Fast Numerical Computing with Python [Video]

Fast Numerical Computing with Python [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 17m | 624 MB
eLearning | Skill level: All Levels

Fast Numerical Computing with Python [Video]: Get to grips with aspects of numerical computing and understand NumPy, the powerful numerical computing module, using Python!

Performing numerical computations using conventional Python methods is inefficient as complex calculations can take their toll on your system’s performance. However, NumPy, the core library for scientific computing in Python, helps keep computations fast and lets your system perform efficiently.

This course adopts a step-by-step approach where you learn through live examples. You will learn numerical computations by performing them! Gain the skills you need to become a better Python developer or data scientist. Beginning with NumPy’s arrays and functions, you’ll master linear algebra concepts, perform vector and matrix math operations, and use NumPy in Python (using quick and easy techniques) to derive numerical results faster and far more easily than with any other tool. You will understand and practice data processing and predictive modeling throughout the course.

• Develop a strong foundation for numerical computational problems
• Competently program in Python and code in Jupyter Notebooks
• Use the most significant numerical computing library available for Python: NumPy
• Gain high-level access to extremely efficient computational routines with NumPy
• Use Python for data science: to work with high-level mathematical functions and simplify data
• Implement multi-dimensional arrays and matrices with NumPy’s powerful data structures to enrich your programming experience
• Perform vector and matrix operations using NumPy and linear algebra
• Work on real-world datasets to develop a predictive model using simple and multiple linear regression techniques

By the end of this course, you will have developed a strong foundation in solving numerical computational problems with NumPy. You will also have a really good knowledge of Python and will be ready to advance your career as a Python developer or data scientist.