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248031836
30 Must-Know AI Algorithms Explained: A Practical Guide to Machine Learning with Clear Explanations, Real-World Use Cases, and Runnable Python Code
Item #:
248031836
WST 169
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- Thirty algorithms. From I've heard of it to I can build it.If you've ever nodded along to talk of gradient boosting, embeddings, or Transformers while quietly wondering how they actually work — this book was written for you. 30 Must-Know AI Algorithms Explained takes the algorithms that power real products and dominate technical interviews and turns them from intimidating names into tools you genuinely understand.Each algorithm is unpacked the way a good mentor would explain it: the plain-English intuition first, then the mechanics, then exactly where it shines and where it quietly fails. Every one comes with clean, runnable Python you can open, execute, and adapt the same afternoon — plus the practical judgment of which settings matter and which mistakes to avoid.You'll journey across the full landscape of modern machine learning:Supervised learning — Linear & Logistic Regression, Decision Trees, Random Forests, SVMs, K-Nearest Neighbors, Naive Bayes, Gradient Boosting, AdaBoost, XGBoostUnsupervised learning — K-Means, Hierarchical Clustering, DBSCAN, PCA, t-SNEReinforcement learning — Q-Learning, SARSA, Deep Q-Networks, Policy Gradient, Actor-CriticDeep learning — Neural Networks, CNNs, RNNs, LSTMs, and the Transformers behind today's AIAdvanced techniques — K-Means++, Autoencoders, Isolation Forest, Markov Decision Processes, Genetic AlgorithmsWhat you'll gain:A clear mental model of how 30 core algorithms actually work — no heavy math requiredComplete, tested Python implementations you can reuse immediatelyThe judgment to choose the right algorithm for the problem in front of youAwareness of the pitfalls that trip up working practitionersGenuine readiness for technical interviewsEvery chapter follows the same clear structure — definition, how it works, where it's used, best practices, common mistakes, key hyperparameters, and a full implementation — so the book works equally well as a first course and a lifelong desk reference. Companion Jupyter notebooks, annotated diagrams, a quick-reference guide, a glossary, and a complete index round it out.Whether you're a student, a working engineer, or preparing for your next interview, this is the book that finally makes the algorithms click.
| Publisher | Independently published |
| Publication date | July 27, 2026 |
| Language | English |
| Print length | 305 pages |
| ISBN-13 | 979-8189380280 |
| Item Weight | 1.17 pounds (530 grams) |
| Dimensions | 6.24 x 0.91 x 9.24 inches (15.8 x 2.3 x 23.5 cm) |
Product Description
30 Must-Know AI Algorithms Explained: A Practical Guide to Machine Learning with Clear Explanations, Real-World Use Cases, and Runnable Python Code
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Customer Questions & Answers
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Is this book suitable for beginners?
Answer: Yes, it explains algorithms in plain English and requires no heavy math. -
Question:
What programming language is used for the examples?
Answer: The examples use Python, with runnable code provided in Jupyter notebooks. -
Question:
Can this book help with technical interviews?
Answer: Absolutely! It covers essential algorithms commonly discussed in interviews.
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Features & Benefits
- Discover 30 essential AI algorithms explained in simple terms.
- Learn through practical, runnable Python code for immediate application.
- Build confidence with clear explanations and best practices.
- Master core concepts without heavy math; ideal for beginners and professionals.
- Prepare effectively for technical interviews with in-depth knowledge.
- Enjoy a structured approach with comprehensive resources and support materials.
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