Master Advanced Statistics, Deep Learning Optimization, Time Series Forecasting, Bayesian Modeling
What you will learn
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Understand and apply key probability distributions, including Normal, Binomial, and Poisson distributions.
Transform skewed datasets into normal distributions using techniques like log, square root, and power transformations.
Calculate and interpret confidence intervals for critical statistical estimates, such as model accuracy.
Distinguish between population data and sample data, and understand their roles in analysis.
Perform random sampling correctly and understand its impact on the validity of data analysis.
Evaluate classification models using metrics like accuracy, precision, recall, and F1 score.
Identify and manage underfitting and overfitting issues in machine learning and statistical modeling.
Apply statistical modeling concepts to real-world deep learning workflows.
