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Numerical and Scientific Computing with SciPy

Video Introducing this tutorial

Installation and Setup :
The Course Overview
Python Installation

Python :
Overview of Python in Engineering and Scientific Computing
Python and the IPython (now Jupiter) Notebook

NumPy and its functionality :
Working with NumPy Arrays
Avoiding For Loops in Some Mathematical Operations via NumPy Arrays
Matrices as an Efficient Way to Operate with Data
Implementation in NumPy of a Matrix Object and Some Operations
Functionality of NumPy for Reading and Writing Data

SciPy and its Functionality :
General Introduction to SciPy
Statistics with SciPy
Fitting Curves with the SciPy Library
Solving Ordinary Differential Equations with the SciPy Library
SciPy Library Special Functions

Matplotlib :
Two Dimensional Plots via Matplotlib (2D plots)
Three Dimensional Plots via Matplotlib (3D plots)
Scatter and Contour Plots via Matplotlib
Plotting Histograms via Matplotlib

Data Preprocessing and Machine Learning Language :
Generalities on Machine Learning
Generalities on Working with Data: Getting it and Putting it in the Right Format
Data Preprocessing and Exploration
Collapsing Data via Principal Component Analysis
Generalities of Supervised and Unsupervised Learning

Solving the Regression Problem in Machine Learning Language :
Overview of Optimization and the Gradient Descent Method
Gradient Descent Implementation via NumPy and Examples Comparing it with SciPy Functions for Optimization
The Linear Regression Problem and its Solution via Gradient Descent
Solving a Non-Linear Regression Problems via Gradient Descent and Some Thoughts for Improvements

Logistic Classification :
Overview of Logistic Regression for Classification and Prediction
Implementing Logistic Regression for Classification via SciPy Functions