## Learn Python NumPy for Machine Learning

How to create NumPy arrays, 1D and 2D and nd arrays

Some built-in functions of Numpy,

Slicing in Numpy

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.

## Learn Python with Google Colab – A Step to Machine Learning

Basics of Python

Data types

Printing output

If-Else conditions

Looping using for, while

Arithmatic operations

Working with functions

Working with List and Arrays

Performing action on List

Tuple, Set and Dictionary

Working with packages

Hands-on with Class

## Requirements

• Basic programming concepts are sufficient

## Description

This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning  and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.

## Who this course is for:

• Beginner and Intermediate

## Beginner’s Guide to Python Arrays

Develop understanding of how Python Arrays work and what advantages they offer over other Data Structures

Create Arrays of Different Dimensions

Arrays Visualization – 2dD, 3D, 4D and higher dimensional Arrays

Array Attributes and how to use them to know more about Arrays

Use Arrays as Data containers for Common Data Operations

## Requirements

• Basic knowledge of Python (including Data Types and Structures, For Loops, List Comprehension, etc.)

## Description

Arrays are a powerful means of storing variables of the same data type (Integer, Float, String, etc.). Compared to their counterpart Data Structures, they provide many benefits, be it:

• Faster processing
• Compact memory usage
• Simpler operations with less coding effort

To give you some context, if you have worked on Pandas DataFrames, which is a special case of 2 Dimensional Arrays, you would know what different operations you can perform and how you can handle datasets more effectively. Well with Arrays you can do most of that and much more and for that very reason they are used as the preferred Data Containers to run Machine Learning algorithms (in Modules such as Scipy and Scikit-learn).

To simply put, “A good command on Arrays will take your understanding of Data Structures and their use to the next level”, and this is exactly where this course comes in. Even if you’ve not worked on Arrays earlier, you can use this course to develop your understanding grounds-up.

Here we cover, “Arrays as Data Structures and how they get applied in Python”. Below are the key areas that this course addresses :

1. Intuition of Arrays as Data Containers
2. Visualizing 2D/3D and higher dimensional Arrays
3. Array Indexing and Slicing – 2D/3D Arrays
4. Performing basic operations using Numpy Arrays

By the end of this course, you will be able to use Arrays in common data operations and data analysis. This will also give you a platform and confidence to quickly scale up to learn more advanced topics related to Numpy.

## Who this course is for:

• Anyone who wants to learn in more depth, about Numpy Arrays and put them to practical use

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## Python Basics: Directories, Arrays, Tuples and Structures

A Brief Introduction to Python

Learning about Tuples, Pythons Language Strucutre

Operators in Python

Introduction to Arrays and Lists on Python

## Requirements

• No expereince required. The course is here to teach you the basics.

## Description

The course is a beginner’s course that seeks to introduce the coding language of Python to users and teaches them how to use the program. This introduction is broken up into six parts with the hope that the learner furthers their knowledge on their own or through additional courses once complete.

The course begins by teaching the learner how to download and use Python for the first time and how the program works. The second part of the courses describes and illustrates what variables are in Python and how to create them on your own. The third part of the course seeks to educate learners on tuples, the data structures of Python and how they serve to organise your data efficiently and effectively. This organisation of your data is later built upon through the fourth part of the course, as it attempts to train learners in dictionaries and the differences between tuples and dictionaries. This understanding allows learners to understand the differences and apply them in the real world. The fifth part of the course educates learners in operators and conditional statements. Operators are the tools utilised to operate mathematical tasks and operations in Python making it a useful tool to know. Finally, the course ends of with the differences between an array and a list through a step-by-step learning exercise. This exercise, much like the previous lessons, serves to teach students in a construct manner that builds from one lesson to the next.

## Who this course is for:

• Beginner Python Developers
• Students Learning Python at School
• People wanting to Learn How to Uterlise Python

## Learn Python with Google Colab – A Step to Machine Learning

Basics of Python

Data types

Printing output

If-Else conditions

Looping using for, while

Arithmatic operations

Working with functions

Working with List and Arrays

Performing action on List

Tuple, Set and Dictionary

Working with packages

Hands-on with Class

## Requirements

• Basic programming concepts are sufficient

## Description

This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning  and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.

## Who this course is for:

• Beginner and Intermediate