Students will learn how to solve the Complex Numbers in Python
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.
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.
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!
Learn first step towards Data Science with all important concept of Numerical Python NumPy in Python For Data Science
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.
Go to my other course needed for Data Scientists. See you inside course.
Happy learning
Abbosjon Madiev
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7 sections • 19 lectures • 1h 50m total length
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
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:
In addition to the preinstalled setup and demos, the VM also comes with:
The VM is available on :
Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence
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:
6 sections • 27 lectures • 1h 59m total length
Python Pandas for data cleaning and wrangling
Creating and manipulating Series
Creating and manipulating DataFrames
Advanced techniques like merging, reshaping, MultiIndexes, Categoricals, and Dates & Times
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
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.
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.
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!
NumPy For Data Analysis
NumPy For Data Science
Numerical Computation Using Python
How To Work With Nd-arrays
How To Perform Matrix Computation
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
