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via Udemy |
Go to Course: https://www.udemy.com/course/data-science-logistic-regression-in-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course: --- **Course Review and Recommendation: Foundations of Deep Learning and Neural Networks with Logistic Regression** If you're curious about how cutting-edge AI technologies like OpenAI's ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion operate behind the scenes, this course is an excellent starting point. Designed for learners with some technical or mathematical background, it offers a thorough introduction to fundamental machine learning concepts, with a particular focus on logistic regression—a core technique used in many AI applications. **What You'll Learn:** The course begins with the basics of logistic regression, covering the mathematical derivation and its real-world applications. One of its standout features is teaching you how to code your own logistic regression model in Python from scratch, rather than relying on pre-built libraries. This hands-on approach ensures you truly understand the mechanics behind the model. The course also explores visualization techniques to help you internalize what’s happening inside the model during training. **Real-World Projects:** The practical focus is evident through engaging projects. For example, you'll learn to predict user actions on websites based on data such as device type, browsing duration, and time of day. Another exciting project involves facial expression recognition—highlighting how deep learning can infer emotions from images. These projects foster deeper understanding by applying concepts directly to tangible problems. **Who Is This Course For?** - Programmers looking to expand their skills into data science and machine learning. - Individuals with a technical or mathematical background who want to leverage data-driven decision-making. - Learners eager to understand the inner workings of AI models rather than just how to use them via APIs. **Why It Stands Out:** Unlike many courses that focus solely on applying existing models through libraries, this course emphasizes building and understanding algorithms from scratch. It encourages experimentation and visualization, aligning perfectly with Feynman’s philosophy: "What I cannot create, I do not understand." The instructor stresses that true comprehension comes from implementation, not just code copying. **Prerequisites:** To get the most out of this course, you should have a basic understanding of calculus (derivatives), matrix arithmetic, probability, and Python programming skills, including familiarity with libraries like NumPy for matrix and vector operations. **Recommendation:** This course is highly recommended for anyone who wants to deepen their understanding of AI and machine learning fundamentals. It is especially beneficial for programmers and data enthusiasts who prefer learning by doing—building their own models and visualizing their internal workings. Before diving into advanced topics, consider taking the recommended prerequisite courses and reviewing the "Machine Learning and AI Prerequisite Roadmap" in the FAQ. **Bottom Line:** If you're ready to go beyond superficial application and truly comprehend how AI models work—and are willing to put in the effort to build your own from the ground up—this course offers invaluable insights and practical experience. It is a perfect stepping stone for aspiring data scientists, AI developers, and technically inclined learners eager to understand the core principles driving modern AI breakthroughs. --- Feel free to ask if you'd like me to help you with your course plan or additional details!
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 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 fileWHAT 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)