Deep Learning Prerequisites: The Numpy Stack in Python
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# Deep Learning Prerequisites: The Numpy Stack in Python

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence
4.5 (11,487 ratings)
148,514 students enrolled
Last updated 3/2019
English
English [Auto-generated]
Price: Free
This course includes
• 3.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
What you'll learn
• Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn

• ### Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms

• Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!
Requirements
• Understand linear algebra and the Gaussian distribution
• Be comfortable with coding in Python
• You should already know "why" things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for
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.

Scipy.

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.

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.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• linear algebra

• probability

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

• you should already know "why" things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

TIPS (for getting through the course):

• Watch it at 2x.

• Take handwritten notes. This will drastically increase your ability to retain the information. This has been proven by research!

• Ask lots of questions on the discussion board. The more the better!

• Realize that most exercises will take you days or weeks to complete.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses)

Who this course is for:
• 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
Course content
Expand all 37 lectures 03:28:09
+ Introduction and Outline
3 lectures 11:07
What’s this course about? How can you succeed? What should you know first?
06:08
Where to get the code (and how to install libraries)
03:50

The answer to this question should be trivial - we've discussed it already in the introduction!

How will you practice what you've learned in this course?
1 question
Python 2 or Python 3?
01:09
+ Numpy
9 lectures 34:37
Lists vs. Arrays
06:23
Dot product 1: For loop vs. cosine method vs. dot function
05:13
Dot product 2: Speed comparison
01:43
Vectors and Matrices
04:00
Generating Matrices to Work With
03:27
Matrix Products
02:39
More Matrix Operations
06:55
Solving a Linear System
02:29
Word Problem
01:48
+ Pandas
7 lectures 28:49
04:38
DataFrames
03:22
More about DataFrames: Selecting Rows and Columns
04:33
Even More about DataFrames: Column Names
03:21
The apply() Function
03:14
Joins
02:32
.values vs .as_matrix()
07:09
+ Matplotlib
4 lectures 14:47
Line Chart
03:19
Scatterplot
03:06
Histogram
03:03
Plotting Images
05:19
+ Scipy
5 lectures 18:51
Gaussian PDF and CDF
05:00
Sampling from a Gaussian Distribution (1-D)
01:46
Sampling from a Gaussian Distribution (Spherical and Axis-aligned Elliptical)
03:08
Sampling from a General Multivariate Normal
03:03
Other Interesting Scipy Functions
05:54
+ Appendix
9 lectures 01:39:58
Exercises
08:55
Bonus Section on Machine Learning Basics
06:19
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32
Python 2 vs Python 3
04:38
Proof that using Jupyter Notebook is the same as not using it
12:29
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
Where to get Udemy coupons and FREE deep learning material
02:20