Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.
Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.
