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Numerical and Scientific Computing with SciPy

Video Description

Master the capabilties of SciPy and put them to use to solve your numeric and scientific computing problems

About This Video

  • Get to grips with the functionalities offered by the Python SciPy Stack (Numpy, Scipy library, and Matplotlib) to computationally tackle scientific and engineering problems.

  • Utilize various algorithms via the SciPy Stack to solve numerically problems related to linear algebra, data analysis, visualization, and much more

  • Your one-stop tutorial to master the Python SciPy Stack and write fast, efficient solutions for your numerical computational needs in any field.

  • In Detail

    The SciPy Stack is a collection of Open-Source Python libraries finding their application in many areas of technical and scientific computing. It builds on the capabilities of the NumPy array object for faster computations, and contains modules and libraries for linear algebra, signal and image processing, visualization, and much more. Accordingly, gaining a solid working knowledge on some of the basic functionality of the SciPy Stack to solve mathematical models numerically is clearly the first step before one can start using it to tackle large-scale computational projects either in the industry or in the academic world.

    This practical course begins with an introduction to the Python SciPy Stack and a coverage of its basic usage cases. You will then delve right into the different functionalities offered by the main modules comprising the SciPy Stack (Numpy, Scipy, and Matplotlib) and see the basics on how they can be implemented in real-life scenarios. You will see how you can make the most of the algorithms in the SciPy Stack to solve problems in linear algebra, numerical analysis, visualization, and much more, including some practical examples drawn from the field of Machine Learning. By the end of this course, you will have all the knowledge you need to take your understanding of the SciPy Stack to a new level altogether, and tackle the trickiest problems in numerical and scientific computational programming with ease and confidence.

    Table of Contents

    1. Chapter 1 : Installation and Setup
      1. The Course Overview 00:05:48
      2. Python Installation 00:06:28
    2. Chapter 2 : Python
      1. Overview of Python in Engineering and Scientific Computing 00:03:20
      2. Python and the IPython (now Jupiter) Notebook 00:06:40
    3. Chapter 3 : NumPy and its functionality
      1. Working with NumPy Arrays 00:16:53
      2. Avoiding For Loops in Some Mathematical Operations via NumPy Arrays 00:09:48
      3. Matrices as an Efficient Way to Operate with Data 00:07:56
      4. Implementation in NumPy of a Matrix Object and Some Operations 00:07:19
      5. Functionality of NumPy for Reading and Writing Data 00:08:54
    4. Chapter 4 : SciPy and its Functionality
      1. General Introduction to SciPy 00:07:12
      2. Statistics with SciPy 00:10:59
      3. Fitting Curves with the SciPy Library 00:06:00
      4. Solving Ordinary Differential Equations with the SciPy Library 00:14:14
      5. SciPy Library Special Functions 00:07:14
    5. Chapter 5 : Matplotlib
      1. Two Dimensional Plots via Matplotlib (2D plots) 00:06:36
      2. Three Dimensional Plots via Matplotlib (3D plots) 00:07:28
      3. Scatter and Contour Plots via Matplotlib 00:05:21
      4. Plotting Histograms via Matplotlib 00:03:39
    6. Chapter 6 : Data Preprocessing and Machine Learning Language
      1. Generalities on Machine Learning 00:06:19
      2. Generalities on Working with Data: Getting it and Putting it in the Right Format 00:04:42
      3. Data Preprocessing and Exploration 00:05:33
      4. Collapsing Data via Principal Component Analysis 00:09:09
      5. Generalities of Supervised and Unsupervised Learning 00:05:02
    7. Chapter 7 : Solving the Regression Problem in Machine Learning Language
      1. Overview of Optimization and the Gradient Descent Method 00:06:03
      2. Gradient Descent Implementation via NumPy and Examples Comparing it with SciPy Functions for Optimization 00:08:40
      3. The Linear Regression Problem and its Solution via Gradient Descent 00:07:46
      4. Solving a Non-Linear Regression Problems via Gradient Descent and Some Thoughts for Improvements 00:08:57
    8. Chapter 8 : Logistic Classification
      1. Overview of Logistic Regression for Classification and Prediction 00:06:36
      2. Implementing Logistic Regression for Classification via SciPy Functions 00:07:44