Learn about data science, machine learning, and AI using Python. Build logistic regression from scratch and understand deep learning through practical examples.
This course dives into data science and machine learning using Python. You'll get hands-on experience with logistic regression and learn how to tackle real-world problems through practical projects. Plus, you'll gain insights into AI tech like ChatGPT and DALL-E. Sounds fun, right?
Data science, machine learning, and artificial intelligence in Python for students and professionals
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic 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 logistic regression module in Python.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
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 use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.
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
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
program logistic regression from scratch in Python
describe how logistic regression is useful in data science
derive the error and update rule for logistic regression
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The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into...
A one stop solution to learn almost all the basics there are to be learnt in logistic regression and deep learning prerequisites. The exercises are the best way to practice as being stressed by the instructor in the course.
Because I am just starting the course and pretty long road to go for me to have a better understanding and knowing to rotate the course
Great course for every CS student who wants to dive deep into ML practise. The combination of both mathematical and practical approaches isnecessary to build up strong ML skills. I will kepp taking the next courses, 100% RECOMMENDED
If you are here for knowing how to use library then don't join the course the instructor says he teaches it for free... if you are here for knowing how the classes in library works, if you are here for deep knowledge which I think is very powerful.. well then welcome to the course... you have found the best instructor. I deeply respect him for teaching students this. Thanks a lot to the instructor and the team
Excellent
Amazing course! All the content is covered and explained in a simple and relatable manner. I completed this course thoroughly and finished all the exercises. I was able to use what I learned on the job.
The content is ok but the links between videos and sections aren't always obvious. I like the fact that it goes in dept on my subjects but the quality of the audio isn't always good enough. I would still recommend to someone that really want to have a better understanding of AI/ML or specifically logistic regression but expect some extra reading if you really want to understand all the concepts well.
Excellent course! Loved the course content, good math with excellent teaching style. I would recommend the course.
This instructor clearly loves pure math. It's a lot on the first pass, but it's exactly what's needed to become proficient.
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Stsee Y.
Excellent explanation