Mashg‘ulotlar shakli: Amaliy mashg‘ulot (A)
A1
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Introduction to machine learning and its basic concepts, algorithms. Types of machine learning. General stages of the machine learning process.
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A2
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The use of tools in machine learning. Working with the Python software environment. Getting to know Python's custom machine learning environments (Google Collab, etc.).
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A3
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Linear algebra for machine learning. Programming linear algebra problems.
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A4
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Selective Training Formulation (Pandas Library)
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A5
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Methods of graphical representation of data in machine learning
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A6
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Linear regression problems with one and several variables and their Programming
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A7
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The concept of logistic regression and their application in machine learning.
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A8
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Classification algorithms (SVM, KNN, RF, DT) and their programming in machine learning.
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A9
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Unsupervised learning algorithms in machine learning
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A10
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((K-means and hierarchical clustering) learn and program them.
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A11
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Building a simple neural network using the Python programming language.
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A12
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Creating a neural network model designed for regression tasks.
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A13
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Solving classification problems through neural networks.
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A14
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Problems of overstrain and avoidance and ways to solve them. Optimization of the L1 and L2 levels.
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A15
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Neural networks based on deep learning. The architecture of convolutional neural networks (CNN), recurrent neural networks (GRNN), autoencoders and other algorithms based on deep learning and their capabilities.
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