Course Details

Supervised Learning Training Course: Building Predictive Models from Labeled Data

INTRODUCTION

Supervised learning is a core branch of machine learning where algorithms learn from labeled data to make predictions or decisions on new, unseen data. This Supervised Learning training course provides a comprehensive understanding of the principles and techniques behind supervised learning, equipping participants with the skills to build predictive models for a wide range of applications. Participants will explore various supervised learning algorithms, learn how to prepare and preprocess data, evaluate model performance, and apply these techniques to real-world problems. This machine learning training is ideal for anyone seeking to develop expertise in building predictive models and leveraging data for informed decision-making. This course covers key areas like regression, classification, model evaluation, and feature engineering, empowering you to become proficient in supervised learning techniques.

DURATION

5 days

TARGET AUDIENCE

This course is designed for a broad audience, including:

• Data Analysts: Professionals seeking to enhance their data analysis skills with supervised learning techniques.
• Business Analysts: Individuals looking to leverage supervised learning for business insights and predictions.
• Software Developers: Those wanting to incorporate predictive modeling capabilities into their applications.
• Students: Individuals exploring career paths in data science and AI.
• Anyone interested in building predictive models: Individuals wanting to gain a deep understanding of supervised learning.

COURSE OBJECTIVES

Upon completion of this course, participants will be able to:

• Understand the principles of supervised learning and its various types.
• Implement common supervised learning algorithms (linear regression, logistic regression, decision trees, support vector machines, etc.).
• Prepare and preprocess data for supervised learning models.
• Evaluate model performance using appropriate metrics.
• Select the best model for a given task and optimize its parameters.
• Apply supervised learning techniques to solve real-world problems.
• Understand the limitations and potential of supervised learning.
• Communicate effectively about supervised learning concepts and findings.

COURSE MODULES

• Introduction to Supervised Learning:
• Defining supervised learning and its relationship to other machine learning paradigms.
• Understanding the supervised learning workflow (data collection, preprocessing, model training, evaluation).
• Exploring the different types of supervised learning tasks (regression, classification).
• Discussing the applications of supervised learning in various industries.
• Linear Regression:
• Introduction to regression problems and linear regression.
• Implementing simple linear regression and multiple linear regression.
• Evaluating regression models using metrics like mean squared error (MSE) and R-squared.
• Hands-on exercises with real-world datasets.
• Logistic Regression:
• Introduction to classification problems and logistic regression.
• Implementing binary logistic regression and multi-class logistic regression.
• Evaluating classification models using metrics like accuracy, precision, recall, and F1-score.
• Hands-on exercises with real-world datasets.
• Decision Trees:
• Introduction to decision trees for classification and regression.
• Building and interpreting decision trees.
• Handling categorical and numerical data in decision trees.
• Evaluating decision tree performance and pruning techniques.
• Hands-on exercises with real-world datasets.
• Support Vector Machines (SVMs):
• Introduction to support vector machines for classification.
• Understanding the concept of support vectors and the kernel trick.
• Implementing linear and non-linear SVMs.
• Evaluating SVM performance and tuning hyperparameters.
• Hands-on exercises with real-world datasets.
• Model Evaluation and Selection:
• Importance of model evaluation in supervised learning.
• Techniques for splitting data into training, validation, and test sets.
• Cross-validation and hyperparameter tuning.
• Model selection criteria and best practices.
• Feature Engineering and Data Preprocessing for Supervised Learning:
• Importance of data preprocessing for supervised learning models.
• Techniques for data cleaning, transformation, and feature engineering.
• Handling missing values and categorical data.
• Feature scaling and normalization.
• Applying Supervised Learning to Real-World Problems:
• Case studies of supervised learning applications in various domains.
• Best practices for deploying supervised learning models in production.
• Ethical considerations in supervised learning.
• The future of supervised learning and its potential impact.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com