Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence

## Requirements

- Linear Algebra, Probability, and Python Programming

## Description

Welcome! This is **Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2)**.

The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.

As I’ve always said: “If you can’t implement it, then you don’t understand it”.

Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.

This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

The goal is that, **after **you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Suggested Prerequisites:

- linear algebra
- probability
- Python programming

## Who this course is for:

- Anyone who wants to implement Machine Learning algorithms

## Course content

6 sections • 27 lectures • 1h 59m total length