🧠 The Ultimate Machine Learning Bootcamp

Welcome to the ML Bootcamp – a comprehensive hands-on repository for mastering core concepts, algorithms, and real-world machine learning workflows. Perfect for students, job seekers, and anyone preparing for ML interviews or projects.

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📘 Supervised Learning

Folder	Content
01-Introduction	Roadmap of ML, types, and tools overview
02-Linear Regression	Basics of regression and prediction modeling
03-Ridge Lasso And Elasticnet	Regularization to prevent overfitting
04-Step By Step Project Implementation	Full ML lifecycle with EDA → deployment
05-Logistic Regression	Classification using sigmoid-based models
06-SVM	Support Vector Machines with kernels
07-Naive Bayes	Probabilistic models for classification
08-K Nearest Neighbor	Instance-based lazy learning
09-Decision Tree	Tree-based decision modeling
10-Random Forest	Ensemble of trees for robust prediction
11-Adaboost	Weighted boosting on misclassified data
12-Gradient Boosting	Sequential model boosting
13-XgBoost	Optimized gradient boosting (Kaggle favorite)

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📊 Unsupervised Learning

Folder	Content
14-Unsupervised Machine Learning	Clustering & dimensionality reduction
15-PCA	Reduce dimensions while preserving variance
16-K Means Clustering	Popular centroid-based clustering
17-Hierarchical Clustering	Tree-style grouping (dendrograms)
18-DBSCAN Clustering	Density-based clustering (noise-resistant)
19-Silhouette Clustering	Evaluate clustering performance

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🚨 Anomaly Detection

Folder	Content
20-Anomaly Detection ML	Detect outliers using Isolation Forest & LOF

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⚙️ DevOps & ML Lifecycle

Folder	Content
21-Dockers	Docker essentials for ML containerization
22-Git And Github	Git cheat sheets & version control
23-MLFlow Dagshub BentoML	Track experiments & deploy models

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🏁 Capstone Projects

Repositories
Student-Score-Prediction
Phishing-URL-Classifier
churnguard-bank-customer-churn-prediction
rnn-imdb-movie-review-sentiment-classifier
Next Word Prediction with LSTM

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🤖 Deep Learning

Folder	Content
24-Deep Learning ANN	CNN basics, activation & loss functions, optimizers
25-RNN	RNN basics, backward propagation, embeddings
26-LSTM+GRU	LSTM architecture, variants, GRU

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✅ Requirements

Install required packages:

pip install -r requirements.txt

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This repository is crafted for hands-on ML & NLP mastery — blending theory, visuals, and real-world implementations. Ideal for portfolio building, interview prep, and structured learning.

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