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Hands-on Python programming exercises: Enables readers to apply concepts immediately for deeper understanding
Covers supervised learning algorithms: Explores linear regression, logistic regression, SVM, decision trees, and neural networks for comprehensive skill development
Real-world dataset usage: Work with open-source datasets to build and evaluate machine learning models
Data preprocessing and model tuning: Learn to clean data, optimize features, and tune parameters for improved model accuracy
Performance metrics and design thinking: Gain insights into evaluating model effectiveness and fostering innovation
Summarized by Shop
Hands-On Ml Problem Solving And Creating Solutions Using Python.Key Features Introduction To Python Programming Python For Machine Learning Introduction To Machine Learning Introduction To Predictive Modelling, Supervised And Unsupervised Algorithms Linear Regression, Logistic Regression And Support Vector Machinesdescriptionyou Will Lear
Language
Python
Content
Supervised learning, data preprocessing, model evaluation
Format
Book
Target audience
Machine learning beginners, engineers, data scientists
