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    3. Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Posted By: ParRus
    Date: 15 Dec 2020 14:45:30
    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL
    WEBRip | English | MP4 | 1280 x 720 | AVC ~5096 kbps | 30 fps
    AAC | 317 Kbps | 48.0 KHz | 2 channels | 06:32:47 | 14.4 GB
    Genre: eLearning Video / Development, Programming, Machine Learning, Algebra

    Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills? Then, this course is for you. With the growing learners of Machine Learning, Data Science, and Deep Learning.
    The Common mistake by a data scientist is→ Applying the tools without the intuition of how it works and behaves.

    Having the solid foundation of mathematics will help you to understand how each algorithm work, its limitations and its underlying assumptions.

    With this, you will have an edge over your peers and makes you more confident in all the applications of Machine Learning, Data Science, and Deep Learning.

    As a common saying:

    It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car.

    Linear Algebra is one of the areas where everyone agrees to be a starting point in the learning curve of Machine Learning, Data Science, and Deep Learning.. Its basic elements – Vectors and Matrices are where we store our data for input as well as output.

    Any operation or Processing involving storing and processing the huge number of data in Machine Learning, Data Science, and Artificial intelligence, would mostly use Linear Algebra in the backend.

    Even Deep Learning and Neural Networks – Employs the Matrices to store the inputs like image, text etc. to give the state of the art solution to complex problems.

    Keeping in mind the significance of Linear Algebra in a Data Science career, we have tailor-made this curriculum such that you will be able to build a strong intuition on the concepts in Linear Algebra without being lost inside the complex mathematics.

    At the end of this course, you will also learn, how the Famous Google PageRank Algorithm works, using the concepts of Linear Algebra which we will be learning in this course.

    In this course. you will not only learn analytically, but you will also see its working by running in Python as well.

    So, with this course, you will learn, build intuition, and apply to some of the interesting real-world applications.

    Click on the Enroll Button to start Learning.

    I look forward to seeing you in Lecture 1

    Who this course is for:
    Data Scientists who wish to improve their career in Data Science.
    Machine Learning Practitioners
    Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence
    Any Data Science enthusiast
    Any student or professional who wants to start or transition to a career in Data Science.
    Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science.
    Any data analysts who want to level up in Machine Learning.
    Any people who are not satisfied with their job and who want to become a Data Scientist.
    Requirements
    Pen and a paper to workout maths problem
    Computer with Python to execute the code
    Some programming experience

    also You can find my other helpful Programming-posts
    (if old file-links don't show activity, try copy-paste them to the address bar)

    General
    Complete name : 52-2.8 Exercises.mp4
    Format : MPEG-4
    Format profile : Base Media / Version 2
    Codec ID : mp42 (mp42/mp41)
    File size : 85.3 MiB
    Duration : 2 min 11 s
    Overall bit rate mode : Variable
    Overall bit rate : 5 420 kb/s
    Encoded date : UTC 2020-10-29 20:20:30
    Tagged date : UTC 2020-10-29 20:21:02
    TIM : 00:00:00:00
    TSC : 30
    TSZ : 1

    Video
    ID : 1
    Format : AVC
    Format/Info : Advanced Video Codec
    Format profile : Main@L4.1
    Format settings : CABAC / 2 Ref Frames
    Format settings, CABAC : Yes
    Format settings, RefFrames : 2 frames
    Format settings, GOP : M=2, N=15
    Codec ID : avc1
    Codec ID/Info : Advanced Video Coding
    Duration : 2 min 11 s
    Bit rate mode : Variable
    Bit rate : 5 096 kb/s
    Maximum bit rate : 768 kb/s
    Width : 1 280 pixels
    Height : 720 pixels
    Display aspect ratio : 16:9
    Frame rate mode : Constant
    Frame rate : 30.000 FPS
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    Chroma subsampling : 4:2:0
    Bit depth : 8 bits
    Scan type : Progressive
    Bits/(Pixel*Frame) : 0.184
    Stream size : 80.2 MiB (94%)
    Language : English
    Encoded date : UTC 2020-10-29 20:20:30
    Tagged date : UTC 2020-10-29 20:20:30
    Color range : Limited
    Color primaries : BT.709
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    Matrix coefficients : BT.709

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    ID : 2
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    Format/Info : Advanced Audio Codec
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    Codec ID : mp4a-40-2
    Duration : 2 min 11 s
    Source duration : 2 min 12 s
    Bit rate mode : Constant
    Bit rate : 317 kb/s
    Channel(s) : 2 channels
    Channel positions : Front: L R
    Sampling rate : 48.0 kHz
    Frame rate : 46.875 FPS (1024 SPF)
    Compression mode : Lossy
    Stream size : 4.99 MiB (6%)
    Source stream size : 4.99 MiB (6%)
    Language : English
    Encoded date : UTC 2020-10-29 20:20:30
    Tagged date : UTC 2020-10-29 20:20:30
    Screenshots

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

    Master Linear Algebra for Data Science & Machine Learning DL (2020)

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    Master Linear Algebra for Data Science & Machine Learning DL (2020)
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