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ML, DL & Computer Vision Learning Journey 🚀

Welcome to my personal learning journey through Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV), primarily based on Kaggle Learn and practical hands-on coding. This repository includes well-structured notebooks and code along with explanations and references.


📚 Courses Covered

Status: Completed ✅
This course covers the fundamentals of machine learning and introduces concepts using decision trees and random forests.

Topics:

  • Introduction to Machine Learning
  • How Models Work (Decision Trees)
  • Model Validation
  • Underfitting & Overfitting
  • Random Forests
  • Feature Engineering Basics
  • Data Leakage
  • Final Project

Status: Completed ✅
This course builds on the basics with a focus on handling missing data, categorical variables, and using XGBoost.

Topics:

  • Missing Values: Drop & Impute
  • Handling Categorical Variables: Drop, Ordinal Encoding, One-Hot Encoding
  • Pipelines in Scikit-Learn
  • Cross-Validation
  • XGBoost
  • Feature Importance
  • Advanced Data Leakage (Target leakage, Train-test contamination)
  • Final Project

Status: Completed ✅
This course introduces neural networks using PyTorch and focuses on core concepts like activation, optimization, and regularization.

Topics:

  • A Single Neuron & Linear Units
  • Fully Connected Networks
  • Activation Functions
  • Sequential Models
  • Loss Functions
  • Optimizers (SGD)
  • Learning Rate & Batch Size
  • Overfitting & Underfitting
  • Learning Curves
  • Early Stopping
  • Dropout & Batch Normalization
  • Classification Task (Final Project)

Status: Coming Soon 🔜
I will begin this course after completing documentation and final touches for the above modules.


📁 Folder Structure

ml-dl-computer-vision-journey/
│
├── README.md
├── machine-learning/
│   ├── 01_intro-to-ml/
│   └── 02_intermediate-ml/
│
├── deep-learning/
│   └── 03_intro-to-dl/
│
├── computer-vision/   # Coming Soon
│
└── datasets/          # Custom or Kaggle datasets (if any)

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Machine Learning, Deep Learning, and Computer Vision.

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