Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
AI Algorithms with Python: Classic Algorithms from K-Nearest Neighbors to Reinforcement Learning

AI Algorithms with Python: Classic Algorithms from K-Nearest Neighbors to Reinforcement Learning

Gaulke, Jens

Paperback

Programming

Publisher Price: $59.95

PREORDER - Expected ship date February 24, 2027

ISBN10: 1493229168
ISBN13: 9781493229161
Publisher: Rheinwerk Computing
Published: Feb 24 2027
Pages: 500
Language: English

What really happens when you call model.fit()? 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!

a. Math and Data Science Foundations Get 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.

b. Algorithms from Scratch Work 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.

c. Limitations and AI Ethics Push 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.

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