Deep Learning Prerequisites: Logistic Regression in Python

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

4.69 (4705 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

Course Gallery

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Deep Learning Prerequisites: Logistic Regression in Python – Screenshot 3 — Screenshot 3 – Deep Learning Prerequisites: Logistic Regression in Python

Deep Learning Prerequisites: Logistic Regression in Python – Screenshot 4 — Screenshot 4 – Deep Learning Prerequisites: Logistic Regression in Python

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Comidoc Review

Our Verdict

Deep Learning Prerequisites: Logistic Regression in Python by Lazy Programmer offers a comprehensive theoretical approach to understanding logistic regression in the context of data science and machine learning. While it provides valuable insights into application of these concepts, its dry delivery mode might prove challenging for beginners, especially those lacking required mathematical prerequisites or intermediate programming skills.

What We Liked

Covers mathematical foundations of logistic regression, including derivation of error function and its derivative
Instructor provides insights into connection between classification problem and biological neuron
Apply logistic regression to real-world business problems like predicting user actions from e-commerce data and facial expression recognition
High quality content with concepts explained in mathematical formulas and detailed theory demonstration

Potential Drawbacks

Delivery can be dry, lacking engaging visuals or interactive elements
Some parts move quickly, making it difficult to grasp complex math without re-watching videos multiple times
Course assumes strong foundational knowledge in Python, statistics, probability, calculus and linear algebra
Codes and explanations could be improved for better understanding of intermediate steps