Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn patterns from data and make predictions or decisions without being manually programmed for every task. It focuses on building models that improve automatically as they are exposed to more data.
There are three main types of Machine Learning. Supervised learning uses labeled data to predict outcomes and includes tasks like regression (predicting numerical values) and classification (predicting categories). Unsupervised learning works with unlabeled data and focuses on discovering hidden patterns through methods such as clustering or dimensionality reduction. Reinforcement learning teaches an agent to make decisions through rewards, allowing it to learn through trial and error.
Machine Learning relies heavily on data preparation. This includes collecting data, cleaning it, handling missing values, detecting outliers, encoding categorical variables, and scaling numerical features. Proper data preprocessing ensures that models learn meaningful patterns rather than noise. Datasets are typically split into training, validation, and testing sets to evaluate how well a model performs on unseen data.
ML uses a wide range of algorithms. For regression tasks, common methods include Linear Regression, Decision Trees, Random Forests, and Gradient Boosting. For classification, models like Logistic Regression, KNN, SVM, Naive Bayes, and Random Forests are frequently used. Unsupervised learning methods include K-Means for clustering and PCA for dimensionality reduction. Basic neural networks (such as Multilayer Perceptrons) introduce the starting concepts of deep learning.
Evaluating model performance is essential. Regression models are assessed using metrics such as MAE, MSE, RMSE, and R². Classification models use accuracy, precision, recall, F1-score, confusion matrices, and ROC/AUC curves. These metrics help determine whether a model performs well and can generalize to new, unseen data.
To improve models, ML practitioners use techniques such as cross-validation, hyperparameter tuning (like GridSearch or RandomSearch), and regularization methods such as L1 or L2 to prevent overfitting. Simplifying the model or adding more data can also help, especially when a model learns too much noise from the training set.
Machine Learning work is commonly done in Python using tools such as NumPy and Pandas for data handling, Matplotlib and Seaborn for visualization, Scikit-learn for traditional ML models, and TensorFlow or PyTorch for deep learning. These tools make it easier to implement models and analyze data effectively.
A typical Machine Learning project follows a workflow: understanding the problem, collecting data, cleaning and preprocessing it, choosing a model, training it, evaluating the results, improving the model, and optionally deploying it. This structured approach ensures consistent and reliable results.
- Enseignant: NIYONSHUTI Yves