Learn Python NumPy for Machine Learning

Name: Learn Python NumPy for Machine Learning
Rating: 4.4 (95 reviews)

Why Learn NumPy for Machine Learning?

Created bySaima Aziz

Last updated 2/2021

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.

Course content

3 sections • 14 lectures • 1h 50m total length

Introduction1:48
Explore NumPy, the numerical Python library, and learn to create, manipulate, and operate on arrays. Apply mathematical and logical operations, broadcasting, and trigonometric functions for data analysis and machine learning.

1D and 2D array17:11
Explore NumPy basics by creating 1D and 2D arrays, including vectors and matrices. Learn array properties, indexing, mutability, and shape and size using examples.
dtype
3D array3:21
Learn to create a 3d array in python using lists of lists of lists, print the array, check its type, and determine its total elements with a size function.
3D array
Full,ones,zeros,empty,eye builtin functions9:52
Explore numpy built-ins such as full, zeros, ones, empty, eye, and random to create matrices of specified shapes and values, convert data types, and generate diagonal or random arrays.
Eye function
Arange,linspace,reshape,ravel,flatten and transpose functions12:07
Learn numpy basics by using arange, linspace, reshape, ravel, flatten, and transpose to transform 1d arrays into 2d or 3d, control spacing, and explore shape compatibility in machine learning.
Slicing in Numpy8:27
Learn how to slice numpy arrays by rows and columns, reshape ranges into 2D matrices, and access single elements, entire rows, or columns using zero-based indexing.
Standard deviation
Numpy functions
Addition, subtraction, multiplication, division, maxmin, standard deviation, mea17:00
Learn NumPy matrix operations in Python, including adding, subtracting, dividing, and multiplying matrices, reshaping with range and indexing, computing max, min, mean, and median, standard deviation, exponent, sqrt, and log.
Slicing
Concatenate function5:00
Explore how to use numpy concatenate to join lists and 2d arrays, with axis-based row or column stacking and shape requirements. Learn horizontal stacking with restack when combining multiple arrays.
concatenate function
Trigonometric function7:45
Explore trigonometric functions using numpy and matplotlib to compute and visualize sine and cosine values, plot x-y relationships, and understand plotting basics.
Sin()
random sampling7:39
Explore random sampling with NumPy: generate arrays of random floats and integers, shape results, reproduce outputs with seed, and use choice and permutation for selections.
Random function
string operations9:42
Explore string operations in Python, including concatenation, spacing, joining, splitting, and center formatting. Learn to replace text, compare strings, count occurrences, find indices, and check lower or upper cases.
String Operations
Sort,Unique,Union,Intersection etc. functions5:25
Learn how numpy broadcasting lets you add arrays of different shapes, using ones matrices, transposes, and scalar arrays to apply broadcasting rules.
Intersection
Broadcasting5:25
Explore numpy broadcasting by adding a 3 by 4 matrix to a 1 by 4 row using axis rules and scalar broadcasting, with transpose used to align axes.
Scaler matrix

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 NumPy for Machine Learning

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 2min

Introduction to Numpy12 lectures • 1hr 49min

Congratulations1 lecture • 1min

Requirements

Description

Who this course is for: