Welcome to my repository documenting the learning journey through various machine learning techniques and models. This collection encapsulates my hands-on experience with a range of methodologies aimed at both Supervised and Unsupervised Learning, Including Regression and Classification Models, Clustering Techniques, and Recommendation Systems.
Introduction and Data Processing:
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Matplotlib, Numpy, Pandas
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Statics and Plots:
- varianse, mean, mode, median
- Box Plot
- Skewness and Quartiles
- Q-Q Plot
- Histogram Analysis
- Correlation Detection
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Data Cleaning
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Normalizing
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Missed Data
Regression :
- Simple Liner Regression
- Multiple Inputs
- Polynominal Regression
- NoneLiner Regression
Classification:
- KNN
- Decision Tree
- Logestic Regression
- Separated vector machine
Clustering:
- KMeans
- Hieracialy
- DBSCAN
Recommandation systems:
- Context Base
- Collabrative Base
Data Mining:
- Pre Processing
- Regression
- Classification
- Clustering
Computational Intelligence:
- Fuzzy Logic
- Genetic Algorithms