals for proportions. Chi square test. Fisher’s exact test. Distribution fitting to data.
Some basic Natural Language Techniques. Preparation of data (tokenization, stemming and removal of stop words). Parts of speech tagging. Bag of words. Topic modelling with GenSim. Tensorflow text-based classification.
A mix of stuff! Travelling Salesman algorithm. An introduction to genetic algorithms. Multiple objective genetic algorithms with Pareto-front based GAs. Interactive charting with Holoviews. Parallel processing in Python. Function decorators. Speeding up Python with Numba. Design patterns. And a game of Pong.