[Free] Dive Into Learning From Data: Mnist With Logistic Regression

Master Classification with Python: Learn logistic regression, PCA, and feature engineering to achieve 98% accuracy! – Free Course

Added on March 16, 2025 IT & Software 3 min read

What you’ll learn

Understand the Fundamentals of Classification: how to work with the MNIST dataset, preprocess image data, and visualize handwritten digits
Build and Train a Logistic Regression Classifier from scratch, understand the mathematics behind it (e.g., sigmoid function)
Evaluate Model Performance: accuracy, precision, recall, F1 score, and confusion matrices
Apply Advanced Techniques for Better Performance: Principal Component Analysis (PCA) for dimensionality reduction and Polynomial Feature Expansion

Requirements

Familiarity with Python programming is recommended. You should be comfortable with basic syntax, data structures (e.g., lists, dictionaries), and control flow (e.g., loops, conditionals).
A basic understanding of linear algebra (e.g., vectors, matrices) and probability will help you grasp concepts like Logistic Regression and PCA. Don’t worry—we’ll break everything down step by step!
You'll need a Python environment set up with libraries like NumPy, pandas, matplotlib, and scikit-learn
No prior experience in machine learning is required! This course is designed to be beginner-friendly, with clear explanations and hands-on examples to guide you every step of the way.

Description

Unlock the Power of Image Classification with Python!

Are you ready to dive into the fascinating world of image classification? In this comprehensive course, you’ll learn how to teach a computer to recognize and classify images using Python. Whether you’re a beginner or an experienced data scientist, this course will guide you through the entire process of building, training, and evaluating image classification models.

Handwritten Digit Recognition — Learn Everything You Need to Start Your Machine Learning Journey in One Comprehensive Course!

What You’ll Learn:

Introduction to Image Classification: Understand the fundamentals of image classification and explore the MNIST dataset, a collection of handwritten digits.
Data Preprocessing: Learn how to preprocess and visualize image data using Python libraries like matplotlib and scikit-learn.
Building a Simple Classifier: Implement a logistic regression model to classify handwritten digits and understand the underlying mathematics, including the sigmoid function.
Model Evaluation: Dive into model evaluation techniques, including accuracy, precision, recall, and F1 score. Learn how to interpret confusion matrices and improve model performance.
Advanced Techniques: Explore advanced techniques like Principal Component Analysis (PCA) for dimensionality reduction and polynomial feature expansion to capture complex relationships in the data.
Optimization: Discover how to fine-tune your models by scaling data, balancing class weights, and optimizing hyperparameters.

Prerequisites:

Basic knowledge of Python programming.
Familiarity with basic machine learning concepts (helpful but not required).

Who Is This Course For?

Aspiring data scientists and machine learning enthusiasts who want to learn image classification from scratch.
Python developers looking to expand their skill set into machine learning and computer vision.
Professionals who want to understand the theory and practical implementation of image classification models.

By the End of This Course, You’ll Be Able To:

Preprocess and visualize image data effectively.
Build and train image classification models using logistic regression.
Evaluate and interpret model performance using various metrics.
Apply advanced techniques like PCA and polynomial feature expansion to improve model accuracy.
Fine-tune models for optimal performance.