Deep Learning Prerequisites: Linear Regression in Python (Udemy.com)

Data science, machine learning, and artificial intelligence in Python for students and professionals

( 303 Reviews )

Created by: Lazy Programmer Inc.

Produced in 2021

What you will learn

Derive and solve a linear regression model, and apply it appropriately to data science problems
Program your own version of a linear regression model in Python

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 90 / 100

Course Description

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 learningmachine learningdata sciencestatisticsIn 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 wantmorethan 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 scratchOther 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 arithmeticprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileWHATORDERSHOULDITAKEYOURCOURSESIN?:
Check out the lecture "Machine Learning and AIPrerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)Who this course is for:
People who are interested in data science, machine learning, statistics and artificial intelligencePeople new to data science who would like an easy introduction to the topicPeople who wish to advance their career by getting into one of technology's trending fields, data scienceSelf-taught programmers who want to improve their computer science theoretical skillsAnalytics experts who want to learn the theoretical basis behind one of statistics' most-used algorithms

Time: 6.5h

Instructor Details

Lazy Programmer Inc.

4.5 Rating
303 Reviews

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.
Experience includesonline advertising and digital media as both a data scientist (optimizing click and conversion rates)and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop,Pig, Hive,MapReduce, and Spark.
I've created deeplearning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and wevalidated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postg

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Reviews

4.5

303 total reviews

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By Almir Bravin on 9/24/2020

Lazy has a very profound knowledge about the subject is easy to see. Didactic on other hand is not his priority.

By Bakhodir Ataniyazov on 9/21/2020

I think you can do better.

By Andres Lopez Candanedo on 9/18/2020

This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.

By Chien-Hsiang Yeh on 9/11/2020

Unlike other courses, the theory is well explained. However, a more mathematical explanation in maximum likelihood, map and regularization would be more helpful.

By Horacio Saggion on 9/3/2020

Lot of repetition and sometimes awkward sequence of topics.

By Vamsikrishna J on 8/21/2020

Except the probabilistic part , everything does Good!

By Bartomiej Growski on 8/12/2020

Best of the best

By Stephanie Forbes on 8/9/2020

In the beginning lecture, the course seems fairly homegrown. Information is good, but there is a lot of background noise in the videos making them seem less than professional. Later lectures are better as far as less-to-no background noise, but you can tell which lectures were done originally and which have been re-recorded by the volume/background noise.
The information is good and what I was looking for - in-depth information on how linear regression works at the mathematical level. I had already taken a few machine learning classes before this one.
I would suggest starting with some of the lectures in the appendix before jumping in to the course material and wish I had known to do that. Some of the "Extra Help" sections would have been more useful to me up-front.

By William Jackson on 8/4/2020

prediction of stock movement

By Pavithra K.S. on 7/27/2020

This course really deserves 5 stars. This is the only course which explained the math behind each and every concept in the regression so far. I really enjoyed the course very much. If anybody wants to start your machine learning , this is the best course to start with.
Thank you Lazy programmer

By Kshitij Yerande on 7/17/2020