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)

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)

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)

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)

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

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

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)

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

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!