Complete Numpy Course - Data Science In Python

Master Python's central data science and scientific computing library: NumPy
What you'll learn
How to solve math / statistics problems using NumPy.
Perform the most common array manipulation operations in Machine Learning / Data Science.
Solve problems common to linear algebra, statistics and image processing using the NumPy library.
Requirements
Basic notions of Python programming.
Description
In this course you will learn to use the NumPy library fluently. NumPy is a numerical computation library extensively used in data science, machine learning and statistics. In fact, many other libraries in these fields rely on NumPy arrays to deliver their functionality efficiently. In the area of data science and machine learning we often work with tabular data, which can be represented very well by NumPy arrays. In the course you will learn how to work with n-dimensional arrays and how to manipulate them comfortably to solve complex tasks in different domains. NumPy processes matrix operations extremely efficiently, offering low execution time and memory usage. Its functionality is implemented in the C programming language: a very efficient compiled language. This functionality is executed from the Python interface with a simple declarative syntax.The course is divided into 12 lessons:- Introduction to the NumPy library.- Creating, indexing and slicing NumPy arrays.- Copying and editing NumPy arrays.- Stacking and restructuring NumPy arrays.- Arithmetic operations with NumPy arrays.- Operations with NumPy arrays of different shapes.- Concatenation, reversion and persistence of NumPy arrays.- Applications of NumPy - Random number generation- Applications of NumPy - Statistics- Applications of NumPy - Linear algebra- Applications of NumPy - Image manipulation- Applications of NumPy - Chaotic dynamical systemsAt the end of the course, you will know how to create arrays using different methods, manipulate them and perform mathematical operations with them.
Overview
Section 1: NumPy basics
Lecture 1 Introduction to NumPy
Lecture 2 Creating, indexing and slicing NumPy arrays
Lecture 3 Copying and editing NumPy arrays
Lecture 4 Stacking and restructuring NumPy arrays
Lecture 5 Arithmetic operations with NumPy arrays
Lecture 6 Operations with NumPy arrays of different shapes
Lecture 7 Concatenation, reversion and persistence of NumPy arrays
Section 2: NumPy applications
Lecture 8 Applications of NumPy - Random number generation
Lecture 9 Applications of NumPy - Statistics
Lecture 10 Applications of NumPy - Linear algebra
Lecture 11 Applications of NumPy - Image manipulation
Lecture 12 Applications of NumPy - Generating the Julia Set (Chaotic dynamical systems)
Data science students.,Professionals of any scientific or engineering discipline.,Programmers interested in machine learning.,Data analysts interested in expanding their knowledge.


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