Build a Football Score Predictor with Python, Machine Learning, Real Match Data & a Web App Using Flask
Description
Build an AI That Predicts Football Scores – Plus 6 Hands-On Bonus Projects
Learn artificial intelligence by creating a full web app that predicts match results — and sharpen your skills with six additional real-world AI projects.
The Most Practical and Complete AI Course for Beginners on Udemy
Tired of theory-heavy tutorials that go nowhere? Want to master AI by doing? Fascinated by football or curious how AI can predict scores ? This course is for you.
Your Main Project: An AI That Predicts Match Results
Build a machine learning model that predicts match outcomes for Europe’s top five leagues (Premier League, La Liga, Serie A, Bundesliga, Ligue 1) using real data from Kaggle, ESPN, and API-Football. Then deploy it as a real-time Flask web app — just like a real SaaS product.
Includes 6 Bonus AI Projects
Bonus 1 – Emotion detection via webcam (Computer Vision)
Bonus 2 – Drone and flying object detection (Computer Vision)
Bonus 3 – Road object detection (Computer Vision)
Bonus 4 – English to French translation (Natural Language Processing)
Bonus 5 – Multilingual summarization (Natural Language Processing)
Bonus 6 – Pneumonia detection from chest X-rays (Medical AI)
Optional Theory Modules
ML/DL foundations, CNNs, YOLO, CPU vs GPU/TPU — explained clearly, without jargon.
Skills & Topics Covered
1. Data Acquisition & Organization
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Import/export CSV, JSON & image files (Kaggle, Google Drive, API-Football)
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Relational schemas and multi-table joins (fixtures – standings – teamStats)
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Multilingual datasets setup (XSum and MLSUM for summarization, KDE4 for translation)
2. Cleaning & Preprocessing
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Visual EDA (histograms, boxplots, heatmaps)
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Detecting and fixing anomalies (outliers, duplicates, encoding issues)
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Advanced imputation (BayesianRidge, IterativeImputer)
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Image augmentation (ImageDataGenerator: flip, rotate, zoom)
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Normalization and standardization (Scikit-learn scalers)
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Dynamic tokenization and padding (MBart50Tokenizer, MarianTokenizer)
3. Feature Engineering
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Derived variables (performance ratios, home vs. away gaps, NLP indicators)
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Categorical encoding (one-hot, label encoding)
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Feature selection & importance (RandomForest, permutation importance)
4. Modeling
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Traditional supervised learning (Ridge/ElasticNet for score prediction)
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Convolutional Neural Networks (EfficientNetB0 for pneumonia detection)
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Seq2Seq Transformers (fine-tuned mBART50 for summarization, MarianMT for translation)
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Real-time computer vision (YOLOv5/v9 for object, emotion, and drone detection)
5. Evaluation & Interpretation
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Regression: MAE, RMSE, R², MedAE
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Classification: accuracy, recall, F1, confusion matrix
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NLP: ROUGE-1/2/L, BLEU
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Learning curves: loss & accuracy (train/val), early stopping
6. Optimization & Best Practices
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Transfer learning & fine-tuning (freezing, compound scaling, gradient checkpointing)
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GPU/TPU memory management (adaptive batch size, gradient accumulation)
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Early stopping and custom callbacks
7. Deployment & Integration
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Saving models (Pickle, save_pretrained, Google Drive)
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REST APIs with Flask (/predict-score, /summary, /translate, /detect-image)
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Web interfaces (HTML/CSS + animated loader)
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Real-time processing (OpenCV video streams, live API queries)
8. Tools & Environment
Python 3 • Google Colab • PyCharm • Pandas • Scikit-learn • TensorFlow/Keras • Hugging Face Transformers • OpenCV • Matplotlib • YOLO • API-Football
By the end of this course, you’ll be able to:
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Clean and leverage complex datasets
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Build and evaluate powerful ML models (MAE, RMSE, R²…)
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Deploy an AI web app with live APIs
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Showcase 7 high-impact AI projects in your portfolio
Who is this for?
Python beginners, football & tech enthusiasts, students, freelancers, career changers — anyone who prefers learning by building.
Udemy 30-Day Money-Back Guarantee
Enroll with zero risk — full refund if you’re not satisfied.
Ready to get hands-on?
In just a few hours, you’ll:
– Build an AI that predicts football scores
– Deploy a fully working web application
– Add 7 impressive projects to your portfolio
Join now and start building real AI — the practical way!
Total Students | 127 |
---|---|
Duration | 6.5 hours |
Language | English (US) |
Original Price | |
Sale Price | 0 |
Number of lectures | 159 |
Number of quizzes | 0 |
Total Reviews | 23 |
Global Rating | 4.978261 |
Instructor Name | Gaël Menou |
Course Insights (for Students)
Actionable, non-generic pointers before you enroll
Student Satisfaction
86% positive recent sentiment
Momentum
Steady interest
Time & Value
- Est. time: 6.5 hours
- Practical value: 8/10
Roadmap Fit
- Beginner → Advanced → Advanced
Key Takeaways for Learners
- Best Practices
- Hands On
- Project
Course Review Summary
Signals distilled from the latest Udemy reviews
What learners praise
- Hands On
- Project
- Clear Explanation
- Step By Step
- Practical
Watch-outs
- Missing project
- Theory only
Difficulty
Best suited for
Practitioners optimizing at scale, Doers who prefer project-led learning, Learners who like theory + frameworks
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