Machine Learning and Predictive Modelling for Data Science

The course “Machine Learning and Predictive Modelling for Data Science” is designed to provide students with a comprehensive understanding of machine learning techniques and their applications in predictive modeling. Machine learning plays a crucial role in data science, enabling the extraction of valuable insights and making accurate predictions from complex datasets.

Through a combination of theoretical lectures, practical exercises, and hands-on projects, students will explore the foundational concepts and practical aspects of machine learning. The course covers a wide range of topics, including supervised learning, unsupervised learning, model evaluation, feature engineering, and model deployment.

Students will learn various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. They will understand the underlying principles of these algorithms, their strengths, and their limitations. Practical implementation of these algorithms using popular libraries and frameworks such as sci-kit-learn and TensorFlow will be covered.

Furthermore, students will delve into unsupervised learning techniques, such as clustering and dimensionality reduction. They will gain insights into algorithms such as k-means, hierarchical clustering, and principal component analysis (PCA). Students will learn how to apply these techniques to discover patterns, identify groups, and reduce the dimensionality of high-dimensional data.

The course emphasizes the importance of model evaluation and selection. Students will learn various evaluation metrics and techniques to assess model performance and avoid overfitting or underfitting. They will also explore feature engineering, where they will learn how to extract meaningful features from raw data, perform feature selection, and handle categorical variables.

In addition, the course covers the essential aspects of deploying machine learning models into production. Students will learn how to prepare models for deployment, package them into standalone applications or APIs, and integrate them into real-world systems.

By the end of the course, students will have gained a solid understanding of machine learning algorithms, their applications, and their practical implementation for predictive modeling in data science. They will be equipped with the skills to build, evaluate, and deploy machine learning models, enabling them to solve real-world problems and make data-driven decisions.

Prerequisites: Successful completion of the “Foundations of Data Science: Principles and Techniques” course or equivalent knowledge. Proficiency in programming, particularly Python, is required. A basic understanding of statistics and mathematics, including linear algebra and calculus, is recommended.