Tag: NumPy

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.

Course content

NumPy For Data Analysis

NumPy For Data Science

Numerical Computation Using Python

How To Work With Nd-arrays

How To Perform Matrix Computation

Requirements

• If students knows Python, that is well & good
• Anaconda Installation to work with the NumPy and Python
• Basic mathematics
• Willing to learn data analysis, data science or numerical computation for programm

Description

Hi, welcome to the ‘NumPy For Data Science & Machine Learning’ course. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays. And also we’re going to do a demo where we prove that using a Numpy vectorized operation is faster than normal Python lists.

So if you want to learn about the fastest python-based numerical multidimensional data processing framework, which is the foundation for many data science packages like pandas for data analysis, sklearn, scikit-learn for the machine learning algorithm, you are at the right place and right track. The course contents are listed in the “Course content” section of the course, please go through it.

I wish you all the very best and good luck with your future endeavors. Looking forward to seeing you inside the course.

Towards your success:

Pruthviraja L

Who this course is for:

• Data Analyst Beginners
• Business Analyst and AI Enthusiasts
• Python Developers Beginners
• Who Is Interested In ML, AI and Other Big Data Engineering

Course content