Deep Learning Prerequisites: The Numpy Stack in Python (V2)

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

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Course Data

Lectures: 40
Length: 4h 26m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

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.

Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.

Pandas makes working with datasets a lot like R, if you’re familiar with R.

The central object in R and Pandas is the DataFrame.

We’ll look at how much easier it is to load a dataset using Pandas vs. trying to do it manually.

Then we’ll look at some dataframe operations, like filtering by column, filtering by row, the apply function, and joins, which look a lot like SQL joins.

So if you have an SQL background and you like working with tables then Pandas will be a great next thing to learn about.

Since Pandas teaches us how to load data, the next step will be looking at the data. For that we will use Matplotlib.

In this section we’ll go over some common plots, namely the line chart, scatter plot, and histogram.

We’ll also look at how to show images using Matplotlib.

99% of the time, you’ll be using some form of the above plots.


I like to think of Scipy as an addon library to Numpy.

Whereas Numpy provides basic building blocks, like vectors, matrices, and operations on them, Scipy uses those general building blocks to do specific things.

For example, Scipy can do many common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

It has signal processing tools so it can do things like convolution and the Fourier transform.

It can also do optimization, a very important thing in machine learning!

In sum:

If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.

If you know some basic coding, but you want to learn how to visualize data and make plots, create dataframes from data files and manipulate dataframes, and do scientific calculations like statistical testing, then this course is for you.

If you've taken one of my more advanced courses, but found that you didn't understand a lot of the code, then this course is for you.

Suggested Prerequisites:

  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets

Tips for success:

  • Use the video speed changer! Personally, I like to watch at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".


Welcome and Logistics

2 Lectures · 11min


10 Lectures · 01hr 12min
  1. Numpy Section Introduction (05:28)
  2. Arrays vs Lists (12:40)
  3. Dot Product (07:02)
  4. Speed Test (02:55)
  5. Matrices (14:45)
  6. Solving Linear Systems (03:38)
  7. Generating Data (14:32)
  8. Numpy Exercise (01:05)
  9. Where to Learn More Numpy (06:55)
  10. Suggestion Box (03:10)


7 Lectures · 35min
  1. Matplotlib Section Introduction (02:39)
  2. Line Chart (03:50)
  3. Scatterplot (04:31)
  4. Histogram (02:26)
  5. Plotting Images (07:40)
  6. Matplotlib Exercise (01:40)
  7. Where to Learn More Matplotlib (13:10)


7 Lectures · 26min
  1. Pandas Section Introduction (01:17)
  2. Loading in Data (03:52)
  3. Selecting Rows and Columns (09:48)
  4. The apply() Function (02:32)
  5. Plotting with Pandas (02:46)
  6. Pandas Exercise (02:10)
  7. Where to Learn More Pandas (04:25)


5 Lectures · 17min
  1. Scipy Section Introduction (01:25)
  2. PDF and CDF (03:07)
  3. Convolution (04:34)
  4. Scipy Exercise (01:03)
  5. Where to Learn More Scipy (07:47)


9 Lectures · 01hr 41min
  1. What is the Appendix? (02:48)
  2. Should you code along? (11:00)
  3. Windows-Focused Environment Setup 2018 (20:21)
  4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
  5. Proof that using Jupyter Notebook is the same as not using it (12:29)
  6. What order should I take your courses in? (part 1) (11:19)
  7. What order should I take your courses in? (part 2) (16:07)
  8. Python 2 vs Python 3 (04:38)
  9. Where to get discount coupons and FREE deep learning material (05:31)
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