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Numerical and Scientific Computing with SciPy
Installation and Setup
The Course Overview (5:49)
Python Installation (6:30)
Python
Overview of Python in Engineering and Scientific Computing (3:20)
Python and the IPython (now Jupiter) Notebook (6:42)
NumPy and its functionality
Working with NumPy Arrays
Avoiding For Loops in Some Mathematical Operations via NumPy Arrays (9:50)
Matrices as an Efficient Way to Operate with Data (7:58)
Implementation in NumPy of a Matrix Object and Some Operations (7:21)
Functionality of NumPy for Reading and Writing Data (8:56)
SciPy and its Functionality
General Introduction to SciPy (7:13)
Statistics with SciPy (11:01)
Fitting Curves with the SciPy Library (6:02)
Solving Ordinary Differential Equations with the SciPy Library (14:16)
SciPy Library Special Functions (7:15)
Matplotlib
Two Dimensional Plots via Matplotlib (2D plots) (6:37)
Three Dimensional Plots via Matplotlib (3D plots) (7:29)
Scatter and Contour Plots via Matplotlib (5:21)
Plotting Histograms via Matplotlib (3:39)
Data Preprocessing and Machine Learning Language
Generalities on Machine Learning (6:19)
Generalities on Working with Data: Getting it and Putting it in the Right Format (4:41)
Data Preprocessing and Exploration (5:34)
Collapsing Data via Principal Component Analysis (9:10)
Generalities of Supervised and Unsupervised Learning (5:01)
Solving the Regression Problem in Machine Learning Language
Overview of Optimization and the Gradient Descent Method (6:03)
Gradient Descent Implementation via NumPy and Examples Comparing it with SciPy Functions for Optimization (8:41)
The Linear Regression Problem and its Solution via Gradient Descent (7:47)
Solving a Non-Linear Regression Problems via Gradient Descent and Some Thoughts for Improvements (8:59)
Logistic Classification
Overview of Logistic Regression for Classification and Prediction (6:38)
Implementing Logistic Regression for Classification via SciPy Functions (7:45)
The Linear Regression Problem and its Solution via Gradient Descent
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