Why Learn NumPy for Machine Learning?
Free tutorial
2,522 students
1hr 51min of on-demand video
Created by Saima Aziz
English
What you’ll learn
- How to create NumPy arrays, 1D and 2D and nd arrays
- Some built-in functions of Numpy,
- Slicing in Numpy
- Broadcasting, and manipulating arrays
- Trigonometric function
- Random sampling
- String operations
- Concatenate function
- Sort, Unique, Union, Intersection etc.
Requirements
- There are no prerequisites for this course, but it might be helpful if you are familiar with, Python Fundamentals for Data Science, by Saima Aziz
- Laptop or PC with Internet Connection
- Motivation to learn
Description
Welcome to Learn NumPy for Machine Learning course. My name is Saima Aziz and I will be the instructor for this course.
In this course we will learn how to create Numpy arrays, learn some built-in functions, access values, broadcasting and manipulating arrays etc.
Python is a general purpose and high level programming language. You can use Python for developing desktop GUI applications, websites and web applications. We will learn Numpy from scratch, which is one of the most popular Python programming language library.
Numpy stands for ‘Numerical Python’. It is an open-source Python library used to perform various mathematical and scientific tasks. It contains multi-dimensional arrays and matrices, along with many high-level mathematical functions that operate on these arrays and matrices. Moreover, NumPy forms the foundation of Machine Learning.
NumPy helps to calculate large quantities and common descriptive statistics. It is very useful for handling linear algebra, fourier transforms, and random numbers. It’s high speed coupled with easy to use functions make it a favorite among Data Science and Machine Learning practitioners. Many of its functions are very useful for performing any mathematical or scientific calculation.
I encourage you to take the course from beginning to end to get the full learning experience. Some topics may be very easy for you and others will be challenging, but each topic should offer something of value.
Hope you will enjoy the course!
Who this course is for:
- Beginners, who want to learn Numpy, Python library from scratch and curious to learn data science and machine learning.
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Course content
3 sections • 14 lectures • 1h 50m total lengthCollapse all sections
Introduction1 lecture • 2min
- Introduction01:48
Introduction to Numpy12 lectures • 1hr 49min
- 1D and 2D array17:11
- dtype1 question
- 3D array03:21
- 3D array1 question
- Full,ones,zeros,empty,eye builtin functions09:52
- Eye function2 questions
- Arange,linspace,reshape,ravel,flatten and transpose functions12:07
- Numpy functions1 question
- Addition, subtraction, multiplication, division, maxmin, standard deviation, mea17:00
- Standard deviation2 questions
- Slicing in Numpy08:27
- Slicing1 question
- Concatenate function05:00
- concatenate function1 question
- Trigonometric function07:45
- Sin()1 question
- random sampling07:39
- Random function1 question
- string operations09:42
- String Operations1 question
- Sort, Unique,Union, Intersection etc. functions05:25
- Intersection1 question
- Broadcasting05:25
- Scaler matrix1 question
Congratulations1 lecture • 1min
- Congratulations00:12
