Statistical Learning for Data Science is a crucial specialization providing advanced techniques for model selection, including regression, classification, trees, SVM, and unsupervised learning. Through this program, you will gain in-depth understanding of coefficient estimation and interpretation, enhancing your ability to justify and explain your models to clients and companies. This course is ideal for individuals pursuing a career in data science or seeking to enhance their expertise in the field.
Throughout this specialization, you will acquire conceptual knowledge and communication skills to effectively convey the rationale behind your model choices and coefficient interpretations. The program builds upon foundational knowledge of statistics, allowing you to apply many regression and classification techniques, and will equip you with the skills to express why Statistical Learning is important and how it can be used.
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This course covers advanced statistical techniques, including model selection, regression, classification, resampling methods, splines, trees, SVM, and unsupervised learning. Gain in-depth understanding of coefficient estimation and interpretation for effective communication of model choices.
Statistical Learning for Data Science starts by delving into the importance and applications of Statistical Learning, enabling you to identify the strengths, weaknesses, and caveats of different models and choose the most appropriate model for a given statistical problem. Additionally, you will determine the type of data and problems that require supervised versus unsupervised techniques.
This module focuses on applying resampling methods to obtain additional information about fitted models. You will also learn to optimize fitting procedures to improve prediction accuracy and interpretability, as well as identify the benefits and approach of non-linear models.
Here, you will learn about the advantages and disadvantages of trees, as well as when and how to use them. The module also covers the application of Support Vector Machines (SVMs) for binary classification or K > 2 classes, and the analysis of strengths and weaknesses of neural networks compared to other machine learning algorithms.
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