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9798290381794: Machine Learning with Python for Beginners: Build Real-World Projects Using Scikit-Learn and Pandas

Sinopsis

  • Master the Fundamentals of Machine Learning: Grasp the core concepts of supervised, unsupervised, and reinforcement learning, and understand the complete machine learning workflow from problem definition to deployment.

  • Set Up a Robust Python Environment: Learn how to install and configure essential libraries like Pandas, Scikit-Learn, NumPy, and Matplotlib in a virtual environment for your machine learning projects.

  • Become Proficient in Data Handling with Pandas: Develop strong skills in loading, cleaning, preparing, and exploring tabular data using Pandas, including handling missing values, duplicates, and different data types.

  • Visualize and Analyze Your Data: Use Matplotlib and Seaborn to create insightful visualizations like histograms, scatter plots, and heatmaps to understand data distributions and relationships.

  • Implement Core Supervised Learning Algorithms: Build and train practical models for both classification (e.g., Logistic Regression, Decision Trees, Random Forests, KNN) and regression (e.g., Linear Regression).

  • Evaluate Your Models Effectively: Go beyond simple accuracy by learning to use crucial evaluation metrics like the Confusion Matrix, Precision, Recall, F1-Score, RMSE, and R-squared to assess your model's performance.

  • Apply Unsupervised Learning Techniques: Discover how to find hidden patterns in unlabeled data using clustering (K-Means) and simplify complex datasets with dimensionality reduction (PCA).

  • Optimize Your Machine Learning Workflow: Learn critical preprocessing steps like feature scaling and categorical encoding, and use Scikit-Learn Pipelines to streamline your model-building process.

  • Tune Models for Better Performance: Understand the difference between parameters and hyperparameters, and use Grid Search to systematically find the best settings for your models.

  • Build Two End-to-End Projects: Apply all the skills you've learned to build and evaluate two complete, real-world projects: a classification model to predict Titanic survival and a regression model to predict house prices.

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Publishing, VectorMind
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ISBN 13: 9798290381794
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Publishing, VectorMind
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ISBN 13: 9798290381794
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ISBN 13: 9798290381794
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Paperback. Condición: new. Paperback. Master the Fundamentals of Machine Learning: Grasp the core concepts of supervised, unsupervised, and reinforcement learning, and understand the complete machine learning workflow from problem definition to deployment.Set Up a Robust Python Environment: Learn how to install and configure essential libraries like Pandas, Scikit-Learn, NumPy, and Matplotlib in a virtual environment for your machine learning projects.Become Proficient in Data Handling with Pandas: Develop strong skills in loading, cleaning, preparing, and exploring tabular data using Pandas, including handling missing values, duplicates, and different data types.Visualize and Analyze Your Data: Use Matplotlib and Seaborn to create insightful visualizations like histograms, scatter plots, and heatmaps to understand data distributions and relationships.Implement Core Supervised Learning Algorithms: Build and train practical models for both classification (e.g., Logistic Regression, Decision Trees, Random Forests, KNN) and regression (e.g., Linear Regression).Evaluate Your Models Effectively: Go beyond simple accuracy by learning to use crucial evaluation metrics like the Confusion Matrix, Precision, Recall, F1-Score, RMSE, and R-squared to assess your model's performance.Apply Unsupervised Learning Techniques: Discover how to find hidden patterns in unlabeled data using clustering (K-Means) and simplify complex datasets with dimensionality reduction (PCA).Optimize Your Machine Learning Workflow: Learn critical preprocessing steps like feature scaling and categorical encoding, and use Scikit-Learn Pipelines to streamline your model-building process.Tune Models for Better Performance: Understand the difference between parameters and hyperparameters, and use Grid Search to systematically find the best settings for your models.Build Two End-to-End Projects: Apply all the skills you've learned to build and evaluate two complete, real-world projects: a classification model to predict Titanic survival and a regression model to predict house prices. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798290381794

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