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Statistical Machine Learning: Methods and Applications


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Course content


Week 1: Introduction to Statistical Machine Learning

Overview of statistical machine learning concepts and terminology
Supervised vs. unsupervised learning
Model selection and evaluation metrics
Introduction to common learning algorithms (e.g., linear regression, logistic regression)

Week 2: Advanced Learning Algorithms

Week 3: Feature Selection, Dimensionality Reduction, and Regularization

Week 4: Advanced Topics in Statistical Machine Learning

About Course

The course “Statistical Machine Learning: Methods and Applications” provides an in-depth exploration of statistical approaches and their applications in the field of machine learning. Through this course, students will gain a comprehensive understanding of the theoretical foundations and practical techniques used in statistical machine learning.

The course begins by introducing fundamental concepts and principles of statistical learning, including supervised and unsupervised learning, model selection, and evaluation metrics. Students will learn about various types of learning algorithms, such as linear regression, logistic regression, decision trees, support vector machines, and neural networks.

In addition to the theoretical aspects, the course emphasizes the practical implementation and application of these methods. Students will have the opportunity to work on hands-on projects and assignments that involve implementing machine learning algorithms, tuning model parameters, and analyzing real-world datasets.

The course also covers topics such as feature selection, dimensionality reduction, ensemble methods, and regularization techniques, providing students with a comprehensive toolkit for solving complex machine-learning problems.

Furthermore, the course explores advanced topics in statistical machine learning, including probabilistic graphical models, clustering algorithms, and deep learning. Students will gain insights into the underlying mathematical principles and learn how to apply these advanced techniques to real-world scenarios.

Throughout the course, there is an emphasis on understanding the assumptions, limitations, and interpretability of different machine-learning methods. Students will learn to critically evaluate model performance and make informed decisions when choosing and applying statistical machine-learning techniques.

The target audience for this course includes students, researchers, and professionals who are interested in deepening their understanding of statistical machine learning and its practical applications. It is beneficial for individuals with a background in mathematics, statistics, computer science, or related fields who want to enhance their knowledge and skills in this rapidly evolving field.

Overall, the course “Statistical Machine Learning: Methods and Applications” offers a comprehensive and practical approach to understanding and applying statistical techniques in the context of machine learning, equipping learners with valuable skills to address real-world data analysis and prediction problems.

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What Will You Learn?

  • Fundamentals of statistical machine learning
  • Popular learning algorithms
  • Feature selection and dimensionality reduction
  • Ensemble methods and model combination
  • Regularization techniques
  • Advanced topics in statistical machine learning
  • Practical implementation and hands-on projects
  • Model evaluation and interpretation
  • Real-world applications and case studies
  • Ethical considerations

Material Includes

  • Live Classes 2Hr Daily
  • Flexibility
  • Accessible Learning Materials
  • Sharable Certificate


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Material Includes

  • Live Classes 2Hr Daily
  • Flexibility
  • Accessible Learning Materials
  • Sharable Certificate

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