Hello guys, welcome to my Data Science Portfolio. I include some knowledges I earn in my journey. I'll include some case study, papers, and code.
Note: This repository was created recently and I upgrade as soon as I can.
- Fundamental of Statistic;
- Population vs Sample;
- Mean;
- Variance;
- Standard Deviation (STD);
- Correlation;
- Covariance;
- Probability Distribution Functions;
- Bayes Theorem.
- Bootstraping;
- Expectation Algorithm;
- Code Challenge;
- knapsack-problem-using-dp-grasp-tabu;
- Dynamic programming;
- Grasp Heuristic;
- TABU Search.
- Machine Leaning Algorithms - Tutorial;
- Pattern Recognition:
- Linear Algebra - Singular Value Decomposition;
- Vector Calculus;
- Probability and Distribution;
- Optimization and Convex Optimization.
- Machine Learning Algorithm:
- Linear:
- Perceptron;
- Adaline;
- Logistic Regression.
- Non Linear:
- SVM;
- Decision Tree;
- Random Forest;
- Adaboost;
- Gradient Boosting;
- XGBoost.
- Linear:
- Linear Algebra - Singular Value Decomposition;