WISeDOM-

Basic Python tutorials for beginners and initial intorduction to Machine Learning using Python Libraries -

The tutorial series has been built on Jupyter Notebooks and specifically targets basic programming concepts in Python which are summarized as follows -

1. Basics : a) Fundamental concepts of programming like I/O, conditonal statements, loops and functions are explained through multitude of examples. Use of numerics also covered.- Import_and_useInput.ipynb, Basic Loops.ipynb, Conditional.ipynb, Numerics.ipynb, Functions.ipynb b) Data Structres specific to python. - Lists.ipynb, Strings.ipynb, tuples.ipynb, Dictionaries.ipynb c) Intriduction to Numpy Library - NumPy_1.ipynb

2. Data Visualization using Python : a) What are datsets and how they can be used - importing_datasets.ipynb b) Using Pandas Library for gaining insights and reorganizing data as per need. - Pandas_1.ipynb , Pandas_2.ipynb c) Using seaborn library for plotting - seaborn catrgorical plots.ipynb, seaborn distribution plots.ipynb, seaborn grids.ipynb, seaborn matrix plots.ipynb, seaborn regression plots.ipynb d) datasets used are stored in folder for reference (csv format).

3. Plot - a) Using numpy and matplotlib for curve plotting - plotting.ipynb b) Graphs and other plotting techniques in matplotlib - matplotlib_1.ipynb c) Basic configurations of matplotlib styles. matpltlib_styles.ipynb

4. Linear regression - a) Showcasing statitstical univariate Linear Regression - Univariate_statform.ipynb b) Using Sklearn to implement univariate Linear regression and signifying epoch testing with open-ended question - UnivariateLinReg_ML_sklearn.ipynb c) Linear Regression implemented with Gradient Descent, coded in numpy. - Linear Regression with Gradient Descent.ipynb

5. Classification - a) Logistic Regression for binary classification, implemented in numpy - Binary Classification.ipynb b) Breast cancer prediction using Logistic regression model in Sklearn - Breast cancer predictor.ipynb

Author : Sounak Saha