Optimizing AI and Machine Learning Solutions

This 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.

KEY 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.

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 FOR

This 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.

1. Optimizing a Machine Learning /Artificial Intelligence Solution
2. ML Problem Formulation: Setting the Right Objective
3. Data Collection and Pre-processing
4. Model Evaluation and Debugging
5. Imbalanced Machine Learning
6. Hyper-parameter Tuning
7. Parameter Optimization Algorithms
8. Optimizing Deep Learning Models
9. Optimizing Image Models
10. Optimizing Natural Language Processing Models
11. Transfer Learning

Since 2009, Mirza Rahim Baig has been exploring, practicing, learning, and teaching all things machine learning/artificial intelligence. He is a seasoned data science expert and renowned thought leader. Rahim is adept at solving complex business problems using AI/ML in a career spanning multiple domains and geographies. Numerous job titles aside, his focus has always been using data science to solve business problems and create high impact.

Rahim is also an author, a speaker and educator. In addition to this book, he has authored two well received books The Deep Learning Workshop and Data Science for Marketing Analytics. In all his titles, Rahim focuses on application and outcomes, and shares best practices derived from his experience. A key feature of his work is the approach of focusing not merely on model building but on the entire end-to-end solution building process. The explanations are easy to follow, even for non-technical learners, thanks to his style developed over years of teaching.

Rahim is a renowned subject matter expert in data science topics, having worked with most of the popular ed-tech platforms in designing courses and delivering content for master’s level programs. He is a visiting faculty at NMIMS for MBA programs. In addition to having been published multiple times, Rahim speaks on various platforms about data science.