Short Course on Programming Python for Data Science and Machine Learning

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Click on each of the links below to get the details of the topic. Each page will have links to additional resources

L1 – Installing Python and IDEs
L2 – Resources Page & Cheat Sheets
L3 – Basics & Install additional Packages
L4 – Control Structures
L5 – Functions
L6 – File I/O
L7 – Pandas

Here is an example google colab notebook demonstrating many features of pandas

L8 – Graphing
L9 – Connecting to Databases

L9-2 How to speed up your Database queries in Python
L10 – Descriptive Analytics
L11 – Intro to Machine Learning & Skitlearn

L11-1 – Data Transformations – From Categorical to Numeric

L11-2 – Managing imbalanced Data Sets with SMOTE in Python

L11-3 – Using FAKER to create new data sets – Links to an external website.

L12 – Association Rules
L13 – Classifications

L13-1 – KNN
L13-2 – Naive Bayes
L13-3 – Decisions Trees
L13-4 – Regression (GLM)
L13-5 – SVM
L13-6 – Random Forests
L13-7 – Neural Networks

L13-8 – Managing Imbalanced Data Sets

L13-9 – k-Fold Cross Validation and Repeated k-Fold Cross Validation

L14 – Regression

L14-1- Linear Regression
L14-2-Logistic Regression
L14-3-Step-Wise Regression

L15 – Clustering
L16 – Creating a Word Cloud
L17 – Twitter Analytics
L18 – Forecasting