Deep Learning Prerequisites: Logistic Regression in Python

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

4.75 (4409 reviews)

Udemy

platform

English

language

Data Science

What you will learn

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

understand how logistic regression works as an analogy for the biological neuron

use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition

understand why regularization is used in machine learning

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Why take this course?

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)

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Our review

📚 **Overall Course Review:** **Pros:** - The course provides an in-depth understanding of logistic regression, including the underlying mathematical concepts and practical applications. (Multiple Reviews) - It offers valuable expert tips and insights into machine learning, feature engineering, and problem-solving. (Several Reviews) - The instructor presents ideas clearly and encourages adult learners effectively. (Several Reviews) - Real-time coding demonstrations with error explanations enhance the learning experience. (Several Reviews) - The course is suitable for beginners in machine learning who have some prior programming knowledge, particularly in Python. (Several Reviews) - Interactive coding sessions and multiple ways to approach problems are highly appreciated. (Several Reviews) - Practical examples like facial recognition projects make the concepts more relatable and easier to understand. (Several Reviews) - The inclusion of mathematical formulas and detailed explanations of theory is a strong point for learners who prefer a foundation in math before applying it in Python. (Several Reviews) - The course is recommended for those interested in the mathematical foundations of logistic regression. (Specific Review) - It's considered one of the best courses available for the topic. (Specific Review) - Q&A sections are very helpful and responsive. (Specific Review) **Cons:** - Some learners find the pace of the course too fast, especially when it comes to complex topics like probability. (Several Reviews) - The course's depth and the teaching style require active engagement and independent study outside of the course material. (Multiple Reviews) - For some, the amount of mathematics presented on the first pass can be overwhelming. (Specific Review) - The course content is dense and may require multiple viewings or additional resources to fully grasp all concepts. (Several Reviews) - Learners with limited programming experience in Python might struggle with the coding sessions without additional support. (Several Reviews) - Some learners would appreciate further dissection of certain topics to provide clearer explanations. (Specific Review) - The course may be challenging for those who are not fond of or prepared for a rigorous mathematical approach to machine learning. (Specific Review) **Additional Notes:** The course is well-rounded, covering both theoretical and practical aspects of logistic regression. It caters to learners with some prior knowledge in programming, particularly in Python, and is highly recommended for those looking to deepen their understanding of the subject. The teaching style, although challenging, is engaging and builds muscle, as noted by one reviewer who likens it to their own teaching style but finds it a bit more tough. Overall, the course receives high praise for its comprehensive approach and practical applications in real-world projects. (Summary of Multiple Reviews) 🔍 **Key Themes from Reviewers:** - **Depth of Content**: The course delves deeply into logistic regression and related mathematical concepts, which is a strong point for learners who seek a thorough understanding. - **Practical Application**: Real-world applications, such as Ecommerce projects and facial recognition, are highlighted to help learners see the practical use of what they're learning. - **Coding Sessions**: Coding along with the instructor is beneficial for hands-on learning and problem-solving. - **Mathematical Focus**: The course emphasizes the mathematical foundations, which might be a plus for those interested in a strong theoretical background but could be daunting if not prepared for this intensity. - **Teaching Style**: The instructor's approach is both challenging and enlightening, with a focus on independent learning and critical thinking. In summary, this course is a comprehensive resource for learners who are serious about understanding logistic regression and its mathematical underpinnings, and who are prepared to engage with complex content and programming exercises. It is particularly well-suited for those with some background in Python programming.

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