Books

Learn how to containerize applications with Docker, automate deployments using GitHub Actions, deploy Python applications to the cloud, integrate AWS S3, monitor applications with the ELK Stack, and build Slack bots for deployment notifications.Master industry-standard tools including Docker, GitHub Actions, AWS, Heroku, Elasticsearch, Logstash, Kibana, Slack APIs, Flask, and Python

Create real-world applications including a To-Do List Manager, BMI Calculator, Expense Manager, Quiz System, File Encryption Tool, Socket-Based Chat Application, Text Editor, and Password-Protected Personal Diary event-driven programming, GUI design, file management, encryption, socket programming, SQLite integration, authentication, and application security through practical, step-by-step project

Create applications such as a PDF Merger Tool, Bulk Email Sender, Folder Organizer, WhatsApp Message Scheduler, Expense Tracker, News Headline Web Scraper, YouTube Video Downloader, and educational automation demonstrations while learning modern Python scripting techniques.Discover how to use powerful Python libraries including PyPDF2, Pandas, BeautifulSoup, Requests, pywhatkit, CSV,

Build intelligent systems including Iris Flower Classification, Handwritten Digit Recognition, Spam Email Detection, Face Recognition Attendance Systems, House Price Prediction, Loan Eligibility Prediction, Customer Segmentation using K-Means, and Object Detection with YOLO.Master powerful tools such as Scikit-learn, TensorFlow, Keras, OpenCV, YOLO, NumPy, Pandas, and Matplotlib

Build complete, real-world projects including COVID-19 Data Analysis, Sales Forecasting, Movie Recommendation Systems, Customer Churn Prediction, Stock Price Prediction using LSTM, Twitter Sentiment Analysis, Credit Card Fraud Detection, and Interactive Dashboards with Streamlit.Learn industry-standard tools such as Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Plotly, and Streamlit

Build portfolio-worthy web applications using Flask, Django, REST APIs, databases, Git, GitHub, authentication systems, third-party APIs, and professional development workflows.From your very first project to complete web applications like an E-commerce System, Job Portal, Resume Builder, Weather App, Blog CMS, and File Sharing Platform, every chapter

Learn Python as a network engineer, not as a computer science student. 632 pages, every example built on real device output. Cisco-first, with Juniper and Fortinet.

A £4.1M PRA compliance failure. A fictional bank called Avon & Wessex. Sixteen chapters and 23 systems showing exactly how to build production-grade, auditable AI for credit, market, operational and liquidity risk, model governance, and regulatory reporting — the practitioner's guide UK banking has been missing.

An agent can score well on average and still fail exactly where it matters. Beyond “Ship and Pray” shows how to replace benchmark averages with designed experiments, geometric ground truth and failure attribution—so teams can discover when an agent breaks, identify the responsible component and test whether it fails safely under tool faults.

If you want to build real NLP applications instead of just experimenting with notebooks, spaCy is one of the best places to start. This book walks you through the entire journey from the fundamentals to advanced production workflows with practical explanations, real code examples and hands-on projects that show you how to build fast, reliable NLP systems for the real world.

Not another "hello world" sheet. 12 print-ready cards for REAL Python automation —files, CSV, APIs, regex & scheduling. ~90 copy-paste snippets. Letter + A4.

Designing AI is only half the journey. Learn how to deploy, secure, govern, monitor, and scale Enterprise AI systems for production with practical architectures and proven operational strategies.

A language model predicts tokens. An agent acts—and must be held accountable. Learn to replace “prompt and pray” with governed tools, geometric memory, independent verification and auditable runtime controls.

We'll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it's no longer a magic process but just plain arithmetic.

Deep learning is transforming the world, and this book provides a clear, practical path to mastering it. Through concise explanations and hands-on Python examples, you will learn to build, train, optimize, and deploy neural networks with confidence for real-world applications.