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

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


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About the Book

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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Product Details
  • ISBN-13: 9798290381794
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 279 mm
  • No of Pages: 294
  • Returnable: N
  • Sub Title: Build Real-World Projects Using Scikit-Learn and Pandas
  • Width: 216 mm
  • ISBN-10: 8290381794
  • Publisher Date: 01 Jul 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 16 mm
  • Weight: 688 gr


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