From Models to Mastery: Building Classical & Deep Learning Systems with PyTorch, Scikit-Learn, and Python

By Alira Vexel

From Models to Mastery: Building Classical & Deep Learning Systems with PyTorch, Scikit-Learn, and Python
Unlock the Full Power of Machine Learning - From Foundational Algorithms to Modern LLMs, Vision Transformers, and Production-Ready MLOps
Are you ready to elevate your machine learning expertise and master modern AI development with the most trusted Python tools in the industry?
From Models to Mastery is your comprehensive guide to designing, building, and deploying powerful machine learning and deep learning systems using PyTorch 2.x, Scikit-Learn 1.7+, and the latest open-source tools shaping the ML landscape in 2025 and beyond.
Whether you're a data scientist, ML engineer, AI researcher, or software developer transitioning into ML, this book empowers you with a complete, end-to-end roadmap - from reproducible classical models to cutting-edge LLM fine-tuning, graph learning, vision systems, and scalable MLOps workflows.
What You'll LearnSet up a modern ML environment using Poetry, Conda, CUDA, and GPU acceleratorsPerform clean, ethical data wrangling using Pandas, Polars, Great Expectations, and DVCMaster feature engineering with Featuretools, Feast, Autoencoders, and Dimensionality ReductionBuild robust models using Logistic Regression, SVMs, XGBoost, k-NN, and Na ve BayesDive deep into PyTorch with tensor operations, torch.compile, and PyTorch LightningCreate and evaluate CNNs, Transformers, GANs, Diffusion Models, and Graph Neural NetworksFine-tune LLMs using LoRA, QLoRA, PEFT, and deploy RAG pipelines with Hugging Face TransformersOptimize and track models using Optuna, MLflow, and Ray TuneServe models via FastAPI, BentoML, Triton, and deploy to edge with TensorRT, ONNX, and TinyMLImplement real-world monitoring with Evidently AI, Prometheus, Model Cards, and ISO/EU complianceBuilt for the 2025 AI Stack
This book is completely modernized with: PyTorch 2.x + torch.compileScikit-Learn 1.7+, Polars, PEFT, Flash Attention 3Graph Transformers, ControlNet, RLHF/DPOFeast, Kubeflow, Ray, Argo, GitHub Actions, and moreWho This Book Is ForAspiring ML practitioners seeking a rigorous, modern foundationSoftware engineers transitioning into AI rolesData scientists and AI engineers looking to scale their skillset with LLMs, MLOps, and production workflowsEducators and researchers in need of a single, trusted reference across classical and deep learningTake Your ML Journey From Idea to Inference
Whether you're solving tabular classification problems, fine-tuning transformers for NLP, building vision systems, or deploying fast inference pipelines to the cloud and edge-this book will help you bridge the gap between academic knowledge and real-world ML engineering mastery.
If you're serious about mastering both classical and modern machine learning systems, this is the only book you'll need.
Grab your copy now and transform how you build, deploy, and scale intelligent systems with Python.

• Format: Paperback
• Language: English
• ISBN: B0FHHWCLJB
• ISBN13: 9798292377696
• Release Date: July 2025
• Publisher: Independently Published
• Length: 576 Pages
• Weight: 2.90 lbs.
• Dimensions: 1.2" x 8.5" x 11.0"