Master Classification with Python: Learn logistic regression, PCA, and feature engineering to achieve 98% accuracy!
| Course Details | |
|---|---|
| Title | Dive Into Learning From Data: MNIST with Logistic Regression |
| Category | IT & Software |
| Sub Category | Other IT & Software |
| Creator Name | Nick Ovchinnikov |
| Language | English |
| Rating | 0 |
| Length | 1:50 Hours |
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.
Enroll Now and Start Your Journey into Image Classification with Python!
