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    NumPy for Data Science and Machine Learning in Python

    Posted By: ELK1nG
    NumPy for Data Science and Machine Learning in Python

    NumPy for Data Science and Machine Learning in Python
    Published 04/2022
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
    Genre: eLearning | Language: English + srt | Duration: 12 lectures (1h) | Size: 293.7 MB

    Deep Learning Prerequisites: The Numpy Stack in Python

    What you'll learn
    Basics of Numpy, Arrays, Lists.
    Accessing/Changing Specific Elements, Rows, Columns, etc
    Initializing Different Arrays (1s, 0s, full, random, etc)
    Basic Mathematics (arithmetic, trigonometry, etc.)
    Linear Algebra and Statistics
    Reorganizing Arrays
    Load data in from a file
    Advanced Indexing and Boolean Masking

    Requirements
    Basics of Python programming languages
    Basic knowledge of mathematics like Matrix, algebra etc.

    Description
    Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.

    One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

    Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.

    This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

    So what are those things?

    Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.

    The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.

    That means you can do vector and matrix operations like addition, subtraction, and multiplication.

    The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

    Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

    Who this course is for
    Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
    Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code