Being keen on artificial intelligence (AI) and interested in career progression or entry into a field is significant, but one of the best steps is getting ready to appear for interviews in companies such as Google, NVIDIA, or Microsoft through the best books to gain AI knowledge for interviews.
Although these organizations do npt consider just the code; they would want the applicants to know AI concepts along with machine learning, system design, and more specialized topics, such as NLP.
Whether you already work in AI or are ready to join, the right set of resources makes all the difference. This post will walk you through some of the books to gain AI knowledge for interviews you can use to build up your knowledge and get ready for those interviews. No, these are not textbooks – all these are weapons to help understand concepts, beat challenges, and stand out at technical interviews.
How to Build AI Knowledge: Practical Steps
1. Start with AI Fundamentals
However, to ace AI interviews, you must have a good grasp of the basics. Some common concepts include search algorithms, reasoning under uncertainty, and reinforcement learning. A good textbook that covers these topics is a great place to start.
Refer to: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
This is one of the best books to gain AI knowledge for interviews and are considered the “Bible of AI.” From the foundational search algorithms to advanced areas like robotics and probabilistic reasoning, this book covers it all. For newcomers to AI, this book sets the stage for understanding the area. However, even those who are already experienced in their profession will look at this for a quick revision of key concepts.
2. Deepen Your Knowledge of Machine Learning
Most AI roles center on machine learning, and the interviewers will challenge your understanding of algorithms, model training, and evaluation metrics. Resources balancing theory with practice will help you prepare better.
Refer to: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Aurélien Géron
This textbook will guide through the process by making engaging in doing projects a fun learning way as a do-it-yourself tool to train its users in using tasks for projects including classification and regression models by which the actual project works, plus how to practically put the acquired understanding of such machine learning algorithms and concept under application.
Refer to: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
For those looking to specialize in deep learning, this book is a must-read. It dives into neural networks, optimization techniques, and architectures like convolutional and recurrent neural networks. While it’s more technical, it’s incredibly rewarding if you’re preparing for advanced AI roles.
3. Learn System Design for AI Applications
System Design is another critical aspect of AI interviews, especially for firms dealing with massive applications. Here, you should be able to explain how to build scalable, efficient systems supporting the AI pipeline.
Refer to: Designing Data-Intensive Applications by Martin Kleppmann
This book is valuable in understanding the design of systems that manage massive datasets. Distributed systems, data modeling, and scalability are presented clearly and practically. If you are preparing for system design interviews, this book will help you approach questions with confidence.
Refer to: Machine Learning System Design Interview Ali Aminian
This book is focused particularly on system design for ML. It guides you through real-world examples, such as building recommendation systems, fraud detection pipelines, and predictive models. If you are targeting AI-specific roles, this is a great resource.
4. Explore Advanced Topics Like NLP
This is one of the most exciting areas of AI today is NLP. Companies use transformer models, such as BERT and GPT, for powering chatbots, recommendation engines, and many more applications. Knowing about these models may make you have a higher probability of winning the interviews.
Refer to: Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
Although, this provides a lot of hands-on application, from the start, towards the deployment of transformer models. Learn how to build and fine-tune models for NLP applications; it’s a good book for anyone interested in specializing or preparing for roles demanding such expertise.
Additional Tips for Preparation
1. Solve Real-World Problems:
Although, utilize Kaggle or Google Colab for working on AI projects. Something like creating a chatbot or a recommendation system will give you practical knowledge of what you learned.
2. Solve interview challenges
However, LeetCode and Educative are websites that provide AI-specific interview questions. Try to solve these as much as possible to improve your problem-solving skills.
3. Read case studies
In addition, look at how companies like Google manage billions of search queries or how NVIDIA designs those GPUs to be optimized for deep learning. These are inspirational examples of system design questions.
Books to Gain AI Knowledge for Interviews:
In addition here’s a quick list of books to keep handy during your preparation:
1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
For AI fundamentals.
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
For practical machine-learning projects.
3. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
To master neural networks and deep learning.
4. Designing Data-Intensive Applications by Martin Kleppmann
To understand system design at scale.
5. Machine Learning System Design Interview by Ali Aminian
For AI-focused system design preparation.
6. Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
For building NLP expertise with modern tools.
You can also check our blog on Mastering AI in Finance with These 5 Must-Read Books
Conclusion
Whether you are an AI professional or in the process of starting your journey, preparing for interviews like Google, NVIDIA, and Microsoft requires you to have a mixture of theoretical knowledge, practical experience, and problem-solving skills. These best books to gain AI knowledge for interviews and tips will give you excellent examples to base your foundation on. Refer to these books, practice accordingly, and always remain curious. The fields of AI keep evolving; yours has just begun.
Good luck!
FAQ’s
Beginners: Spend 3–6 months focusing on foundational AI concepts, coding, and basic system design.
Intermediate learners: Dedicate 2–4 months reviewing advanced topics, solving interview-style questions, and building real-world projects.
Experienced professionals: Focus on refining your knowledge and interview skills over 1–2 months by practicing mock interviews and revisiting advanced concepts.
Start by:
1. Using platforms like Kaggle or Google Colab to work on datasets.
2. Building small projects, such as a chatbot, image classifier, or recommendation system.
3. Recreating case studies from books like Designing Data-Intensive Applications or Hands-On Machine Learning to connect theory to practice.
Although, you don’t need to cover every single topic just focus on the most relevant areas for the role you’re applying for:
1. For machine learning roles, prioritize understanding algorithms, model evaluation, and deployment.
2. For system design interviews, focus on scalability, data pipelines, and distributed systems.
3. For NLP roles, dive deep into transformers and sequence-to-sequence models.
Yes, many of these books are beginner-friendly:
1. Artificial Intelligence: A Modern Approach
2. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
In addition to books, consider these resources:
1. Online courses like Andrew Ng’s Machine Learning or Deep Learning Specialization on Coursera.
2. Platforms like LeetCode and Educative for solving AI and system design interview questions.
3. Blogs and research papers from Google AI, Microsoft Research, and NVIDIA to stay updated with the latest advancements.
