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ML for Beginners Simplifying Complex Algorithms: A beginner's guide to understanding and applying ML concepts by Isandro Myles

ML for Beginners Simplifying Complex Algorithms: A beginner's guide to understanding and applying ML concepts

by Isandro Myles
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Book cover type: Paperback
  • ISBN13: 9798264521416
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 154
  • Original Price: USD 16.99
  • Language: English
  • Edition: N/A
  • Item Weight: 214 grams
  • BISAC Subject(s): Programming / Algorithms

Start your machine learning journey with confidence.

In ML for Beginners, you'll learn how to break down complex machine learning (ML) algorithms into simple, easy-to-understand concepts. This beginner-friendly guide is designed for anyone looking to get started with machine learning, even if you have no technical background. Through clear explanations and hands-on examples, you'll master the fundamentals of ML and apply them to real-world problems.

Inside, you'll discover how to:

  • Understand key ML concepts: from supervised vs. unsupervised learning to classification, regression, and clustering.

  • Explore popular algorithms: get a grasp of decision trees, k-nearest neighbors (KNN), linear regression, and support vector machines (SVM).

  • Implement your first ML models using Python and popular libraries like scikit-learn and pandas.

  • Preprocess data: handle missing values, normalize data, and encode categorical variables for model readiness.

  • Build and evaluate models: measure accuracy, precision, recall, F1 score, and confusion matrices.

  • Understand overfitting vs. underfitting and use techniques like cross-validation to improve model performance.

  • Dive into unsupervised learning and clustering with algorithms like k-means and hierarchical clustering.

  • Apply regression techniques to predict continuous values, such as stock prices or housing costs.

  • Implement real-world projects for practical ML applications in customer segmentation, recommendation systems, and predictive analytics.

  • Use model selection and hyperparameter tuning to improve performance and make your models more accurate.

  • Gain insights into the future of machine learning with an introduction to deep learning and neural networks.

Packed with step-by-step examples, easy-to-follow tutorials, and real-world datasets, this book helps you take the first step toward becoming a confident ML practitioner.

Who This Book Is For

  • Beginners with no prior programming or data science experience

  • Aspiring data scientists and ML enthusiasts eager to understand machine learning concepts

  • Students looking for an approachable introduction to ML algorithms

  • Developers wanting to learn how to implement machine learning into real-world applications

  • Non-technical readers who want to gain a solid understanding of ML without the jargon

Understand and apply machine learning concepts with clarity, and start building your own intelligent systems today.

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