Welcome to my Machine Learning Tasks Repository! This collection showcases my exploration of various machine learning tasks, spanning from fundamental techniques to advanced applications. Each task involves in-depth analysis, experimentation, and examination of the effects of different parameters.
- Linear Regression: Predictive modeling technique for analyzing relationships between variables.
- Linear Classification: Categorizing data using linear decision boundaries.
- Logistic Regression: Statistical method for binary outcome prediction.
- Bayes Theory: Bayesian methods for probabilistic modeling and inference.
- Decision Trees: Hierarchical structures for classification and regression.
- Random Forest: Ensemble learning with multiple decision trees.
- SVM Classification: Support Vector Machines for binary and multiclass classification.
- Dimensionality Reduction: Techniques for handling high-dimensional data.
- Clustering: Unsupervised learning for grouping similar data points.
- Word to Vector Representation: Transforming words into numerical vectors.
- Reinforcement Learning: Agents learning optimal actions in an environment.
- Recommender Systems: Predicting and recommending items based on user preferences.
For each task, I've conducted detailed analyses to understand the impact of different parameters on model performance and generalization. The repository includes comprehensive documentation, code implementations, and insights gained during the exploration of each machine learning task.
Navigate to the specific task Notebook file for detailed documentated implementations