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  1. Luis Serrano, Instructor | Coursera. Bio. AI scientist, popular YouTuber, and author of Grokking Machine Learning. I am a quantum AI research scientist at Zapata Computing in Toronto, Canada, developing machine learning algorithms to work in quantum computers.

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      Luis Serrano, Instructor | Coursera. Bio. AI scientist,...

  2. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools.

    • $37.79
  3. serrano.academysearch

    Hi! I'm Luis Serrano. In this page, you will find educational material in machine learning and mathematics. Most of it comes from my YouTube channel, which I encourage you to subscribe to, and my book Grokking Machine Learning.

  4. 14 de dic. de 2021 · Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools.

    • (28)
    • Manning Publications
    • $46.54
    • Luis Serrano
  5. 14 de dic. de 2021 · Grokking Machine Learning. Luis Serrano. Simon and Schuster, Dec 14, 2021 - Computers - 350 pages. Discover valuable machine learning techniques you can understand and apply using just...

    • Luis Serrano
    • Simon and Schuster, 2021
    • illustrated
    • Grokking Machine Learning
  6. 22 de dic. de 2018 · For a code implementation, check out this repo:https://github.com/luisguiserrano/manning/tree/master/Chapter_3_Linear_RegressionAnnouncement: New Book by Lui...

    • 31 min
    • 37.8K
    • Serrano.Academy
  7. What is machine learning? Types of machine learning. Drawing a line close to our points: Linear regression (code) Optimizing the training process: Underfitting, overfitting, testing, and regularization (code) Using lines to split our points: The perceptron algorithm (code) A continuous approach to splitting points: Logistic classifiers (code)