{"product_id":"ai-algorithms-with-python-classic-algorithms-from-k-nearest-neighbors-to-reinforcement-learning","title":"AI Algorithms with Python: Classic Algorithms from K-Nearest Neighbors to Reinforcement Learning","description":"\u003cdiv id=\"product-short-text\" itemprop=\"description\"\u003eWhat really happens when you call \u003cem\u003emodel.fit()\u003c\/em\u003e? Open the black box of machine learning and find out for yourself by building classic AI algorithms using this practical guide! You’ll implement k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, k-means clustering, support vector machines, neural networks, and reinforcement learning—all from scratch in Python. Apply each method to real data, measure its performance, and then deliberately break it to discover its limits!\u003c\/div\u003e\n\u003cdiv id=\"product-usps\"\u003e\n\u003cul class=\"usp\"\u003e\n\u003cli\u003e\n\u003cdiv\u003eImplement classic machine learning and AI algorithms with Python from scratch\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eWork with k-nearest neighbors, Naïve Bayes classifiers, linear regression, decision trees, and more\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eTest the limits of each algorithm with hands-on exercises\u003c\/div\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eIn this book, you'll learn about:\u003c\/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ch3\u003e\u003cstrong\u003eMath and Data Science Foundations\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eGet an intro to the math you need—linear algebra, statistics, probability, and gradient descent—explained with code instead of proofs. Then learn the data science workflow: clean and explore data with pandas, engineer features, and evaluate models with metrics such as accuracy, precision, recall, and RMSE.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003ch3\u003e\u003cstrong\u003eAlgorithms from Scratch\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eWork through one algorithm at a time, from k-nearest neighbors and Naïve Bayes to support vector machines, neural networks, and Q-learning. Build each one in simple Python, apply it to real data in Jupyter Notebook, and then implement it professionally with scikit-learn.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003ch3\u003e\u003cstrong\u003eLimitations and AI Ethics\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003ePush each algorithm into situations it can’t handle—outliers, poor scaling, imbalanced classes, overfitting—to find out exactly why it fails. Understand the responsibility that comes with building AI models, and learn about bias, transparency, and the importance of keeping a human in the loop.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003ch2\u003eHighlights include\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv\u003eMathematics for machine learning\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eData science and evaluation metrics\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003ek-nearest neighbors (kNN)\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eNaïve Bayes\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eLinear and logistic regression\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eDecision trees\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003ek-means clustering\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eSupport vector machines (SVMs)\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eNeural networks\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eReinforcement learning\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003ePygame simulations\u003c\/div\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003eAI and ethics\u003c\/div\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAbout the Author\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eJens Gaulke\u003c\/strong\u003e is a computer scientist who has worked in information security at Arvato Systems for more than a decade, focusing on information security management, business continuity management, ISO standards, cloud computing, and AI. He also leads the company’s security awareness training. Before that, he spent 14 years teaching programming, game development, networking, databases, IT security, and software engineering at bib International College in Paderborn, Germany. His books cover game programming, machine learning, cryptography, and computer science—always in Python, and always with a focus on practice.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"SAP Press (Rheinwerk Publishing)","offers":[{"title":"Paperback","offer_id":50817710096454,"sku":"9781493229161","price":5019.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50817710129222,"sku":"9781493229178","price":5416.0,"currency_code":"INR","in_stock":true},{"title":"Combo","offer_id":50817710161990,"sku":"9781493229185","price":6894.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/Screenshot2026-10-24122258.png?v=1792824800","url":"https:\/\/cb-india.com\/products\/ai-algorithms-with-python-classic-algorithms-from-k-nearest-neighbors-to-reinforcement-learning","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}