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.
Overview
Syllabus
- Lesson 1: Exploring the California Housing Dataset: An Introduction to Dataset Characteristics and Basic Visualizations
- Exploring the California Housing Dataset
- Enhance the DataFrame with a New Population-Household Value Feature
- Lesson 2: Mastering Matrix Operations in Numpy for Machine Learning Applications
- 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
- Lesson 3: Mastering Advanced Functions in Pandas: Groupby and Apply for Large-Scale Data Analysis
- 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
- Lesson 4: Mastering Code Optimization with Numpy and Pandas for Large Datasets
- 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
- Lesson 5: Expanding Horizons: Applications of Numpy and Pandas in Bioinformatics, Astronomy, and Social Networks
- 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
