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Education is one of the easy keys to being an industry fit. But, picking up the domain that suits you the best from the pool of options? That’s a bit confusing. Great Learning offers you a plethora of choices in the fields of your interests. You can walk through the courses, understand what pleases your specifications, and choose the best that suits you. Each of these courses will help you be ready by offering you the best content. You will gain Degree and PG certificates from recognized universities on successful completion of the registered program.
McCombs School of Business at The University of Texas at Austin
PG Program in Artificial Intelligence and Machine Learning: Business Applications7 months · Online · Weekend
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Applied AI and Data Science Program14 Weeks · Live Online · Weekdays & Weekend
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McCombs School of Business at The University of Texas at Austin
PG Program in Artificial Intelligence and Machine Learning: Business Applications7 months · Online · Weekend
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MIT Professional Education
No Code AI and Machine Learning: Building Data Science Solutions12 Weeks · Online · Weekend
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MIT Professional Education
Applied AI and Data Science Program14 Weeks · Live Online · Weekdays & Weekend
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McCombs School of Business at The University of Texas at Austin
Post Graduate Program in Data Science with Generative AI: Applications to Business7 months · Online · Weekend
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McCombs School of Business at The University of Texas at Austin
PG Program in Cloud Computing: Leveraging GenAI6 months · Online · Weekend
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McCombs School of Business at The University of Texas at Austin
Post Graduate Program in Generative AI for Business Applications14 Weeks · online
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McCombs School of Business at The University of Texas at Austin
Post Graduate Program in User Experience Design6 Months · Online
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Certificate Program in Applied Generative AI16 weeks · Online
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SciPy is a free and open-source Python library. It is specifically used for scientific and technical computation. It has different modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other actions that are common in science and engineering.
SciPy is a group of conferences for users and developers of tools like SciPy (which is used in the United States), EuroSciPy (in Europe), and SciPy.in (in India). Enthought introduced the SciPy conference in the United States and sponsors many international conferences, and also hosts the SciPy website. It is now sponsored by an open community of developers. SciPy is supported by NumFOCUS, a community foundation that provides support to reproducible and accessible science.
The SciPy package makes the core of Python’s scientific computing capabilities. The core components include:
Constants: Physical constants, conversion factors.
Fft: Discrete Fourier Transform algorithm.
Fftpack: Interface for Discrete Fourier Transform.
Integrate: Numerical integration routines.
IO: data input and output.
Linalg: Linear algebra routine.
Misc: Miscellaneous utilities like sample images.
Ndimage: Different functions for multi-dimensional image processing.
ODR: Orthogonal distance regression classes and algorithms.
Optimize: Algorithms including linear programming.
Signal: Signal processing tools
Sparse: Sparse matrices and related algorithms.
Spacial: Algorithms like spatial structures like K-D trees, nearest neighbors, convex hulls, etc.
Special: Special functions
Stats: Statistical functions
Weave: Tools to program in C/C++ as Python multiline strings; it is now deprecated for Cython.
Data Structures:
A multi-dimensional array is the fundamental data structure used in SciPy. It is provided by the NumPy module. It offers a few functions for linear algebra, Fourier transform, and random number generation. However, it is not the same with the generality of equivalent functions in SciPy. NumPy is additionally used as an efficient multi-dimensional container of the data with arbitrary data types. This way, it allows NumPy to boundlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type. This is now deprecated in favor of the newer NumPy array code.
The SciPy course offered by Great Learning will take you through a specific Python library that helps the developers to work with scientific and technical problems or applications. You will also be able to analyze the different modules offered by SciPy and know the difference between NumPy and the subject. At the end of the SciPy tutorial, you will be able to work in fledge with the SciPy library. The course is designed to help both working professionals and students work with Python and its projects. You will secure a certificate after the successful completion of the program. Happy Learning!
SciPy is the Python library that is used to solve scientific, mathematical, and technical problems. It is built on the NumPy extension, allowing the users to manipulate and visualize data with a massive variety of high-level commands.
SciPy is Scientific Python, a free, open-source Python library that is built on the NumPy extension. It stands for Numerical Python. It is used to manipulate the elements of numerical array data. It is a user-friendly environment that provides extended functionality to work with Python.
SciPy stands for Scientific Python. It is a free and open-source library of Python. It is specifically used to work with scientific and technical problems.
SciPy contains modules for operations like optimization, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks that are common in science and engineering.
SciPy can be learned for free online. Great Learning brings to you an opportunity to learn SciPy for free and also offers you a certificate after the successful completion of the course.
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Advance your career with accredited online programs from world-class universities
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