Predict Football Scores with Python & Machine Learning

Analyze team stats to reveal how possession, discipline, positioning, shots, and defence influence match outcomes, guiding data preparation for a score prediction model.

Validate standings data by checking for zero games and zero points, ensure required columns exist, and identify anomalies like disqualifications or penalties to clean data before analysis.

Check fixtures.csv for inconsistencies by validating non-negative scores, ensuring event id, unique match id, venue id, home/away team ids align to teams and venues, dates 2010–2030; remove invalid venue data.

Use bayesian linear regression for regression imputation to fill missing football match data, preserving variable relationships with iterative imputer and applying zero thresholds plus three-sigma limits.

Evaluate match score predictions using mean absolute error, median absolute error, and R2, reporting MAE 0.42 (home) and 0.37 (away) with R2 about 0.68 and 0.65.

Create an empty PyCharm project with an app.py Flask app, templates/index.html, a model folder for prediction of match results and train files, and a requirements file listing Flask, pandas, scikit-learn.

Integrate back-end logic with Flask to fetch upcoming fixtures and team stats from a football API, load a machine learning model and scaler, and expose routes for predicting match scores.

Explore a 25-step ai model training pipeline in Google Colab, covering data preparation, augmentation, cnn architecture, training, evaluation, and saving models and graphs.

Analyzes the model’s test data performance, showing 67% overall accuracy with balanced macro and weighted averages, and highlights strong happy detection (F1 0.86, recall 85%), plus notable surprise (F1 0.8).

Explore an end-to-end training pipeline for aircraft detection with YOLOv8 in Google Colab, covering library setup, data preparation, annotation extraction, model training, and evaluation across 27 steps.

Analyze how the loss components decrease across 30 training epochs, improving box localization and classification, while validation losses trend down with stable generalization and no significant overfitting.

Analyze the training results of the Yolo v9 model to see how it learns object detection, tracing box loss, class loss, DFL, and mean average precision toward validation data.

learn to fine-tune a MarianMT english-french translation model in google colab, covering data preparation, deduplication, smart lowercase, tokenization, balancing, training with Hugging Face trainer, and saving to Google Drive.

Analyze final performance of the Marion, MT translation model fine-tuned on an English-French corpus, reporting stable loss, fast inference, and strong bleu, meteor, and igf quality metrics.

Explore how a Flask web app loads a Marian MT model and tokenizer to translate English to French, with a web interface and JSON API.

Develop a multilingual text summarizer web app in Python and Flask that supports English and French, using a fine-tuned Emballage 50 model for supervised summarization.

Explore how the facebook/mbart-large-50 model uses a bidirectional encoder and autoregressive decoder for multilingual summarization, pre-trained on 50 languages and fine-tuned with text-summary pairs.

Deploys a fine-tuned M Bart 50 multilingual summarization model for English and French within a Flask web app, handling language selection, GPU acceleration, and an animated processing spinner.

Present the final project showcasing an AI application that detects pneumonia from chest x-rays using a fine-tuned efficientnet B0, with a flask backend and bootstrap frontend for rapid pre-screening.

Explore how the app.py file powers a pneumonia detection web app by connecting a pre-trained EfficientNet model to a Flask interface, processing uploaded x-rays, and returning diagnosis and confidence.

Explore how deep learning uses artificial neural networks to learn from raw data. See how input, hidden, and output layers, weights, and activation functions shape predictions.

Learn how loss quantifies the difference between a neural network's prediction and the true label, and how backpropagation adjusts weights across layers to improve future predictions.

Activation functions transform neuron outputs into nonlinear values, enabling learning of complex relationships. The lecture covers sigmoid, tangent function, ReLU, leaky ReLU, and softmax, and notes vanishing gradients.

Explore TensorFlow, PyTorch, and Keras, three leading deep learning frameworks that leverage GPUs and TPUs to train models from simple neural networks to complex architectures.