Master Key Machine Learning Algorithms: From Basics to Real-World Applications
What you will learn
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Understand key machine learning algorithms and their applications in real-world scenarios.
Build predictive models using supervised and unsupervised techniques.
Analyze and preprocess data for optimal algorithm performance.
Implement machine learning solutions using Python and popular libraries.
Master core concepts of supervised and unsupervised learning.
Apply decision trees, SVM, and neural networks in practical projects.
Evaluate model performance using accuracy, precision, and recall.
Build and optimize clustering models like K-Means and Hierarchical Clustering.
Understand ensemble techniques like Random Forest and Gradient Boosting.
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