Hello! I'm Daniel, a machine learning enthusiast passionate about developing intelligent systems and solving complex problems through data-driven approaches. I enjoy working on projects related to artificial intelligence, data science, Web developement and software development.
- Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM)
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis (PCA), Hierarchical Clustering
- Deep Learning: Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs)
- Natural Language Processing (NLP): Text Preprocessing, Sentiment Analysis, Word Embeddings, Transformers
- Model Evaluation and Optimization: Cross-Validation, Grid Search, Hyperparameter Tuning, Model Metrics
- Python: Essential for machine learning, data analysis, and scripting.
- SQL: Crucial for database management and data querying.
- JavaScript: Important for web development and interactive visualizations.
- Java: Good for large-scale systems and Android development.
- C++: Useful for performance-critical applications.
- Version Control: Git, GitHub
- Software Testing: Unit Testing, Integration Testing, Test-Driven Development (TDD)
- Data Wrangling: pandas, NumPy
- Data Visualization: Matplotlib, Seaborn
- Big Data Technologies: Hadoop, Spark
- Business Intelligence Tools: Tableau, Power BI
- Frontend: HTML, CSS, JavaScript, React, Angular, Vue.js
- Backend: Node.js, Django, Flask, Express.js
- Databases: MySQL, MongoDB
- Cloud Platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure
- Infrastructure as Code (IaC): Terraform, Ansible
- Monitoring and Logging: Prometheus, Grafana, ELK Stack
- Problem-Solving: Analytical thinking, creativity in solution design
- Collaboration: Teamwork, communication, project management
- Adaptability: Learning new technologies, adapting to new challenges