max-mena-sd / datascienceml

In this repository will be archiving all the advancement in machine learning based in the research.

Machine Learning - Goals

Learning Structure

Programming Languages

• Python or R: Choose the programming language that suits your career goals, or learn both for a comprehensive understanding.

Research topics

1. Data Preprocessing
2. Regression:
   • Simple Linear Regression
   • Multiple Linear Regression
   • Polynomial Regression
   • SVR
   • Decision Tree Regression
   • Random Forest Regression
3. Classification:
   • Logistic Regression
   • K-NN
   • SVM
   • Kernel SVM
   • Naive Bayes
   • Decision Tree Classification
   • Random Forest Classification
4. Clustering:
   • K-Means
   • Hierarchical Clustering
5. Association Rule Learning:
   • Apriori
   • Eclat
6. Reinforcement Learning:
   • Upper Confidence Bound
   • Thompson Sampling
7. Natural Language Processing (NLP):
   • Bag-of-words model
   • NLP algorithms
8. Deep Learning:
   • Artificial Neural Networks
   • Convolutional Neural Networks
9. Dimensionality Reduction:
   • PCA
   • LDA
   • Kernel PCA
10. Model Selection & Boosting:
    • k-fold Cross Validation
    • Parameter Tuning
    • Grid Search
    • XGBoost

What I want to Learn

• Master Machine Learning using Python and R
• Develop intuition for various Machine Learning models
• Make accurate predictions and powerful analyses
• Build robust Machine Learning models
• Create added value for businesses using Machine Learning
• Apply advanced techniques like Reinforcement Learning, NLP, and Deep Learning
• Understand and choose the right Machine Learning model for different problems