This course strengthens your Python programming skills specifically for machine learning applications. Build upon your foundational Python knowledge to master essential libraries like NumPy and Pandas for data manipulation, analysis, and preparation. Learn effective data visualization techniques with Matplotlib and Seaborn, and understand how to structure and optimize Python code for common machine learning workflows. Gain practical experience in preparing data for model training.
Prerequisites Foundational Python skills
Level:
Advanced Python Techniques
Apply intermediate Python features like comprehensions, generators, decorators, and OOP principles relevant to data science.
Numerical Computation with NumPy
Perform efficient array operations, indexing, slicing, broadcasting, and linear algebra using NumPy.
Data Manipulation with Pandas
Utilize Pandas Series and DataFrames for loading, cleaning, transforming, grouping, and merging datasets.
Data Visualization
Create informative static plots using Matplotlib and Seaborn for data exploration and communication.
Data Preparation for ML
Implement common data preprocessing steps such as feature scaling, encoding categorical data, and splitting datasets.
Efficient Python Code
Write cleaner, more efficient, and maintainable Python code following best practices for ML projects.
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