Students will learn how to solve the Complex Numbers in Python

Requirements

Fundamental knowledge of Mathematics

Description

In this course, mathematics of complex numbers is explained in detailed. Mathematical operations on Complex Numbers are explained with examples. All these problems are also solved in Python. Important python modules for this course such as NumPy and Matplotlib are also the part of this course. After the completion of this course students will be able to solve Complex Number problems manually and by python.

Who this course is for:

Every one who wants to learn the basics of complex numbers

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.

Learn first step towards Data Science with all important concept of Numerical Python NumPy in Python For Data Science

Requirements

Basic knowledge of Python Programming Language

Description

Wanna learn NumPy?

Look no further. This course covers everything from how to install and import NumPy to how to solve complex problems involving array creation, transformations, and random sampling.

Course Structure

The course is presented as a series of on-demand lecture style videos with lots of animated examples, code walkthroughs, and challenge problems to test your knowledge. Go as fast or as slow as you want.

It’s difficult to describe everything around us with just one number. The world is multidimensional. The data we are consuming, product we use on daily basis, from non living organism to living organism require many feature to fully characterise and quantify it.

So if you want to learn about fastest python based numerical multi dimensional data processing framework, which is the foundation for many data science package like pandas for data analysis, sklearn scikit-learn for machine learning algorithm, you are at right place.

This course introduce with all majority of concept of NumPy – numerical python library.

I will teach from what and why of NumPy to all important concept of N dimension data processing

This course covers following topics.

Why and What NumPy is

NumPy installation

Creating NumPy array

Array indexing and slicing

Array manipulation

Mathematical & statistical function

Linear algebra function

How to persist NumPy array

Numpy practical application on Images

RGB Image to Gray scale conversion

Apply average and edge detection filter on images

Go to my other course needed for Data Scientists. See you inside course.

Happy learning

Abbosjon Madiev

Who this course is for:

Data Science Beginners

Anyone who want to learn how to process N dimensional Data

Anyone who want to learn Numpy – Numerical Python Library.

Developing & deploying AI & Machine Learning applications using python AI & ML frameworks

how to use most popular AI & ML frameworks: NumPy ,SciPy, Scikit-Learn, Matplotlib

How to use Jupyter/iPython notebook for interactive development

How to create multi-user notebook enviroment using JupyterHub

Requirements

Basic Knowledge of Python

Description

When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.

Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.

Instructor based training can be expensive at times and need your time commitment.

This course combines the best of both these options. The course is based on one of the most famous books in the field “Python Machine Learning (2nd Ed.)” by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.

You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.

The demo covers following concepts:

Machine Learning – Giving Computers the Ability to Learn from Data

Training Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using Scikit-Learn

Building Good Training Sets – Data Pre-Processing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation & Hyperparameter Optimization

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing a Multi-layer Artificial Neural Network from Scratch

Parallelizing Neural Network Training with TensorFlow

Going Deeper: The Mechanics of TensorFlow

Classifying Images with Deep Convolutional Neural Networks

Modeling Sequential Data Using Recurrent Neural Networks

In addition to the preinstalled setup and demos, the VM also comes with:

Jupyter notebook for web based interactive development

JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development

Remote desktop

Visual studio code IDE

Fish Shell

The VM is available on :

Google Cloud Platform

AWS

Microsoft Azure

Who this course is for:

Python developers who are intrested in learning Artificial Intelligence and Machine Learning

Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence

Requirements

Linear Algebra, Probability, and Python Programming

Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).

The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.

As I’ve always said: “If you can’t implement it, then you don’t understand it”.

Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.

This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Suggested Prerequisites:

linear algebra

probability

Python programming

Who this course is for:

Anyone who wants to implement Machine Learning algorithms

Advanced techniques like merging, reshaping, MultiIndexes, Categoricals, and Dates & Times

Requirements

Basic knowledge of Python

Some knowledge of NumPy preferred (not necessary)

Description

Wanna learn Pandas?

Then boy do I have good news for you! For three months I hid from my wife and responsibilities, slaving away making this course so that you could learn Python Pandas. In this course, I cover topics like

Importing and installing pandas

Series

DataFrames

Indexes (including MultiIndexes)

Reading and writing to CSV

Merge, Reshape, Aggregation operations

Dates & times

Missing values

Strings

Categoricals

including tons of examples, animations, and practice problems with detailed solutions. But rather than me drone on about the course, check out some of my free lectures in the course curriculum to see it for yourself!

Need help?

If you buy this course, you’ll have a commitment from me to help you understand any Pandas topics you might struggle with. I’m usually pretty quick to reply to questions.

Notes

This course was developed using Python 3.9.1 and Pandas version 1.2.0. If you’re on a later version, don’t worry – most of what I teach is unlikely to break.

Throughout this course, I use Google Colab as my IDE. You don’t need to use Google Colab, but if you want to, it’s a fantastic way to execute Python directly from your browser.

Also, you could take this course without knowing NumPy, but pre-existing knowledge of NumPy is preferred. After all, Pandas is built on top of it. And if you don’t know NumPy, check out my course Python NumPy For Your Grandma.

Who this course is for:

Anyone with basic Python knowledge interested in learning Pandas for data wrangling

People interested in data science and machine learning

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.

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