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

 

Supervised Learning Training Course: Building Predictive Models From Labeled Data
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