
English | Size: 1 GB
Genre: eLearning
Build a Football Score Predictor with Python, Machine Learning, Real Match Data & a Web App Using Flask
What you’ll learn
Build a real-world AI model to predict football scores and power up your portfolio.
Master Python, Pandas, Scikit-learn, Flask, OpenCV, and NLP with real AI projects.
Use machine learning to predict outcomes in sports, healthcare, NLP, and beyond.
Deploy a fully functional AI web app with Flask to impress clients, recruiters, or users.
Level up your data science skills and land freelance gigs or entry-level ML roles.
Apply real-world best practices used by data scientists to build reliable AI systems.
Understand how to evaluate models with metrics like RMSE, MAE, F1-score, and confusion matrix.
Fine-tune advanced models like YOLOv9, EfficientNet, or transformers (mBART, MarianMT).
Integrate AI into real-time applications using APIs, webcam video, or live data streams.
Showcase 7 impressive AI projects covering computer vision, NLP, and medical diagnosis.
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
- Import/export CSV, JSON & image files (Kaggle, Google Drive, API-Football)
- Relational schemas and multi-table joins (fixtures – standings – teamStats)
- Multilingual datasets setup (XSum and MLSUM for summarization, KDE4 for translation)
2. Cleaning & Preprocessing
- Visual EDA (histograms, boxplots, heatmaps)
- Detecting and fixing anomalies (outliers, duplicates, encoding issues)
- Advanced imputation (BayesianRidge, IterativeImputer)
- Image augmentation (ImageDataGenerator: flip, rotate, zoom)
- Normalization and standardization (Scikit-learn scalers)
- Dynamic tokenization and padding (MBart50Tokenizer, MarianTokenizer)
3. Feature Engineering
- Derived variables (performance ratios, home vs. away gaps, NLP indicators)
- Categorical encoding (one-hot, label encoding)
- Feature selection & importance (RandomForest, permutation importance)
4. Modeling
- Traditional supervised learning (Ridge/ElasticNet for score prediction)
- Convolutional Neural Networks (EfficientNetB0 for pneumonia detection)
- Seq2Seq Transformers (fine-tuned mBART50 for summarization, MarianMT for translation)
- Real-time computer vision (YOLOv5/v9 for object, emotion, and drone detection)
5. Evaluation & Interpretation
- Regression: MAE, RMSE, R², MedAE
- Classification: accuracy, recall, F1, confusion matrix
- NLP: ROUGE-1/2/L, BLEU
- Learning curves: loss & accuracy (train/val), early stopping
6. Optimization & Best Practices
- Transfer learning & fine-tuning (freezing, compound scaling, gradient checkpointing)
- GPU/TPU memory management (adaptive batch size, gradient accumulation)
- Early stopping and custom callbacks
7. Deployment & Integration
- Saving models (Pickle, save_pretrained, Google Drive)
- REST APIs with Flask (/predict-score, /summary, /translate, /detect-image)
- Web interfaces (HTML/CSS + animated loader)
- 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:
- Clean and leverage complex datasets
- Build and evaluate powerful ML models (MAE, RMSE, R²…)
- Deploy an AI web app with live APIs
- 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!
Who this course is for:
- Beginner to intermediate developers looking to build a practical sports-focused AI project.
- Students in data science or artificial intelligence seeking real-world projects to enhance their portfolio.
- Football enthusiasts interested in sports analytics and eager to develop predictive modeling skills.
- Anyone motivated by practical projects that combine machine learning, Python programming, and web development (Flask).

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