**Fast Numerical Computing with Python: Performing numerical computations with Python quickly and easily**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 17m | 624 MB

Get to grips with aspects of numerical computing and understand NumPy, the powerful numerical computing module, using Python!

Performing numerical computations using conventional Python methods is inefficient as complex calculations can take their toll on your system’s performance. However, NumPy, the core library for scientific computing in Python, helps keep computations fast and lets your system perform efficiently.

This course adopts a step-by-step approach where you learn through live examples. You will learn numerical computations by performing them! Gain the skills you need to become a better Python developer or data scientist. Beginning with NumPy’s arrays and functions, you’ll master linear algebra concepts, perform vector and matrix math operations, and use NumPy in Python (using quick and easy techniques) to derive numerical results faster and far more easily than with any other tool. You will understand and practice data processing and predictive modeling throughout the course.

By the end of this course, you will have developed a strong foundation in solving numerical computational problems with NumPy. You will also have a really good knowledge of Python and will be ready to advance your career as a Python developer or data scientist.

Learn

- Develop a strong foundation for numerical computational problems
- Competently program in Python and code in Jupyter Notebooks
- Use the most significant numerical computing library available for Python: NumPy
- Gain high-level access to extremely efficient computational routines with NumPy
- Use Python for data science: to work with high-level mathematical functions and simplify data
- Implement multi-dimensional arrays and matrices with NumPy’s powerful data structures to enrich your programming experience
- Perform vector and matrix operations using NumPy and linear algebra
- Work on real-world datasets to develop a predictive model using simple and multiple linear regression techniques

**Table of Contents**

**Setting Up Our Environment**

1 The Course Overview

2 Overview of Command Line

3 Installing Python

4 Running Python Code

5 Git and GitHub Overview

**NumPy Fundamentals in Python**

6 Python Fundamentals

7 Exploring Python Data Types

8 Numbers and Strings

9 Print Operations and Formatting

10 Boolean Algebra

11 Working with Lists and Dictionaries

12 Understanding Tuples and Sets

**Creating Your First Matrix Using NumPy**

13 Case Study Overview

14 Matrices

15 Building Your First Matrix

16 Matrix Operations

17 Your First Visualization

18 Creating Your First Function

**Working with NumPy**

19 Introduction to NumPy

20 Building NumPy Arrays

21 NumPy Arrays Indexing

22 Data Processing

23 Exploratory Data Analysis

24 Mathematical Functions

**Python for Data Science with NumPy**

25 Performing NumPy Operations

26 NumPy Case Study Overview

27 NumPy Case Study Solution

**Building a Document Scanner with OpenCV and NumPy**

28 Scanning Data from an Image Using OpenCV

29 Converting Information to Workable Data

30 Adding Perspective and Converting the Document

31 Data Manipulation Using NumPy

32 Performing Computations Using the Data

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