NumPy for Data Science and Machine Learning in Python

Deep Learning Prerequisites: The Numpy Stack in Python

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Last Updated: 2023-03-20 06:03:28 English Course Type: Self Learning

Basics of Numpy, Arrays, Lists.
Accessing/Changing Specific Elements, Rows, Columns, etc
Initializing Different Arrays (1s, 0s, full, random, etc)
Basic Mathematics (arithmetic, trigonometry, etc.)
Linear Algebra and Statistics
Reorganizing Arrays
Load data in from a file
Advanced Indexing and Boolean Masking

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

One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.

This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

So what are those things?

Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.

The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.

That means you can do vector and matrix operations like addition, subtraction, and multiplication.

The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Basics of Python programming languages
Basic knowledge of mathematics like Matrix, algebra etc.

Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Section 1 : Introduction 1 Lectures 00:03:42
Lecture 1 :
Introduction Preview
Section 2 : NumPy and It's Application 1 Lectures 00:03:42
Lecture 1 :
NumPy and It's Application
Section 3 : Basics of NumPy 2 Lectures 00:08:57
Lecture 1 :
Initializing and Array
Lecture 2 :
NumPy Datatypes
Section 4 : Accessing/Changing Specific Elements, Rows, Columns, etc 1 Lectures 00:11:06
Lecture 1 :
Accessing/Changing Specific Elements,
Section 5 : Initializing Different Arrays (1s, 0s, full , random, etc) 1 Lectures 00:09:55
Lecture 1 :
Initializing Different Arrays
Section 6 : Basic Mathematics (arithmetic, trigonometry, etc) 1 Lectures 00:04:49
Lecture 1 :
Basic Mathematics
Section 7 : Linear Algebra and Statistics 1 Lectures 00:07:55
Lecture 1 :
Linear Algebra and Statistics
Section 8 : Reorganizing Arrays 1 Lectures 00:05:00
Lecture 1 :
Reorganizing Arrays
Section 9 : Load data in form a file 1 Lectures 00:02:27
Lecture 1 :
Load data using NumPy
Section 10 : Advanced Indexing and Boolean Masking 1 Lectures 00:03:38
Lecture 1 :
Advanced Indexing and Boolean Masking

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