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MACHINE LEARNING prerequisite - Python | Numpy | Mathematics

MACHINE LEARNING prerequisite - Python | Numpy | Mathematics

MACHINE LEARNING prerequisite - Python | Numpy | Mathematics Learn, in less than a day, the essential concepts of Mathematics, Python and Numpy for Machine Learning

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What you'll learn

  • Essential Mathematics Concepts for Machine Learning
  • A Linear Algebra crash course
  • An advanced level of mastery of lists in Python (Slicing / List-Comprehension / Multi-Level Indexing)
  • Complete tour of the essential functions of Numpy

Requirements

  • Un niveau basique en Python (les fondamentaux de Python ne sont pas couverts dans ce cours)
  • Être motivé·es ;)

Description

The goal of this course is simple: to teach in less than a day all the prerequisites to get started smoothly in machine learning.

On the menu, three pillars: Python, Numpy and Mathematics.

Python specifically for ML and DL

• The 4 awesome must-have list functions to save time.

• List-Slicing in Python will have no secrets for you (explanations + Mnemotechnical means + exercises).

• Advanced List-Slicing (on the menu: Step-Slicing, Reverse Slicing, Negative Step Slicing and Slice Insert + Slice Delete).

• List-Comprehension in Python (step-by-step explanations for mastering this ultra-practical concept for life).

• Multi-Level Indexing (my shortcuts to find your way around in several dimensions).

• Matrix Multiplication with lists (really important to explain it in simple Python code in order to anchor the concept and how it works)

Numpy (the machine that runs Scikit-Learn, Pandas and Matplolib and inspired Tensorflow (from Google) and Pytorch (from Facebook)

• Linear Algebra with Numpy, why Numpy is faster than pure Python (+ the lab that will prove it to you by comparing the speed performance of Numpy and Python)

• Broadcasting & Element Wise Operations & Tips to optimize performance (understand: why Numpy is essential for ML in Python).

• 50 shades of Numpy Advanced Slicing (do whatever you want with your matrices and tensors so you never have to go to Stack Overflow again to do what you want).

• The mysteries of Tensor numpy.Sum and the Axis & Keepdims arguments finally explained clearly (and the mnemonic to never forget which axis 0 and axis 1 are).

• Numpy Reshape Ninja (master the reshape method and the famous -1 which block so many beginners).

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