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Machine Learning for Beginners: A Complete Guide to Supervised and Unsupervised Learning with Python: Master Regression, Classification, Decision Trees, ... Series – Learn. Build. Master. Book 9)
Master Machine Learning and Build Production-Ready AI Models with Python
Machine Learning for Beginners is your comprehensive guide to building real-world AI systems using industry-standard tools. This book bridges theory and practice, teaching you to develop, evaluate, and deploy machine learning models professionally.
What's Inside
Learn machine learning fundamentals including supervised and unsupervised learning, proper model evaluation, and the iterative mindset essential for success. Master regression techniques from linear models through advanced regularization methods including Ridge, Lasso, and ElasticNet for feature selection and handling non-linear patterns.
Progress to classification algorithms including logistic regression with probability outputs, decision trees with visual interpretability, random forests demonstrating ensemble learning power, and XGBoost with competition-winning techniques. Explore unsupervised learning through K-Means clustering for customer segmentation and Principal Component Analysis for dimensionality reduction.
Develop professional practices including systematic model comparison, hyperparameter tuning with grid and random search, and complete end-to-end project workflows from business problem through deployment with documentation.
Practical Projects Included
Build house price predictors, customer churn classifiers, fraud detection systems, sales forecasters, customer segmentation models, and a portfolio-ready employee attrition prediction system with deployment code and professional documentation.
Industry-Standard Tools
Master scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, and Seaborn. All code runs in Jupyter Notebooks, Google Colab, or local Python environments. Complete GitHub repository included.
Who This Book Is For
Aspiring data scientists, analysts expanding technical skills, software developers adding ML capabilities, and professionals wanting to understand AI applications. Requires basic Python knowledge. No advanced mathematics needed.
Unique Approach
Each concept includes intuitive explanations before mathematics, complete working code, real-world business context, visual demonstrations, and common pitfall warnings. Learn proper evaluation metrics, systematic algorithm selection, feature engineering, deployment strategies, and professional documentation practices.
Address practical challenges including missing values, imbalanced classes, data leakage prevention, feature scaling, and production deployment. Understand not just how algorithms work, but when and why to use each technique.
Career Development
Includes guidance on data scientist versus ML engineer roles, portfolio building with GitHub best practices, Kaggle competition strategies, interview preparation, and career pathways in this rapidly growing field.
What You'll Achieve
Fundamental machine learning skills applicable across industries, portfolio projects demonstrating capabilities, systematic model development approaches, understanding of algorithm selection, and confidence to explore advanced topics including deep learning and natural language processing.
Machine learning expertise opens doors to high-demand careers in data science, artificial intelligence, and business analytics with median salaries exceeding six figures. This book provides the practical foundation for professional success.
Start building production-ready machine learning models today.
Machine Learning for Beginners is your comprehensive guide to building real-world AI systems using industry-standard tools. This book bridges theory and practice, teaching you to develop, evaluate, and deploy machine learning models professionally.
What's Inside
Learn machine learning fundamentals including supervised and unsupervised learning, proper model evaluation, and the iterative mindset essential for success. Master regression techniques from linear models through advanced regularization methods including Ridge, Lasso, and ElasticNet for feature selection and handling non-linear patterns.
Progress to classification algorithms including logistic regression with probability outputs, decision trees with visual interpretability, random forests demonstrating ensemble learning power, and XGBoost with competition-winning techniques. Explore unsupervised learning through K-Means clustering for customer segmentation and Principal Component Analysis for dimensionality reduction.
Develop professional practices including systematic model comparison, hyperparameter tuning with grid and random search, and complete end-to-end project workflows from business problem through deployment with documentation.
Practical Projects Included
Build house price predictors, customer churn classifiers, fraud detection systems, sales forecasters, customer segmentation models, and a portfolio-ready employee attrition prediction system with deployment code and professional documentation.
Industry-Standard Tools
Master scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, and Seaborn. All code runs in Jupyter Notebooks, Google Colab, or local Python environments. Complete GitHub repository included.
Who This Book Is For
Aspiring data scientists, analysts expanding technical skills, software developers adding ML capabilities, and professionals wanting to understand AI applications. Requires basic Python knowledge. No advanced mathematics needed.
Unique Approach
Each concept includes intuitive explanations before mathematics, complete working code, real-world business context, visual demonstrations, and common pitfall warnings. Learn proper evaluation metrics, systematic algorithm selection, feature engineering, deployment strategies, and professional documentation practices.
Address practical challenges including missing values, imbalanced classes, data leakage prevention, feature scaling, and production deployment. Understand not just how algorithms work, but when and why to use each technique.
Career Development
Includes guidance on data scientist versus ML engineer roles, portfolio building with GitHub best practices, Kaggle competition strategies, interview preparation, and career pathways in this rapidly growing field.
What You'll Achieve
Fundamental machine learning skills applicable across industries, portfolio projects demonstrating capabilities, systematic model development approaches, understanding of algorithm selection, and confidence to explore advanced topics including deep learning and natural language processing.
Machine learning expertise opens doors to high-demand careers in data science, artificial intelligence, and business analytics with median salaries exceeding six figures. This book provides the practical foundation for professional success.
Start building production-ready machine learning models today.
417 pages, Kindle Edition
Published November 16, 2025
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