Overview of Machine Learning Concepts
Explore fundamental machine learning concepts, encompassing both the iterative process and key algorithms used in data analysis and prediction.
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Artificial intelligence and machine learning
Artificial intelligence (AI) refers to the ability of machines to mimic human intelligence. AI aims to develop systems that can perform tasks like problem-solving, pattern matching, image recognition, knowledge acquisition, and more.
Machine Learning (ML), a branch of AI, focuses on enabling computers to learn automatically without explicit programming. ML has gained immense popularity and has shown tremendous potential in various domains, including recommendation systems, fraud detection, and self-driving cars.
Machine learning steps
PySpark emerges as a powerhouse for machine learning, boasting a wealth of tools and algorithms that facilitate a seamless model-building process. PySpark’s MLlib and DataFrame API collectively furnish a sophisticated environment, streamlining the intricate process of ML. The following constitute the basic steps in the ML pipeline:
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Data collection: The first step is to collect the relevant data for our ML task. PySpark provides various data sources and connectors to read data from different file formats and databases.
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Data preprocessing: Once the data is collected, it often requires preprocessing to handle missing values, outliers, and other data quality issues. PySpark’s MLlib provides tools and functions for data preprocessing tasks, such as cleaning, filtering, imputing, and transforming the data.
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Exploratory data analysis (EDA): EDA involves gaining insights and understanding the data by exploring its statistical properties, visualizing distributions, and identifying patterns or correlations. PySpark’s MLlib integrates well with other data visualization libraries, ...