Statistics and Machine Learning For Regression Modelling With R
Learn Complete Hands-On Regression Analysis in R for Machine Learning, Statistical Analysis, Data Science, Deep Learning
This comprehensive program is designed for individuals seeking to master the art of regression modeling using the R programming language. From understanding foundational statistical concepts to implementing advanced machine learning algorithms, this course provides a robust foundation for predicting and understanding complex relationships within data.
What You Will Learn:
- Foundations of Regression Analysis:
- Gain a deep understanding of regression analysis and its applications in statistical modeling.
- Learn the principles of linear regression and its assumptions.
- Exploratory Data Analysis (EDA) with R:
- Master the art of exploring and visualizing data using R.
- Understand how to identify patterns, outliers, and relationships within datasets.
- Linear Regression in R:
- Implement linear regression models in R for predicting and modeling linear relationships.
- Explore techniques for model interpretation and evaluation.
- Multiple Regression and Variable Selection:
- Extend regression modeling to multiple variables and understand the nuances of variable selection.
- Learn how to build and interpret multiple regression models in R.
- Polynomial Regression and Nonlinear Models:
- Explore the flexibility of regression by implementing polynomial regression models.
- Understand how to model nonlinear relationships using advanced techniques.
- Machine Learning for Regression:
- Dive into machine learning algorithms for regression, including decision trees, random forests, and support vector machines.
- Learn how to implement and optimize these algorithms using R.
- Cross-Validation and Model Evaluation:
- Understand the importance of cross-validation in model assessment.
- Explore different metrics for evaluating regression models and ensuring robust performance.
- Time Series Regression:
- Learn how to handle time series data and implement time series regression models in R.
- Understand the unique challenges and considerations in time-dependent data.
- Advanced Topics and Applications:
- Delve into advanced regression modeling topics, such as regularization techniques.
- Explore real-world applications of regression modeling in various fields.
The instructors have a gift for breaking down complex statistical and machine learning concepts into digestible pieces. The course covers a wide range of topics, from traditional linear regression to advanced machine learning algorithms.
— Alexandra
The instructors take you on a journey from the basics to advanced topics, providing clear explanations and practical examples. The emphasis on both statistical foundations and machine learning algorithms sets this course apart.
— Jonathan
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