Predict Football Scores with Python & Machine Learning | Udemy


Predict Football Scores with Python & Machine Learning | Udemy [Update 04/2025]
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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