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Machine Learning with Python [Karforma] - mklabgr - 08-30-2026
Machine Learning with Python: Neural Networks, Algorithms, Deep Learning
Authors: Sunil Karforma, Subhajit Adhikari, Indrajit De, Siddhartha Bhattacharyya, Jan Platos
Publication date: July 2026
Publisher: De Gruyter
Series: De Gruyter Textbook
Pages: 358
ISBN: 978-3-11-914470-4 (print); 978-3-11-221739-9 (eBook)
Summary:
Machine Learning with Python is a broad introductory-to-intermediate textbook designed to take the reader from basic Python programming through modern machine learning, neural networks, large language models, and generative AI. It begins with the main paradigms of supervised, unsupervised, and reinforcement learning, while also introducing the practical Python ecosystem—Jupyter, Google Colab, Anaconda, Kaggle, NumPy, Pandas, Scikit-learn, and Matplotlib. A substantial section is devoted to preparing real datasets: missing values, outliers, normalization, standardization, categorical encoding, feature engineering, feature selection, and training/test splitting. The classical machine-learning chapters then cover linear and polynomial regression, logistic regression, decision trees, random forests, K-nearest neighbors, support-vector machines, Naive Bayes, K-means and hierarchical clustering, PCA, and introductory reinforcement learning.
The book places considerable emphasis on evaluating and improving models, explaining accuracy, precision, recall, $F_1$ score, ROC-AUC, cross-validation, Grid Search, Randomized Search, Bayesian optimization, overfitting, underfitting, and $L_1$/$L_2$ regularization. It then moves into deep learning, covering the perceptron, feed-forward networks and backpropagation, CNNs, MLPs, RBF networks, RNNs, LSTMs, GRUs and deep belief networks, with TensorFlow, Keras and PyTorch alongside tools such as XGBoost and LightGBM. This gives the reader a reasonably complete progression from traditional statistical learning techniques to contemporary neural-network architectures.
The final part expands beyond conventional ML into NLP, large language models, generative AI and real-world applications. NLP topics include tokenization, stemming, text classification and sentiment analysis. Separate chapters discuss LLMs and generative-AI systems, followed by big-data concepts and applications in agriculture, biological computation, e-commerce, healthcare, education and security. The book therefore functions less as a narrowly specialized treatment of machine-learning theory and more as a wide-ranging survey and practical course covering the modern AI landscape. It appears particularly suitable for students who know some programming but want one volume connecting Python, data preprocessing, classical ML, deep learning, NLP and newer generative-AI technologies.
Key takeaways
- Broad coverage: progresses from Python fundamentals and data preparation to classical ML, deep learning, NLP, LLMs and generative AI.
- Practical orientation: emphasizes Python libraries, model implementation, preprocessing, evaluation and hyperparameter optimization rather than purely mathematical theory.
- Modern scope: unusually for an introductory textbook, entire chapters are devoted to large language models and generative AI.
- Best suited for: undergraduate or graduate students, programmers, and newcomers seeking a structured overview of the contemporary machine-learning ecosystem rather than an advanced theoretical treatment.
De Gruyter book page