Machine Learning with Python for Beginners (Paperback)

Vectormind Publishing

ISBN 13: 9798290381794

Verlag: Independently Published, 2025

Beschreibung

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.

Bestandsnummer des Verkäufers 9798290381794

Bibliografische Details

• Titel: Machine Learning with Python for Beginners (Paperback)
• Verlag: Independently Published
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-13: 9798290381794
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

• 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.