Deep Learning Prerequisites: Logistic Regression in Python

Explore the fascinating world of artificial intelligence (AI) and understand the inner workings of cutting-edge technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion. This course provides a solid foundation in the principles behind these revolutionary AI applications.

This course serves as an excellent stepping stone to more advanced topics in deep learning and neural networks. We begin with logistic regression, a fundamental technique widely used in machine learning, data science, and statistics. You'll learn the theory from the ground up, including the derivation of the solution and practical applications to real-world scenarios. We'll guide you through coding your own logistic regression module in Python.

Best of all, this course requires no external materials. Everything you need – including Python and essential Python libraries – can be obtained for free. You'll gain practical experience through numerous examples, demonstrating how deep learning can be applied to a wide range of problems.

A key component of this course is a comprehensive project where you'll predict user actions on a website using user data. This includes factors like device type, number of products viewed, time spent on site, return visitor status, and time of day. Further, you'll tackle a project focused on facial expression recognition, leveraging deep learning to predict emotions from images.

This course is ideal for programmers looking to enhance their skills with data science. If you have a technical or mathematical background and want to apply your abilities to data-driven decision-making and business optimization using scientific principles, this course is designed for you.

We emphasize understanding how to build and interpret models, not just how to use pre-built tools. Forget simply plugging data into libraries. This course focuses on implementation from scratch, allowing you to truly grasp the underlying mechanisms. You'll learn to visualize what's happening inside the model through experimentation. As Richard Feynman famously said, "What I cannot create, I do not understand."

Unlike courses that simply show you how to use APIs, this course teaches you how to implement machine learning algorithms from scratch. We believe that true learning comes from building things yourself. Avoid the trap of repetitive tasks – we focus on learning principles rather than rote memorization.

Suggested Prerequisites:

• Calculus (derivatives)
• Matrix Arithmetic
• Probability
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file

Course Roadmap: For a comprehensive overview of prerequisites and course sequencing, please refer to the "Machine Learning and AI Prerequisite Roadmap" lecture, available in the FAQ section of any of my courses (including the free Numpy course).