Professional Development · IT & Digital

Machine Learning — From Theory to Practice

A 25-topic comprehensive guide to machine learning: the ML workflow, data preparation, classical algorithms, evaluation, regularisation, deep learning, and deploying models to production — with Python-oriented examples using scikit-learn, Keras, and TensorFlow.

25 Lektionen

25 Karteikarten-Decks

25 Quizze

Lehrplan

Module 1: ML Foundations and Data

The Machine Learning Workflow: Problem to Prediction1 Decks1 Quiz Types of Problems: Classification, Regression, Clustering1 Decks1 Quiz Data Preparation: Cleaning and Feature Selection1 Decks1 Quiz Feature Engineering: Creating Better Inputs1 Decks1 Quiz Splitting Data: Train, Validation, and Test Sets1 Decks1 Quiz

Module 2: Regression and Tree Models

Linear Regression: How and Why It Works1 Decks1 Quiz Logistic Regression: Classification Fundamentals1 Decks1 Quiz Decision Trees: Splits, Depth, and Overfitting1 Decks1 Quiz Random Forests: Ensemble Power1 Decks1 Quiz Gradient Boosting: XGBoost and LightGBM1 Decks1 Quiz

Module 3: More Algorithms

Support Vector Machines (SVM)1 Decks1 Quiz k-Nearest Neighbours (kNN)1 Decks1 Quiz Naïve Bayes and Text Classification1 Decks1 Quiz Clustering: k-Means and Hierarchical Methods1 Decks1 Quiz Dimensionality Reduction: PCA and t-SNE1 Decks1 Quiz

Module 4: Evaluation, Tuning, and Regularisation

Model Evaluation: Confusion Matrix, ROC, AUC1 Decks1 Quiz Handling Imbalanced Datasets1 Decks1 Quiz Regularisation: Ridge, Lasso, and Elastic Net1 Decks1 Quiz Cross-Validation and Hyperparameter Tuning1 Decks1 Quiz

Module 5: Deep Learning and Production

Neural Networks with Keras/TensorFlow: First Steps1 Decks1 Quiz Convolutional Neural Networks for Images1 Decks1 Quiz Recurrent Neural Networks and Sequence Data1 Decks1 Quiz Transfer Learning: Using Pre-Trained Models1 Decks1 Quiz Deploying a Model: From Notebook to API1 Decks1 Quiz ML in Production: Monitoring and Drift Detection1 Decks1 Quiz

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