Build high-impact ML/AI solutions by optimizing each stepKey Features● Build and fine-tune models for maximum performance. ● Practical tips to make your own state-of-the-art AI/ML models.

● ML/AI problem solving tips with multiple case studies to tackle real-world challenges. DescriptionThis book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance.

It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for deep learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques.

Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting. They will also benefit from deep learning by optimizing models for image processing, natural language processing, and specialized applications.

The reader can put theory into practice with hands-on case studies and code examples, reinforcing their understanding. With this book, the reader will be able to create high-impact, high-value ML/AI solutions by optimizing each step of the solution building process, which is the ultimate goal of every data science professional.

What you will learn● End-to-end solutions to ML/AI problems. ● Data augmentation and transfer learning.

● Optimizing AI/ML solutions at each step of development. ● Multiple hands-on real case studies.

● Choose between various ML/AI models. Who this book is forThis book empowers data scientists, developers, and AI enthusiasts at all levels to unlock the full potential of their ML solutions.

This guide equips you to become a confident AI optimization expert. Table of Contents1.

Optimizing a Machine Learning /Artificial Intelligence Solution2. ML Problem Formulation: Setting the Right Objective3.

Data Collection and Pre-processing4. Model Evaluation and Debugging5.

Imbalanced Machine Learning6. Hyper-parameter Tuning7.

Parameter Optimization Algorithms8. Optimizing Deep Learning Models9.

Optimizing Image Models10. Optimizing Natural Language Processing Models11.

Transfer Learning. .