Machine Learning
This course provides a comprehensive introduction to machine learning, a subset of artificial intelligence that enables computers to learn from data and make predictions. Machine learning is used in various applications, including recommendation systems, image recognition, and natural language processing. Students will cover:
- Introduction to Machine Learning: Students will gain an understanding of what machine learning is, its significance, and the different types of machine learning: supervised, unsupervised, and reinforcement learning.
- Data Preprocessing: This section covers essential techniques for preparing data for analysis, including cleaning, normalization, encoding categorical variables, and handling missing data.
- Supervised Learning Algorithms: Students will learn about common supervised learning algorithms, such as linear regression, logistic regression, decision trees, and support vector machines. They will explore how to build and evaluate predictive models.
- Unsupervised Learning Algorithms: The course introduces unsupervised learning techniques, including clustering (k-means, hierarchical clustering) and dimensionality reduction (PCA), for uncovering patterns in data without labeled outputs.
- Model Evaluation and Selection: Students will learn methods for evaluating model performance, including metrics like accuracy, precision, recall, and F1-score. They will also explore techniques for model selection and validation, such as cross-validation.
- Hands-On Projects: Throughout the course, students will engage in hands-on projects to apply machine learning techniques to real-world problems, reinforcing their understanding of concepts and methodologies.
This machine learning course is suitable for beginners with a basic understanding of programming and statistics. By the end, students will have the skills to develop, evaluate, and implement machine learning models, preparing them for careers in data science, AI, and analytics.