Supervised Learning Training in San Francisco

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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.

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Supervised Learning Training in San Francisco

Supervised Learning Training in San Francisco, USA

Expert Panel

Course Overview

Overview

Pre-Requisites

Course Outcomes

What Skills It Will Add

Assessment Topics

Certification

Course Outline

Introduction to Supervised Learning and Classification Algorithms

Advanced Supervised Learning Techniques and Model Evaluation

Training Materials

Evaluation and Certification

Supervised Learning Training Events in san-francisco

More Upcoming Session Dates

October 2026

November 2026

December 2026

Events for other courses in San Francisco, USA

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