The ML Diaries — My Machine Learning Journey

Hey everyone 👋
Welcome to The ML Diaries, a place where I am documenting my learning journey from foundational machine learning concepts to practical implementations with code and visuals.

Each article here is part of a growing series where I explore how algorithms actually work behind the scenes, not just the “what” but the “why” and “how.”

I’ve organized the series below for an easy step-by-step flow

Foundation: Getting Started with Machine Learning

1. An Introduction to Machine Learning & the Relationship
A beginner’s introduction to what Machine Learning is, how it connects with AI and Data Science, and why it matters in today’s world.

2. Types of Machine Learning & Their Basic Workflow
Explains the three main types — Supervised, Unsupervised, and Reinforcement Learning — along with their general workflows.

3. Understanding Data, Attributes & Data Objects in Machine Learning
Learn what data really means in ML — features, attributes, and how they form the foundation of every model.

4. Before the Model: Essential Steps of Data Preprocessing in ML
Walk through the must-do data cleaning, handling missing values, normalization, and preparation steps before model training.

1. Supervised Learning Series

• Introduction to Supervised Learning
  A gentle start into supervised learning — understanding labeled data, workflow, and how machines learn from examples.

• A Simple Guide to Logistic Regression with Concepts, Workflow & Real-World Use Case
  Understand Logistic Regression in an intuitive way — how it predicts categories and where it’s used in real-world applications.

• Linear Regression — Explained in the Simplest Possible Way

  Understand the logic and the math behind this algorithm -

• Decision Tree Algorithm Explained: Simple Guide with Example and Gradio App
  Learn how Decision Trees make data-driven decisions step by step, with a practical example and interactive Gradio UI.

• Understanding Random Forest – The Power of Many Trees
  Dive deeper into ensemble learning — how combining multiple decision trees improves accuracy and reduces errors.

• Understanding K-Nearest Neighbors (KNN): A Simple Yet Powerful ML Algorithm
  Explore how KNN predicts outcomes by learning from nearby data points, with easy examples and explanations of distance metrics.

• Exploring the Naive Bayes Classifier: A Simple but Powerful Algorithm

  Dive into how Naive Bayes uses probability and prior knowledge to classify data from theory to real-world applications in an easy-to-understand way.

• SVM Made Easy: How Support Vector Machines Work for Classification and Regression

• Discover how Support Vector Machines (SVM) tackle classification and regression tasks, maximizing confidence and accuracy in machine learning

  2. Unsupervised Learning Series

• An Introductory Overview of Unsupervised Learning Discover how machines find patterns and group data without labels — covering clustering, association rules, and dimensionality reduction.

• Simplifying K-Means Clustering: A Beginner's Guide to Unsupervised Learning

  will explore one of the most popular and intuitive algorithms used in unsupervised learning — K-Means Clustering.

• DBSCAN Explained Simply: Clustering with Noise and Arbitrary Shapes

  will be covering another important clustering algorithm that comes right after K-Means in popularity and usefulness — DBSCAN.

Why I Created This

You know, algorithms are just one line of code away, but the real learning begins when you understand how and when to use them.

That’s what The ML Diaries is all about — making machine learning approachable, structured, and meaningful.