Python Programming for Machine Learning
Description
Python programming skills specifically focused on machine learning applications, data manipulation, and scientific computing.
Learning Objectives
Learners will develop proficiency in Python programming for machine learning applications including data manipulation with pandas, numerical computing with NumPy, visualization with matplotlib/seaborn, and ML implementation with scikit-learn. They will master Python libraries essential for data science workflows, file handling, API interactions, and integration with AWS services through boto3 SDK.
Topics (10)
Core Python concepts including variables, data types, loops, conditionals, functions, and object-oriented programming principles.
Comprehensive NumPy usage including array creation, indexing, slicing, mathematical functions, linear algebra operations, and random number generation.
Advanced pandas operations including data loading, cleaning, transformation, aggregation, merging, and time series analysis for ML data preparation.
Comprehensive visualization techniques including plots, charts, statistical visualizations, and custom graphics for data analysis and presentation.
Comprehensive file handling including CSV, JSON, Parquet, database connections, API data retrieval, and cloud storage integration.
API integration techniques including requests library, authentication, error handling, and data retrieval from web services and cloud APIs.
Comprehensive boto3 usage for AWS service integration including S3, EC2, SageMaker, IAM, and other services essential for ML workflows.
Advanced error handling including exception management, logging, debugging tools, unit testing, and code quality practices for ML applications.
Advanced Python optimization including profiling, memory management, vectorization, parallel processing, and best practices for scalable ML code.
Practical ML implementation including model training, evaluation, preprocessing, feature selection, and pipeline creation using scikit-learn.