PLU | How Machine Learning Works (2019 EN)

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  1. Kanka

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    Company: Pluralsight
    Author: Paolo Perrotta
    Full Title: How Machine Learning Works
    Year: 2019
    Language: English
    Genre: Educational: Machine Learning
    Skill Level: Beginner
    Price: -
    Files: MP4 (+ Exercise Files, Subtitles .SRT, Slides .PDF)
    Time: 02:22:43
    Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 200 kbps
    Audio: AAC at 96 Kbps, 2 channels, 48.0 KHz

    Machine learning is amazing… and intimidating. How can computers do magical things like understand images or text? This training for programmers will dispel the magic and help you to build your own computer vision program, starting from scratch.

    Machine learning is fascinating for programmers, but at the same time it can be intimidating because of all those math-heavy tutorials. In this course, How Machine Learning Works, you’ll learn the basics of machine learning from code. First, you will have a look at supervised learning, and you'll quickly move to coding your first learning program. Then, you'll discover how to improve that program line by line. Finally, you'll be writing the code all by yourself without resorting to obscure machine learning libraries. By the end of the course, you'll have a working computer vision program that recognizes handwritten characters, and you'll have practical knowledge of the foundational ideas of machine learning.

    1. Course Overview
    01. Course Overview
    2. Introduction
    02. Getting to Know Machine Learning
    03. Programming vs. Machine Learning
    04. Supervised Learning
    05. Approximating a Function
    3. Building Your First Machine Learning Program
    06. Tackling a Machine Learning Problem
    07. Making Sense of Our Data
    08. Understanding Linear Regression
    09. Implementing Prediction
    10. Understanding the Loss
    11. Implementing the Training Algorithm
    12. Running the Code
    4. Improving the Algorithm with Gradient Descent
    13. Our Algorithm Doesn't Cut It
    14. Understanding Gradient Descent
    15. Descending the Gradient in Three Dimensions
    16. Calculating the Gradient
    17. Implementing Gradient Descent
    5. Expanding Regression to Multiple Variables
    18. Dealing with a Complicated World
    19. Tricking Away the Bias
    20. Switching to Matrices
    21. Shaping Data
    22. Upgrading the Loss
    23. Running Multiple Regression
    6. Predicting Discrete Outcomes
    24. Getting to Know Classification
    25. From Regression to Classification
    26. Introducing the Log Loss
    27. Testing the Classifier
    7. Recognizing Individual Digits
    28. Introducing MNIST
    29. Understanding the Test Set
    30. Preparing the Images
    31. Preparing the Labels
    32. Recognizing a Digit
    8. Figuring Out Image Recognition
    33. Planning for Multiple Classes
    34. Encoding the Labels
    35. Updating the Weights
    36. Updating Prediction
    37. Running the Final Test
    9. Seeing the Big Picture
    38. From the Basics to the Perceptron
    39. Understanding the Perceptron's Limitations
    40. The Story So Far