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Deep Learning Prerequisites

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:

  • deep learning

  • machine learning

  • data science

  • statistics

In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

What's that you say? Moore's Law is not linear?

You are correct. I will show you how linear regression can still be applied.

In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.

We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.

Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • calculus (taking derivatives)

  • matrix arithmetic

  • probability

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

  • Numpy coding: matrix and vector operations, loading a CSV file


  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

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Rating 4.4 based on 551 ratings
Length 6.5 total hours
Starts On Demand (Start anytime)
Cost $12
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Academics Data & Analytics Math & Science

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What people are saying

machine learning

Just started machine learning and data science.

Please do detailed courses on mathematics for machine learning.

This guy is simply one of the best Machine Learning instructors I have ever met.

This subject of machine learning is super-hard to learn but like he says you have to spend time trying to solve things you don't understand or you won't truly learn it.

very well understood facts about the theoretical side of Machine Learning are provided also the coding style of the lecturer is very understandable..

Very good for beginners who want to know about machine learning and develop tools in python.

I've already bought all of the other courses in this machine learning and deep learning series.

However - unlike other courses I have tried - these actually explain the theoretical concepts that underpin machine learning rather than just walking through some 'cool' superficial implementations.

Another great course overall, I'm looking forward to working through the rest of your machine learning stack here on Udemy.

Great course, especially for beginners who are struggling to get started with Machine Learning.

Anyone can plug some values in an API and get the results and call them Machine Learning Experts but few understand the Math behind it.

There are also little tidbits of life lessons that can be applied outside of a linear regression/machine learning context.

Secondly, I have my Ph.D. in Statistics and I am using the course to get a different perspective on the material since statistics and machine learning are constantly evolving and it is always nice to see someone else's approach to the subject matter.

So besides learning Machine Learning and Neural Networks I am catching up on developments that have occured over the past several year.

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lazy programmer

THANK YOUUU lazy Programmer!!!!

Amazing course from the Lazy programmer.

Before the Lazy Programmer courses, I did not have this confidence.

This allows you to do statistical Bayesian tests (also a course by Lazy Programmer) to truly understand the data.

Lazy programmer build up your fundamental knowledge of deep learning AI.

Editing my review: Lazy Programmer is far from any notion of laziness.

I am thankful to the lazy programmer for teaching me the basics I need.

Great Course And a great amount of mathematical background has been implemented in Python which is very brave as most courses shows calling the APIS and predicting data.For starting on ML journey it is mandatory to learn this mathematics and Lazy programmer has done a great job in implementing the basic mathematics for Linear regression by deriving the formula and than using the formula in his code .Great Work keep it up looking forward to enjoy the Logistic regression course.

I highly recommend all Lazy Programmer's courses, all of them are explained in depth, focused, clear and always in a challenging way.

I really like mindset behind the course (explained in the appendix videos, which is why I chose Lazy Programmer's Deep Learning courses ), but in some points it feels like as an excuse not to provide those few clarifying and enlightening 'why' explanations to really understand the matter that would dig deeper than just solving the equations with calculus.

I'm signing up for the rest of Lazy Programmer's courses immediately.

In this series by Lazy Programmer, only Numpy is used and entire algorithms are coded from Scratch, leveraging the Mathematics behind it - a good demo of the real world scenarios.

Lazy Programmer is on a whole other level when it comes to learning about Data Science, Machine Learning, and the latest algorithmic developments in Artificial Intelligence.

I would have to say that I usually don't check out the appendix of courses, but I decided to view the course appendix on top of the brilliant way that Lazy Programmer has interpolated the various perspectives of academic theory, practicality and good coding practices.

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deep learning

Previously, I thought I have known this topic very well because learnt in uni before, but combining coding in Python and in relation of building up to deep learning, it looks new and useful to me.

Good course for Deep learning from scratch.

This seems to be Udemy's only course series on machine/deep learning which goes this far in depth with the theory.

Can't wait to follow along the deep learning track for NLP.

After subscribing & going through too many many courses on Machine & Deep Learning, I finally found the right course for me.

thank you very much for your great effort to put up those great deep learning courses.

Great course in my journey to really understand and apply Deep Learning.

I am completely satisfied and going to continue studying Machine/Deep Learning with Lazy Programmer!

If you know basic calculus and linear algebra, this is the best place to start your Machine learning and Deep learning journey.

Detailed, well-explained lectures on linear regression and deep learning.

I started with another Course of Lazy Programmer for Deep Learning and took his advice to refreshed my understanding of the basics.

Its reassuring to learn from someone with deep knowledge of deep learning, so we know that what we learn about linear regression in this course will be useful in later deep learning courses.

A modern take on linear regression and the useful parts for deep learning.

The title of the course "Deep Learning Prerequisites: Linear Regression in Python" is a misnomer.

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data science

Este curso es excelente, pues a diferencia de otros cursos de Data Science te enseña las matemáticas detrás de los conceptos y esto se traduce en un aprendizaje significativo.

I used to consider myself good at data science because I could use the fit() predict() methods in sklearn.

It is hard to find courses online, let alone Udemy courses which cover the underlying theory and mathematics in Data Science, but also cover coding implementation.

This course is a great first course for data science.

The constant questions are also really helpful with consolidating concepts The instructor sounds practical and clearly mentions what is needed in order to learn from the contents of the course and to succeed in completing the course This course is suitable for building a data science foundation Very nice introduction.

A little short but useful for people already familiar with linear regression but not from a data science background.

The theory part is easy, so you can put forth your effort into writing code and getting acquainted with data science concepts.

This course is great and a must-see if you are new to data science.

simple examples illustrate how you apply the teachings in real situations this course introduces a simple machine learning topic explained using principles that are useful for machine learning and data science, putting a new spin on this subject.

Perfect for a data science beginner Instructor is uncovering the crux of the topics in a concise manner.

Great for programmers who want to learn how to add data science to their toolbox i found this course very engaging and enjoyed working through the code examples should make it clear that this is not suitable for people with out a degree in maths, I bought this thinking it was the right thing to do after hearing the disclaimer in the deep learning course.

i like the data science twist on an old statistics topic Every step is clearly expressed in a logical manor.

i would definitely recommend this course for anyone interested in data science but don't know any data science algorithms yet None.

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highly recommend

I highly recommend it to anyone interested in machine learning.

If you are new to deep learning I can highly recommend this course as the first one in the instructor's deep learning series.

Highly recommended.

Logical teacher A+ Highly Recommended Fantastic course, it's really helping a lot in terms of understanding the concepts you'll encounter in the deep learning courses that other course providers seem to just skim over.

Highly recommended!

Highly Recommended!!!

Overall the course is excellent and I highly recommend it as a great start.

Highly recommendable Lazy Programmer is the MCU of getting your feet wet in Machine Learning.

Highly recommend as starting point for both theory and coding.

Highly recommended for anyone interested in seriously doing ML beyond API calls.

I highly recommend this course for anyone willing to put in the time to really follow along, and who wants to understand both the mathematics and implementation of linear regression.

I would highly recommend this instructor and the course to anyone looking to get a start into data science and machine learning.

highly recommended Excellent work Good explanantion helpful course Materials seem interesting Too slow and redundant Awesome instructor!

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easy to follow

All explanations and code are very easy to follow.

The course content is fairly good and easy to follow, but the instructor's arrogant attitude makes this course more like a self-righteous talk show rather than a class.

Contains some advanced mathematics which is easy to follow and practical coding exercises for every part.

Well presented and easy to follow.

Easy to follow and what I need.

Easy to follow and really nicely taught.

The theory is well explained and easy to follow.

The code examples are very helpful and just as easy to follow in the videos.

Instructor speaks clearly and the demonstrations are easy to follow, presented in a way anyone can understand.

It was very easy to follow, since I already have a statistics background.

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gradient descent

really learned how to use gradient descent in linear regression it was new idea to me also the use of categorical data also very helpful small examples and explanation of theory !

There are few areas in practical machine learning issues section like gradient descent and probabilistic interpretation section which are not clearly explained.

The lesson on gradient descent is slightly worse, but that is not the main point of this course.

The gradient descent videos did not flow particularly well.

We also saw how those concepts apply to LR, theory is never shallow and not a drag the author is successful in reaching a sweet spot; Particularly regarding the use of the gradient descent which is going to become an important tool.

This theory is good and practical, but the was only one exercise - gradient descent - before the very final lecture.

This course provides an excellent introduction to deep learning basic ideas like gradient descent, overfitting, and regularization.

The instructor could've also added a section about obtaining the weights using gradient descent for example.

Could have used some more in-depth details on gradient descent, bias & variance.

Excellent intro to different algorithms, especially Gradient Descent.

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step by step

Also the Lazy guy is giving the right importance to the fact that people can follow step by step in the coding as well.

It provides a step by step visualization of the process of getting to the answer and is something people can emulate in real-world applications.

He goes through each of the methods step by step and makes sure you understand why each step is happening.

Good step by step increase in complexity.

Good explanations of the math instead of other courses that give you the formulas without telling you where they came from Pretty much covers everything in great detail Lucid explanations - step by step A lot of it felt like recitation from a textbook.

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far so good

so far so good It's a shame that the class sometimes requires writing code that is unexplained.

So far so good.

Thank you clear presentation Didn't explain anything So far so good.

Excellent Yes, so far so good.

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linear regression before

I've studied linear regression before and took this course as a refresher and it left me scratching my head.

Even though a fan of linear regression before this course as a foundational tool to any regression study, this course made me see linear regression as even more important to any study.

i knew a little about linear regression before starting this course from uni, but this puts a new spin on an old concept and gets you excited for other machine learning algos Lazy programmer rips apart your laziness and brings out the best in u Awesome course indeed :D The concept explain very clear.

would be good to add subtitle good course Clear content and detailed explanation I already had some exposure to linear regression before starting this course, and I was glad to see it done in numpy.

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rather than

Talking through part of the matrix identities when differentiating matrices would be a huge help rather than leaving that important detail out.

This is a course for actual learning in Deep learning rather than just several codes with some powerful library.

Also explains concepts in a simple and straightforward way The instructor is going deep into the topic and showing the math rather than just glossing over things.

Too many slides spent on what not do rather than what to do.

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l2 regularization

I'm glad that probabilistic explanation of L1, L2 regularization methods was given, but it took me a bit of time to understand what is going on there.

Still do not feel completely confident on the Dummy Variable Trap and L1 and L2 regularization.

Some topics like MAP and difference between L1 and L2 regularization is presented brilliantly.

However, after I completed the L2 regularization section, I revisited the MLE section and realized the need for the MLE approach.

The L1 and L2 regularization are difficult to grasp.

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Rating 4.4 based on 551 ratings
Length 6.5 total hours
Starts On Demand (Start anytime)
Cost $12
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Academics Data & Analytics Math & Science

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