🤖 The ML Grind: Hands-On Machine Learning

"Learning Machine Learning is not about memorizing syntax. It's about understanding the math behind the magic."

🚀 About The Repo

Welcome to my code garden! 🌱

This repository is where I document my implementation of concepts from the Hands-On Machine Learning book. I am building these models from the ground up to understand not just how to use the libraries, but why they work.

Currently, I have implemented foundational models for Regression and Classification, solving real-world data problems.

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📂 Current Builds

Here is what is currently live in the repo. I've broken down the problem statements and the tech used.

Project	Type	The Mission	Key Tech Stack	Status
01. Housing Price Predictor	Regression	Predict district housing prices based on median income, location, and population density.	LinearRegression, Pipelines, OneHotEncoder, RMSE	✅
02. Binary Classifier	Classification	Distinguish between classes (e.g., "5" vs "Not 5" in MNIST) with high precision.	SGDClassifier, Cross-Validation, Confusion Matrix, ROC Curves	✅

🔍 Deep Dive: What I Learned

1. Linear Regression (The Foundation)

It wasn't just about model.fit(). The real challenge was data preprocessing.

• Learnings: Handling missing data with Imputers, feature scaling (Standardization vs Normalization), and building transformation pipelines.
• Result: Achieved a baseline RMSE that I am now working to improve with ensemble methods.

2. Classification (The Decision Maker)

Moved from predicting values to predicting categories.

• Learnings: Accuracy is a lie! I learned why we need Precision, Recall, and F1 Scores—especially for skewed datasets.
• Visuals: heavily utilized Matplotlib to plot Precision-Recall trade-offs.

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🔮 Upcoming Modules

I am currently studying and coding the following algorithms. Watch this space!

• Support Vector Machines (SVM): Understanding Kernels and Margins.
• Decision Trees: Visualizing splits and Gini impurity.
• Random Forests: The power of Ensemble learning.

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🛠️ How to Run This

Want to test the models yourself?

1. Clone the repo:

   git clone [https://github.com/yourusername/ml-journey.git](https://github.com/yourusername/ml-journey.git)

2. Install dependencies:

   pip install pandas numpy scikit-learn matplotlib jupyter

3. Fire up the notebook:

   jupyter notebook

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📬 Let's Connect!

I am an aspiring ML Engineer always looking for feedback on my code or a good chat about the latest in AI.

• LinkedIn: LinkedIn Profile

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Built with 💻 and ☕ by Harsh Prajapati