# Data Science Prerequisites: The Numpy Stack in Python (V2+)

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### Course Data

Lectures: 86
Length: 9h 35m
Skill Level: All Levels
Languages: English

### 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.

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.

SciKit-Learn

SciKit-Learn is an awesome library that brings the most powerful AI models to you with absolutely ZERO work to do on your part. You can use state of the art models with just 2-3 lines of code, and I'm going to show you how.

In this section, which is a bonus addition I created a few years after I began the course, I also go over some machine learning and deep learning basics.

What is machine learning in the first place?

We talk about classification, regression, how to feed in ANY kind of dataset into your machine learning model, and a few important rules that, if followed, will make using and implementing machine learning algorithms an absolute breeze.

This section is what I call the "black sheep" of this course because it's the only section that contains new concepts. The previous sections are just about taking some mathematical concepts you already know (or should know if you follow the prerequisites), and teaching you the "computer commands" you need to accomplish them.

This new section is a 1-2 punch because it not only teaches you the SKLearn API, but also some very important concepts behind machine learning and artificial intelligence.

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".

## Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

### Lectures

#### Welcome and Logistics

2 Lectures · 11min

#### Numpy

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)
10. Suggestion Box (03:10)

#### Matplotlib

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)

#### Pandas

7 Lectures · 26min
1. Pandas Section Introduction (01:17)
3. Selecting Rows and Columns (09:48)
4. The apply() Function (02:32)
5. Plotting with Pandas (02:46)
6. Pandas Exercise (02:10)

#### Scipy

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

#### Numpy Stack Exercises

1 Lectures · 08min
1. Exercises (08:56)

#### Beginner Troubleshooting

1 Lectures · 13min
1. What if I don't meet the math prerequisites? (13:56)

#### Machine Learning Basics (Bonus Introduction)

1 Lectures · 42min
1. What is Machine Learning? (Scikit-Learn Tutorial) (42:14)

#### Machine Learning Basics

11 Lectures · 01hr 34min
1. Machine Learning: Section Introduction (07:47)
2. What is Classification? (12:22)
3. Classification in Code (14:39)
4. What is Regression? (12:14)
5. Regression in Code (08:30)
6. What is a Feature Vector (06:49)
7. Machine Learning is Nothing but Geometry (04:50)
8. All Data is the Same (05:23)
9. Comparing Different Machine Learning Models (09:47)
10. Machine Learning and Deep Learning: Future Topics (05:55)
11. Machine Learning: Section Summary (05:47)

#### More Machine Learning

2 Lectures · 21min
1. When should I use Support Vector Machines? (11:16)
2. What Level of Probability for Machine Learning? (10:30)

#### How to Master Different Subfields in Machine Learning

1 Lectures · 15min
1. What is Computer Vision? Guide to learning path for how to master Computer Vision with Deep Learning (15:05)

#### Introduction and Outline (Legacy)

3 Lectures · 11min
1. What's this course about? How can you succeed? What should you know first? (06:08)
2. Where to get the code (and how to install libraries) (03:50)
3. Python 2 or Python 3? (01:09)

#### Numpy (Legacy)

9 Lectures · 34min
1. Lists vs. Arrays (06:23)
2. Dot product 1: For loop vs. cosine method vs. dot function (05:13)
3. Dot product 2: Speed comparison (01:38)
4. Vectors and Matrices (04:00)
5. Generating Matrices to Work With (03:27)
6. Matrix Products (02:39)
7. More Matrix Operations (06:55)
8. Solving a Linear System (02:29)
9. Word Problem (01:48)

#### Pandas (Legacy)

7 Lectures · 28min
2. DataFrames (03:22)
3. More about DataFrames: Selecting Rows and Columns (04:34)
4. Even More about DataFrames: Column Names (03:22)
5. The apply() Function (03:14)
6. Joins (02:33)
7. .values vs. .as_matrix() (07:10)

#### Matplotlib (Legacy)

4 Lectures · 14min
1. Line Chart (03:20)
2. Scatterplot (03:07)
3. Histogram (03:04)
4. Plotting Images (05:19)

#### Scipy (Legacy)

5 Lectures · 18min
1. Gaussian PDF and CDF (05:01)
2. Sampling from a Gaussian Distribution (1-D) (01:47)
3. Sampling from a Gaussian Distribution (Spherical and Axis-aligned Elliptical) (03:09)
4. Sampling from a General Multivariate Normal (03:03)
5. Other Interesting Scipy Functions (05:55)

#### Appendix

10 Lectures · 01hr 46min
1. What is the Appendix? (02:48)
2. Should you code along? (11:00)
3. Pre-Installation Check (04:13)
4. Anaconda Environment Setup (20:21)
5. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
6. Proof that using Jupyter Notebook is the same as not using it (12:29)
7. What order should I take your courses in? (part 1) (11:19)
8. What order should I take your courses in? (part 2) (16:07)
9. Python 2 vs Python 3 (04:38)
10. Where to get discount coupons and FREE deep learning material (05:49)
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