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  1. Data/Abraham-Silberschatz-Henry-F.-Korth-S.-Sudarshan-Database-System-Concepts-McGraw-Hill-Education-2019.pdf +0 -3
  2. Data/Fundamentals_of_Database_Systems_6th_Edition-1.pdf +0 -3
  3. Data/Hands On Machine Learning with Scikit Learn and TensorFlow.pdf +0 -3
  4. Data/dbms-peter-rob.pdf +0 -3
  5. Data/machine_learning.txt +0 -15
  6. Data/python_intro.txt +0 -13
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Data/machine_learning.txt DELETED
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- Machine Learning Basics
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-
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- Machine learning is a subset of artificial intelligence that enables systems to learn and improve
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- from experience without being explicitly programmed. It focuses on developing computer programs
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- that can access data and use it to learn for themselves.
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-
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- Types of Machine Learning:
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- 1. Supervised Learning: Learning with labeled data
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- 2. Unsupervised Learning: Finding patterns in unlabeled data
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- 3. Reinforcement Learning: Learning through rewards and penalties
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-
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- Applications include image recognition, speech processing, and recommendation systems
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Data/python_intro.txt DELETED
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- Python Programming Introduction
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- Python is a high-level, interpreted programming language known for its simplicity and readability.
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- Created by Guido van Rossum and first released in 1991, Python has become one of the most popular
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- programming languages in the world.
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-
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- Key Features:
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- - Easy to learn and use
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- - Extensive standard library
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- - Cross-platform compatibility
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- - Strong community support
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- Python is widely used in web development, data science, artificial intelligence, and automation.