Master essential data cleaning techniques including handling missing values, outliers, and data
normalization for high-quality datasets.
Explore supervised and unsupervised learning algorithms with practical implementations and
real-world applications in data science.
Learn neural networks, convolutional networks, and recurrent architectures for advanced pattern
recognition and prediction tasks.
Understand Python data structures including lists, dictionaries, sets, and tuples for efficient
data manipulation and analysis.