🤖📊My Machine Learning Journey🧠📈💡

Welcome to my Machine Learning journey!

Here's a categorized list of all my published and upcoming articles:

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🧠 Supervised Learning Algorithms

Models trained on labeled data — where inputs are paired with known outputs.

📘 Foundations

1. Machine Learning Basics
2. Types of Machine Learning Algorithms
3. Classification of Machine Learning Algorithms
4. Machine Learning Explained with Tea — A Zero Knowledge Analogy

⚙️ Data Preparation

1. Feature Engineering
2. Feature Scaling

📐 Regression

1. Linear Regression Algorithm
2. Linear Regression for Absolute Beginners with Tea — A Zero Knowledge Analogy
3. Predicting Tea Sales with ML — Linear Regression, Gradient Descent & Regularization (Beginner)
4. How to Check if Linear Regression Works for Your Dataset
5. Ridge Regression and Lasso Regression

🔢 Classification

1. Logistic Regression Algorithm
2. Logistic Regression but Make it Tea — ML Basics Served Hot
3. How to Check if Logistic Regression Works for Your Dataset
4. Decision Trees Algorithm
5. How to Check if Decision Trees Work for Your Dataset
6. Random Forest Algorithm
7. How to Check if Random Forests Work for Your Dataset

🧮 Neural Networks

1. Neural Networks for Absolute Beginners
2. Forward and Backward Propagation in Neural Networks
3. Brewing Neural Networks with TensorFlow — A Coffee Example for Beginners
4. Vectorization in Neural Networks — A Beginner’s Guide

🧭 Strategy & Evaluation

1. Choosing the Right Machine Learning Algorithm
2. How to Know if Your Data is Linear, Non-Linear, or Complex
3. How to Evaluate ML Models Step by Step
4. Understanding AGI vs ANI — A Beginner’s Guide to Artificial Intelligence

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🧩 Unsupervised Learning Algorithms

Models that find patterns in unlabeled data — no predefined outputs.

🔍 Clustering & Dimensionality Reduction

• K-Means Clustering (Coming Soon)
• Hierarchical Clustering (Coming Soon)
• DBSCAN (Coming Soon)
• Principal Component Analysis (PCA) (Coming Soon)
• Autoencoders (Coming Soon)

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