Machine Learning with Python: Build Real Projects
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- ✓ All chapters + source
- ✓ Elexa dashboard access
Overview
About This Playbook
What this machine learning playbook covers
Machine learning is how software learns patterns from data instead of following rules someone wrote by hand. It powers price predictions, fraud checks, product recommendations and much more. This playbook teaches it the way it is used at work: with real data, Python and a clear process from problem to result.
Each chapter starts with the idea in plain language, shows it on a small example, then puts it into code you run yourself. You will not be asked to memorise formulas. You will understand what each method does, when to use it and how to tell if it is working.
What you will build
House price predictor: a regression model that estimates prices from features like size and location
Customer churn classifier: a model that flags customers likely to leave, with the metrics a business would check
Customer segmentation: grouping customers by behaviour using clustering
The final chapter turns your best model into a small API with FastAPI, so other programs can use it.
How the playbook is taught
You work in Python with NumPy, pandas, Matplotlib and scikit-learn. Every chapter ends with short exercises and ready-to-use AI prompts that help you explore data, debug code and explain results. The complete source code is included and organised to match the chapters.
Who this playbook is for
It suits developers, students and analysts who know basic Python and want to move into machine learning. It also helps data analysts who use spreadsheets or SQL and want to start predicting, not only reporting.
What you need
A computer with Python 3, a code editor such as VS Code or Jupyter, and basic Python knowledge (variables, loops, functions). No advanced maths is required; the ideas are explained with examples.
What You'll Build
What you build and learn in this playbook.
Three real projects
Prediction, classification and clustering projects built on real-world style data.
Explained before coded
Each method is explained in plain language with a worked example before you write code.
Honest evaluation
Learn the metrics that matter, so you know when a model is good enough and when it is not.
Source code included
Complete, commented code for every chapter, ready to run and change.
Screenshots
What you'll learn
- ✓Load, explore and clean data with pandas
- ✓Turn raw columns into useful features
- ✓Train regression and classification models with scikit-learn
- ✓Use decision trees, random forests and gradient boosting
- ✓Evaluate models with accuracy, precision, recall and cross-validation
- ✓Find groups in data with clustering
- ✓Avoid common mistakes such as data leakage and overfitting
- ✓Serve a trained model through a simple FastAPI endpoint
Prerequisites
- ✓basic Python; school-level maths
Who it's for
- ✓developers moving into AI, computer science students, data analysts, working professionals who want practical ML skills
Chapters
18 chapters in this playbook
- 1
Elexa Machine Learning with Python Chapter 01 Visual Playbook
- 2
Elexa Machine Learning with Python Chapter 02 Visual Playbook
- 3
Elexa Machine Learning with Python Chapter 03 Visual Playbook
- 4
Elexa Machine Learning with Python Chapter 04 Visual Playbook
- 5
Elexa Machine Learning with Python Chapter 05 Visual Playbook
- 6
Elexa Machine Learning with Python Chapter 06 Visual Playbook
- 7
Elexa Machine Learning with Python Chapter 07 Visual Playbook
- 8
Elexa Machine Learning with Python Chapter 08 Visual Playbook
- 9
Elexa Machine Learning with Python Chapter 09 Visual Playbook
- 10
Elexa Machine Learning with Python Chapter 10 Visual Playbook
- 11
Elexa Machine Learning with Python Chapter 11 Visual Playbook
- 12
Elexa Machine Learning with Python Chapter 12 Visual Playbook
- 13
Elexa Machine Learning with Python Chapter 13 Visual Playbook
- 14
Elexa Machine Learning with Python Chapter 14 Visual Playbook
- 15
Elexa Machine Learning with Python Chapter 15 Visual Playbook
- 16
Elexa Machine Learning with Python Chapter 16 Visual Playbook
- 17
Elexa Machine Learning with Python Chapter 17 Visual Playbook
- 18
Elexa Machine Learning with Python Chapter 18 Visual Playbook
Who Builds These Playbooks
Elexa AI Team
Architecture-first · Production-style code · AI prompts in every chapter
Every Elexa playbook is written around one rule: explain the why before the code. Each chapter moves from the concept and its tradeoffs to annotated, production-style code, checklists and ready-to-use AI prompts, so you understand the system you're building, not just the snippets.
Frequently Asked Questions
Do I need to be good at maths?
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No. School-level maths is enough. Every idea is explained with examples, and the code does the calculations.
Which Python level do I need?
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You should be comfortable with variables, loops, functions and lists. The first chapters cover the libraries you need.
Which libraries does it use?
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NumPy, pandas, Matplotlib and scikit-learn, with FastAPI in the final chapter.
Is the source code included?
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Yes. You get the complete code for every chapter and project.
How long does it take?
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Most learners finish in 2 to 3 weeks at 2 hours a day.
Is this enough to start a job in machine learning?
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It gives you solid foundations and three portfolio projects. Deep Learning and NLP are the natural next steps.
Machine Learning with Python: Build Real Projects
Ready to start?
One payment. Lifetime access. Real code you can ship.
- ✓ Lifetime access
- ✓ All chapters + source
- ✓ Elexa dashboard access