Name: Python Numpy Data Analysis for Data Scientist | AI | ML | DL
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What You'll Learn

Understand the basics of Numpy and how to set up the Numpy environment.
Create and access arrays, use indexing and slicing, and work with arrays of different dimensions.
Understand the ndarray object, data types, and conversion between data types.
Work with array attributes and different ways of creating arrays from existing data or ranges functions.
Apply broadcasting, iteration, and updating array values.
Perform array manipulation, joining, transposing, and splitting operations.
Apply string, mathematical, and trigonometric functions.
Perform arithmetic operations, including add, subtract, multiply, divide, floor_divide, power, mod, remainder, reciprocal, negative, and abs.
Apply statistical functions and counting functions.
Sort arrays using different methods, including sort(), argsort(), lexsort(), searchsorted(), partition(), and argpartition().
Understand the different types of array copies, including view, copy, "no copy", shallow copy, and deep copy.

Requirements

Data Scientists who need to analyze large data sets and want to use Python's powerful tools for this purpose.
AI and Machine Learning engineers who want to work with numerical data using Python and Numpy.
Deep Learning enthusiasts who want to understand the fundamentals of Numpy arrays and use it to manipulate and process image and audio data.
Researchers who want to use Python and Numpy for scientific computing and numerical analysis.
Programmers who want to learn a powerful and widely-used library for numerical computing with Python.
Students who are interested in pursuing a career in Data Science or related fields and want to learn the basics of Numpy for data analysis.

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