Machine Learning Algorithms
Learn the fundamental machine learning algorithms and gain the skills to implement them in real-world applications.
This comprehensive course provides an in-depth understanding of various machine learning algorithms. Participants will learn how to apply these algorithms to solve real-world problems and enhance prediction accuracy. The course covers topics such as supervised learning, unsupervised learning, decision trees, support vector machines, and neural networks. By the end of the training, participants will have a strong foundation in machine learning algorithms and be able to apply them effectively in their projects.
Course Syllabus
- Introduction to Machine Learning
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Decision Trees
- Support Vector Machines
- Neural Networks
- Ensemble Methods
- Feature Selection and Engineering
- Model Evaluation and Validation
- Reinforcement Learning
- Deep Learning
- Natural Language Processing
- Time Series Analysis
- Dimensionality Reduction
- Clustering
- Recommendation Systems
- Anomaly Detection
- Case Studies and Real-World Applications
- Ethical Considerations in Machine Learning
- Future Trends and Emerging Technologies
Course Additional Information
Basic programming knowledge, familiarity with Python
Periods
| Start date | End date | Start time | End time | Target Audience | Meetings | Code | |||||
| 01.01.1970 | 01.01.1970 | 00:00 | 00:00 | -A |
