Intended for those interested in Machine Learning, this advanced course delves deeper into the extensive functionalities of Numpy and Pandas. The course covers complex operations, large-scale data manipulation, and cross-disciplinary applications.
Exploring the California Housing Dataset
Enhance the DataFrame with a New Population-Household Value Feature
Combining, Normalizing Features and Computing Weighted Sum with Numpy
Normalizing Matrix to [0, 1] Range
Fixing the Image Pixels Normalization and Weights Calculation
Normalization and Transposition of a Matrix
Mastering Advanced Matrix Operations with Numpy: Write from Scratch
Calculating Median Income by Room per Household
Grouping Data by Average Rooms Categories
Average Bedrooms Per Unit vs. Max Income: Debug and Fix
Calculate Average Population by Room Size Categories
Analyzing Average Bedrooms Across Different Age Categories in Housing Data
Speed Comparison: Python's Sum vs. Numpy's Sum
Memory Optimization: Compute and Display Percentage of Memory Saved
Optimizing Memory Usage through Data Type Conversions
Optimizing DataFrame Memory Usage by Converting Data Types
Optimizing Memory Usage for Large Datasets with Python, Numpy and Pandas
Sorting Gene Dataset by Popularity in Social Networks
Exploring Lowercase DNA Sequences in Bioinformatics Data
Exploring Bioinformatic Data and Filtering Astronomical Data
Manipulating Bioinformatics and Astronomical Data with Python, Pandas, and Numpy