Complete NumPy course – Data Science in Python

Complete NumPy course – Data Science in Python, available at $49.99, has an average rating of 4.65, with 15 lectures, based on 33 reviews, and has 152 subscribers.

You will learn about 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. This course is ideal for individuals who are Data science students. or Professionals of any scientific or engineering discipline. or Programmers interested in machine learning. or Data analysts interested in expanding their knowledge. It is particularly useful for Data science students. or Professionals of any scientific or engineering discipline. or Programmers interested in machine learning. or Data analysts interested in expanding their knowledge.

Enroll now: Complete NumPy course – Data Science in Python

Summary

Title: Complete NumPy course – Data Science in Python

Price: $49.99

Average Rating: 4.65

Number of Lectures: 15

Number of Published Lectures: 15

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 15

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Data science students.
Professionals of any scientific or engineering discipline.
Programmers interested in machine learning.
Data analysts interested in expanding their knowledge.

Target Audiences

Data science students.
Professionals of any scientific or engineering discipline.
Programmers interested in machine learning.
Data analysts interested in expanding their knowledge.

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 systems

At the end of the course, you will know how to create arrays using different methods, manipulate them and perform mathematical operations with them.

Course Curriculum

Chapter 1: NumPy basics

Lecture 1: Before we begin

Lecture 2: Our complete course catalog

Lecture 3: Follow us on social media

Lecture 4: Introduction to NumPy

Lecture 5: Creating, indexing and slicing NumPy arrays

Lecture 6: Copying and editing NumPy arrays

Lecture 7: Stacking and restructuring NumPy arrays

Lecture 8: Arithmetic operations with NumPy arrays

Lecture 9: Operations with NumPy arrays of different shapes

Lecture 10: Concatenation, reversion and persistence of NumPy arrays

Chapter 2: NumPy applications

Lecture 1: Applications of NumPy – Random number generation

Lecture 2: Applications of NumPy – Statistics

Lecture 3: Applications of NumPy – Linear algebra

Lecture 4: Applications of NumPy – Image manipulation

Lecture 5: Applications of NumPy – Generating the Julia Set (Chaotic dynamical systems)

Instructors

Escape Velocity Labs
Hands-on, comprehensive AI courses

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 7 votes
4 stars: 5 votes
5 stars: 20 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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