Published 08/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 37 lectures (3h 46m) | Size: 949.7 MB
NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course
What you’ll learn
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Introduction to NumPy Library
The Power of NumPy
Creating NumPy Array with The Array() Function
Creating NumPy Array with Zeros() Function
Creating NumPy Array with Ones() Function
Creating NumPy Array with Full() Function
Creating NumPy Array with Arange() Function
Creating NumPy Array with Eye() Function
Creating NumPy Array with Linspace() Function
Creating NumPy Array with Random() Function
Properties of NumPy Array
Reshaping a NumPy Array: Reshape() Function
Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions
Detecting Least Element of Numpy Array: Min(), Argmin() Functions
Concatenating Numpy Arrays: Concatenate() Function
Splitting One-Dimensional Numpy Arrays: The Split() Function
Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function
Sorting Numpy Arrays: Sort() Function
Indexing Numpy Arrays
Slicing One-Dimensional Numpy Arrays
Slicing Two-Dimensional Numpy Arrays
Assigning Value to One-Dimensional Arrays
Assigning Value to Two-Dimensional Array
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
Requirements
No prior knowledge of Numpy is required
Free software and tools used during the course
Basic computer knowledge
Desire to learn Python and Numpy library
Nothing else! It’s just you, your computer and your ambition to get started today
Desire to learn data science
Desire to learn Python
Desire to work on machine learning
Desire to learn python machine learning A-Z
Description
Description
Hello there,
Welcome to “NumPy Python Programming Language Library from Scratch A-Z™” Course
NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important. numpy, numpy stack, numpy python, scipy, Python numpy, deep learning, artificial intelligence, lazy programmer, pandas, machine learning, Data Science, Pandas, Deep Learning, machine learning python, numpy course
POWERFUL N-DIMENSIONAL ARRAYS: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
NUMERICAL COMPUTING TOOLS: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
INTEROPERABLE: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
PERFORMANT: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
EASY TO USE: NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
OPEN SOURCE: Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
Nearly every scientist working in Python draws on the power of NumPy.
NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you.
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.
Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization.
The core programming language is quite small and the standard library is also large. In fact, Python’s large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.
Are you ready for a Data Science career?
Do you want to learn the Python Numpy from Scratch? or
Are you an experienced Data scientist and looking to improve your skills with Numpy!
In both cases, you are at the right place! The number of companies and enterprises using Python is increasing day by day. The world we are in is experiencing the age of informatics. Python and its Numpy library will be the right choice for you to take part in this world and create your own opportunities,
In this course, we will open the door of the Data Science world and will move deeper. You will learn the fundamentals of Python and its beautiful library Numpy step by step with hands-on examples. Most importantly in Data Science, you should know how to use effectively the Numpy library. Because this library is limitless.
Throughout the course, we will teach you how to use Python in Linear Algebra and we will also do a variety of exercises to reinforce what we have learned in this Data Science Using Python Programming Language: NumPy Library | A-Z™ course.
In this course you will learn;
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Introduction to NumPy Library
The Power of NumPy
Creating NumPy Array with The Array() Function
Creating NumPy Array with Zeros() Function
Creating NumPy Array with Ones() Function
Creating NumPy Array with Full() Function
Creating NumPy Array with Arange() Function
Creating NumPy Array with Eye() Function
Creating NumPy Array with Linspace() Function
Creating NumPy Array with Random() Function
Properties of NumPy Array
Reshaping a NumPy Array: Reshape() Function
Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions
Detecting Least Element of Numpy Array: Min(), Argmin() Functions
Concatenating Numpy Arrays: Concatenate() Function
Splitting One-Dimensional Numpy Arrays: The Split() Function
Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function
Sorting Numpy Arrays: Sort() Function
Indexing Numpy Arrays
Slicing One-Dimensional Numpy Arrays
Slicing Two-Dimensional Numpy Arrays
Assigning Value to One-Dimensional Arrays
Assigning Value to Two-Dimensional Array
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
What is data science?
We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science python uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Python data science seeks to find patterns in data and use those patterns to predict future data.
It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science using python includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a python for data science, it progresses by creating new algorithms to analyze data and validate current methods.
What is python?
Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn.
Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization.
The core programming language is quite small and the standard library is also large. In fact, Python’s large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.
What is NumPy?
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
What is machine learning?
Machine learning describes systems that make predictions using a model trained on real-world data. For example, let’s say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it’s fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.
What is machine learning used for?
Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use.
Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.
What is NumPy is used for?
NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
What is the difference between NumPy and Python?
NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory.
What is NumPy arrays in Python?
A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.
Why NumPy is used in Machine Learning?
NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning.
What is NumPy array example?
It is basically a table of elements which are all of the same type and indexed by a tuple of positive integers. The dimensions are called axis in NumPy. The NumPy’s array class is known as ndarray or alias array. The numpy. array is not the same as the standard Python library class array.
What are the benefits of NumPy in Python?
NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.
Why would you want to take this course?
We have prepared this course in the simplest way for beginners and have prepared many different exercises to help them understand better.
No prior knowledge is needed!
In this course, you need no previous knowledge about Python or Numpy.
This course will take you from a beginner to a more experienced level.
If you are new to data science or have no idea about what data science is, no problem, you will learn anything from scratch you need to start data science.
If you are a software developer or familiar with other programming languages and you want to start a new world, you are also in the right place. You will learn step by step with hands-on examples.
You’ll also get
· Lifetime Access to The Course
· Fast & Friendly Support in the Q&A section
· Udemy Certificate of Completion Ready for Download
Dive in now NumPy Python Programming Language Library from Scratch A-Z™
NumPy Library for Data Science, Machine Learning,Pandas, Deep Learning using Python from A-Z with the NumPy stack course
We offer full support, answering any questions.
See you in the course!
Who this course is for
Anyone who wants to learn Numpy
Anyone who want to use effectively linear algebra,
Software developer whom want to learn the Neural Network’s math,
Data scientist whom want to use effectively Numpy array
Anyone interested in data sciences
Anyone who plans a career in data scientist,
Anyone eager to learn python with no coding background
Anyone who is particularly interested in big data, machine learning
Anyone eager to learn Python with no coding background
Anyone who wants to learn Numpy
Homepage
https://www.udemy.com/course/numpy-python-programming-language-library-from-scratch-a-ztm/
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