Supervised Learning Training in Kuala Lumpur, Malaysia

Introduction to Supervised Learning and Classification Algorithms

• Session 1: Introduction to Supervised Learning

  • What is supervised learning?
  • Types of problems: Classification vs Regression.
  • Overview of scikit-learn and Python libraries for machine learning.
  • Steps in the supervised learning process: Data collection, preprocessing, model selection, evaluation, and deployment.

• Session 2: Data Preprocessing and Feature Engineering

  • Handling missing data (imputation, removal).
  • Feature scaling (Normalization, Standardization).
  • Encoding categorical variables (One-hot encoding, Label encoding).

• Session 3: Linear Regression

  • Introduction to Linear Regression.
  • Understanding hypothesis and cost function.
  • Gradient Descent and Normal Equation for optimization.

• Session 4: Logistic Regression

  • Understanding Logistic Regression and its use for binary classification.
  • The sigmoid function and the logistic loss function.
  • Evaluation metrics for classification: Accuracy, Confusion Matrix, Precision, Recall, F1 Score.

Advanced Supervised Learning Techniques and Model Evaluation

• Session 5: Decision Trees and Random Forests

  • Introduction to Decision Trees and the concept of splitting nodes.
  • Understanding entropy, information gain, and Gini impurity.
  • Random Forests: Building an ensemble of Decision Trees.

• Session 6: Support Vector Machines (SVM)

  • Understanding the concept of Support Vectors and hyperplanes.
  • Linear vs Non-linear SVM and the kernel trick.

• Session 7: Model Evaluation and Cross-Validation

  • Evaluating regression models using Mean Squared Error (MSE), R^2.
  • Evaluating classification models using Confusion Matrix, ROC-AUC, Precision-Recall Curve.
  • Understanding Cross-validation and Hyperparameter tuning (GridSearchCV, RandomizedSearchCV).

• Session 8: Putting It All Together: Final Evaluation

  • Participants work on a final project to solve a classification or regression problem using the techniques learned in the course.
  • Evaluation based on model performance, data preprocessing, and optimization.
  • Q&A and feedback.

This training on Supervised Learning equips participants with the necessary skills to solve real-world machine learning problems using classification and regression techniques. Through a combination of theory, hands-on exercises, and practical implementation, participants will be ready to apply these techniques in their work and further their understanding of machine learning.