Machine Learning Foundations

Structured learning materials from Ostad’s guidance (Batch 38)

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📂 Modules

Python & Data Science

• Functions: Core Python concepts
• NumPy: Numerical computing basics
• Pandas: Data manipulation
• Matplotlib/Seaborn: Data visualization
• IQR & Encoding: Data preprocessing

Machine Learning

• Linear Regression: Implementation and theory
• Logistic Regression: Classification models
• KNN: k-Nearest Neighbors
• Decision Trees: With cross-validation
• SVM: Support Vector Machines
• K-Means: Clustering algorithms
• Naive Bayes: Probability-based classification

Special Topics

• Feature Scaling & PCA: Data normalization
• Model Persistence: Saving/loading models
• ANN: Artificial Neural Networks intro
• NLP: Basic Natural Language Processing

Projects

• EDA Projects: Exploratory Data Analysis
• ML Projects: End-to-end machine learning

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🛠️ How to Use

1. Clone this repo
2. Navigate to specific topic folders (e.g., /2-List_Tuples_Set_Dict/1-Python/5-Numpy)
3. Open Jupyter notebooks or Python scripts

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🌟 Credits

• Ostad (Batch 38) for the structured curriculum
• Batch 38 peers for collaborative learning

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_{📚 Focused on core concepts | No fluff}