Hands-on Machine Learning For Beginners: Implementing Models with Python, Scikit-Learn, and TensorFlow - Tapa blanda

Sinopsis

You see “Machine Learning” and “Neural Networks” in every job description, but how do you go from reading headlines to writing code? While others talk about algorithms and models, you’re looking for the first practical step—the one that skips the abstract theory and gets right to the keyboard. This book is that step.

It was written to be the guide you can actually use, closing the gap between curiosity and capability. No Ph.D. in advanced mathematics required. This is a project-based, hands-on-the-code guide designed to make you a practitioner, not just an observer.

🔍 You’ll learn how to:

• Set up a professional Python environment and master the essential tools: Pandas, Scikit-Learn, and TensorFlow.
• Clean and prepare messy, real-world data—the single most important skill in machine learning.
• Build your first predictive models from scratch, from simple Linear Regression to a robust Random Forest.
• Honestly evaluate your models with industry-standard techniques like cross-validation and confusion matrices to avoid the common trap of overfitting.
• Make the leap to deep learning by building your first Neural Network with Keras.
• Build and train a Convolutional Neural Network (CNN) that can actually “see” and classify images of handwritten digits with stunning accuracy.

• Aspiring Data Scientists who need a practical, project-based introduction to the field.
• Developers and Programmers looking to add one of the most in-demand skills to their toolkit.
• Ambitious Students and Self-Learners who are tired of theory and want to start building a real portfolio.
• Anyone who wants to stop just reading about machine learning and start doing it.

"Sinopsis" puede pertenecer a otra edición de este libro.