Machine learning books often cover many algorithms, including game development, system administration, threat detection, security applications, etc. So, deepening and consolidating your knowledge through books is essential.
Yet, not all books are perfect for your needs and uses. How to pick the right one for your level? What criteria should you prioritize? You need to choose the right product for the area you are interested in or the project you are working on.
No matter where you are in your career, learning, and honing your knowledge and skills from books is essential. They provide helpful information about the applications of computer science and programming.
Keep reading for the best recommendations I have for you!
1. The Hundred-Page Machine Learning Book
Specification
- Language: English
- Paperback: 160 pages
- Item Weight: 13.8 ounces
- Dimensions: 7.5 x 0.38 x 9.25 inches
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Reason To Buy
- High-quality color printing
- Many useful machine-learning approaches
- Chapters are informative and accessible to newbies
Review
I recommend this Hundred Page Machine Learning Book to anyone starting to manage a team of data scientists.
It covers various topics, explaining everything about the implementation method and providing effective practical exercises. It is a tremendous competitive advantage when compared to other products.
After reading this book, I understand the essential math and algorithms in the IT field. The book’s content is well-organized, intuitive, and easy to read. It surpassed the books I had read before when researching computers and AI.
2. Hands-On Machine Learning
Specification
- Language: English
- Paperback: 856 pages
- Item Weight: 2.8 ounces
- Dimensions: 7 x 1.2 x 9.2 inches
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Reason To Buy
- High use value
- Good paper quality
- Easy to follow and absorb knowledge
- Good explanation and dive into topic exploration
Review
If you are looking for a book that provides total value on the core of Machine Learning, Hands Machine Learning Scikit Learn TensorFlow is a solid choice.
It covers many of the topics of ML and comes with compelling and easy-to-understand explanatory examples. It helps me to explore every Python and ML code. Plus, it also offers suggestions for advanced topics, which is very effective when you want to improve your craft.
Yet, it isn’t suitable if you are new. You will find it difficult to understand because it needs an in-depth explanation like math.
Last but not least, it taught me many advanced values , including custom modeling, optimizer setup, and Tensorflow tools.
This one also taught me everything related to linear regression and logistics, so I am proficient in Deep Learning and ready to do well on any project.
3. Designing Machine Learning Systems
Specification
- Language: English
- Paperback: 386 Pages
- Item Weights: 1.36 pounds
- Dimensions: 7 x 0.75 x 9 inches
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Reason To Buy
- Many good algorithms
- Good content organization
- Service quality and preferential policies
- Good balance between theory and explanatory examples
Review
Designing Machine Learning Systems Production Ready is ideal for those who are looking to learn or focus on studying algorithms.
Good organization is a big plus. I quickly grasp the knowledge and apply everything in it to the real world in a simple way.
The example system is easy to understand and has a bit of humor, so you or anyone will not get bored reading it like you would with other books.
This book is a perfect resource for tools used in science and technology. It is helpful for building and understanding techniques and algorithms for ML.
The only minus is that numeric data has no color. I need help to see the units of the algorithm. Fortunately, the manufacturer also provides a PDF copy, so I can preview the numbers whenever possible.
4. Machine Learning Bookcamp
Specification
- Language: English
- Paperback: 472 pages
- Item Weights: 1.85 pounds
- Dimensions: 7.38 x 0.6 x 9.25 inches
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Reason To Buy
- Outstanding Resources
- Easily accessible to beginners
- Many useful examples
Review
The Machine Learning Bookcamp made a big impression on me with its packaging design and superb paper and print quality.
As a meticulous person, I always place high demands on durability in any product, and significantly, this book meets most of my criteria.
As for the content value, it is what I was looking for in how to build my projects. It taught me how to use popular tools like Python, Scikit-Learn, NumPy, and TensorFlow. Essential insights that help me collect and clean all types of data for my training model and projects I’m working on.
I also like that this book has enough theoretical knowledge and illustrative examples to go with it. Hence, you or anyone will quickly grasp all the great information from it, even just starting in this field.
5. AI and Machine Learning for Coders
Specification
- Language: English
- Paperback: 392 pages
- Item Weight: 1.9 pounds
- Dimensions: 6.93 x 1.18 x 8.98 inches
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Reason To Buy
- Tensorflow details
- Exemplary topic arrangement
- Support to handle many problems
- Easy-to-understand explanation
Review
While learning about AI, I realized I needed more than the basic knowledge I learned in college. I picked up the book Machine Learning Coders Programmers Intelligence to learn more about coding and how to use TensorFlow.
I love everything this book offers, especially the neural network mx+b example.
Their arrangement is very intuitive and easy to understand. I also address my concerns through JavaScript examples and transfer learning.
Most AI experts will either read through or stick with this book for a long time because of its excellent knowledge. Finally, I highly recommend it if you are also worried about understanding technology and artificial intelligence.
6. Machine Learning with PyTorch and Scikit-Learn
Specification
- Language: English
- Paperback: 774 pages
- Item Weights: 2.88 pounds
- Dimensions: 7.5 x 1.75 x 9.25 inches
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Reason To Buy
- High practical value
- Excellence in technical details
- Extensive library, abundant resources
- Clear and easy-to-understand explanation
Review
I chose Machine Learning PyTorch Scikit Learn for ML research in Python and the modern TensorFlow system. It taught me essential skills that have given me great opportunities for career advancement.
This book gives a more in-depth illustration of standard systems work and presents many practical coding examples. Therefore, I have deeply understood many algorithms in building models and real projects.
Its outstanding advantage is the clear and understandable explanation related to Transformers. This book allows me to learn with models and big data files from the Hugging Face library system.
7. The Kaggle Book
Specification
- Language: English
- Paperback: 534 pages
- Item Weights: 2 pounds
- Dimensions: 7.5 x 1.21 x 9.25 inches
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Reason To Buy
- Glossy and smooth pages
- Excellent quality prints
- Good price
- Engaging and rich content
Review
The Kaggle Book taught me how to use Kaggle, an important stepping stone to good development in AI and ML.
It covers everything interesting about Kaggle, such as datasets, notebooks, data mining techniques, and new career opportunities. I appreciate that the book goes into advanced courses.
Specifically, it taught me how to use hyperparameters and Bayesian optimization, which are most helpful in extracting value combinations for a project I’m working on.
If you want to get off to a good start when entering contests on Kaggle, this book is the ideal investment in my recommendation.
8. Mathematics for Machine Learning
Specification
- Language: English
- Paperback: 398 pages
- Item Weights: 1.76 pounds
- Dimensions: 7 x 0.92 x 10 inches
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Reason To Buy
- Lightweight
- Great content value
- Flexible, consistent, and intuitive theme
- High-quality prints
Review
Weight is one of many factors you must consider when purchasing. If you often have to carry books out, you need them to be lighter and more compact.
So, if weight is your thing, Mathematics for Machine Learning is an excellent choice. At 398 pages, it only weighs about 1.7 pounds, much lighter than the books I’ve had before.
Diverse content is a big plus. It’s perfect for bridging the gap between foundational theoretical knowledge and machine learning code implementations at a granular level. I found myself better at math reading this book, which is expected of any programmer.
This book’s content is concise but provides enough detail to cover the most essential and valuable parts.
9. Machine Learning Design Patterns
Specification
- Language: English
- Paperback: 408 pages
- Item Weights: 1.45 pounds
- Dimensions: 9.06 x 0.94 x 6.85 inches
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Reason To Buy
- Easy to read and understand
- It offers lots of great ideas
- Many suitable logical thinking methods
- A comprehensive description of concepts and applications
Review
I became a professional C++ and Java software engineer thanks to Machine Learning Design Patterns. It has helped me identify and fill in the gaps in statistical terminology in my knowledge base.
So I can infer better quantifiers after reading them for the first time. It also taught me how to use the necessary tools in creating machine learning models.
This book lists the different data types and coherently presents the problems most likely to be encountered while setting up and developing the model.
I like this book’s structure, which is easy to read and understand, even for beginners. So, it became the perfect choice for my ML workflows.
10. Machine Learning Design Interview
Specification
- Language: English
- Paperback: 210 pages
- Item Weights: 11.8 ounces
- Dimensions: 6 x 0.48 x 9 inches
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Reason To Buy
- Good price
- Lightweight
- Many practical examples
- Easy to understand ML systems
Review
The Machine Learning Design Interview is a good choice if your top goal is to pass ML interviews. It provides detailed and vital information about the problems you may encounter when interviewing at a large company.
It covers ML system design, design patterns, data processing methods, real-life models, and more. Most impressively, it comes with many practical examples, allowing me to familiarize myself with and grasp every situation during my work.
I recommend updating your knowledge regularly to work more efficiently. The various arXiv and tech blog sources it covers may change over time.
Buying Guide
Collecting data
Collecting data is of prime importance, as it involves finding the patterns you’re working on. Also, the data quality you provide determines and guides the accuracy of the final result.
So I recommend looking at reliable data sources and ensuring they represent different categories and values well.
Preparing the data
You need more than just finding high-quality data sources to succeed; it is also essential to organize and process them appropriately and effectively. Therefore, you may want to consider the following:
- Aggregate and distribute data evenly
- Clean data to remove unnecessary risks
- Split data into different subsets
Choosing a model
Choosing the proper sample is essential. The best options are templates that include speech, image, and predictive features that match your model-building and development goals. Finally, check that the data type is compatible with the model to ensure the best machine learning efficiency.
FAQs
Can I learn AI and ML on my own?
The short answer is yes. AI knowledge is more complex and challenging than some programming languages like Python. Yet, you can ultimately conquer it with the best self-study resources.
What are the best research papers or books for machine learning?
The best research papers or books for machine learning include:
- The Hundred-Page Machine Learning Book
- Machine Learning for Hackers
- Data Analysis Machine Learning
- The Elements of Statistical Learning
Is Raspberry Pi enough for machine learning?
The truth is yes. Raspberry Pi 4 Model B is one of the new and popular models for machine learning applications. It has a very efficient item classification system, image templates, and voiceovers.
How much RAM do you need for AI?
You need at least 16GB of RAM for the smooth operation of AI. If it is less than this number, you will have many problems while building accurate models.
Is ML or AI better for data science?
It is difficult to say that ML or AI is better for data science because each has its advantages and is suitable for each specific use:
AI is the perfect choice to solve complex problems and let the system run smoothly. Meanwhile, ML provides super-precision output information flow.
Final Thoughts
As the technology industry has made significant advances and achievements, the demand for mechanical engineers and data scientists have also increased significantly.
If you want to create a more competitive edge and impress employers, check out the list above to hone your knowledge.
From my point of view, the Hundred Page Machine Learning Book is still a must-read this year to stay up to date with the latest knowledge in the field.
My above news and suggestions have given you a great reference value. Through that, you will provide better direction and preparation for your career.