Master Optimization in Machine Learning – The Complete Practical Guide with Python

Professional and Marketing Summary

Book Title: Optimization for Machine Learning: Finding Function Optima with Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery (2021–2023, Edition v1.05)
Language: English
Access: Official resale rights held by Big Data Consult, with lifetime access via www.bigdataconsult.fr

1. Author Overview

Jason Brownlee is the founder of Machine Learning Mastery, a globally recognized educational platform for data science and AI practitioners.
He is known for his hands-on, code-driven books that simplify complex concepts in Machine Learning and Deep Learning for engineers and developers.

2. Learning Focus

This book presents optimization as the mathematical and algorithmic foundation of all Machine Learning models — the process of minimizing loss functions to achieve the best-performing models.
It explains both gradient-free and gradient-based optimization methods and shows how to implement them effectively in Python.

3. Content Highlights

Core Topics Covered:

• Foundations of Optimization: Objective functions, candidate solutions, local vs. global optima, and hyperparameter trade-offs.

• Gradient-Free Methods: Random Search, Grid Search, Hill Climbing, Simulated Annealing, and Evolutionary Algorithms.

• Gradient-Based Methods: Gradient Descent and its advanced variants — Momentum, AdaGrad, RMSProp, Adadelta, and Adam.

• Practical Implementation: Applying these techniques to real-world ML tasks for model fitting and convergence improvement.

4. Target Audience

• Machine Learning Practitioners seeking to master model training beyond prebuilt libraries.

• Software Engineers & Developers needing deeper insight into optimizers.

• Data Scientists & Analysts optimizing model performance and convergence speed.

• Students in AI and Applied Math looking for a practical, code-centered learning resource.

5. Why This Book Matters

• Demystifies Model Training: Explains the core process behind how ML models learn.

• Builds Technical Mastery: Enables readers to tune optimizers intelligently instead of relying on defaults.

• Long-Term Value: Optimization is a universal skill applicable across all ML frameworks and architectures.

• Code-Oriented Learning: Each concept is reinforced through Python examples and visual demonstrations.

6. Conclusion

Optimization for Machine Learning bridges the gap between mathematical theory and coding practice.
It equips readers with the skills to analyze, implement, and fine-tune optimization algorithms used in modern AI systems.
Through this book, engineers and data scientists develop the confidence to design models that converge faster and perform better.

Big Data Consult Official Message

Big Data Consult is the authorized reseller of this publication.
All students purchasing through our platform enjoy lifetime access to the complete book and its learning resources via www.bigdataconsult.fr.