Name: Predictive Modeling and Machine Learning
Author: Holland Swiss

Predictive Modeling and Machine Learning

Categories: Chartered

About Course

This module introduces the core concepts and techniques of predictive modeling and machine learning, essential for analyzing data and making informed predictions. Learners will explore the fundamentals of building models that can forecast future trends or classify data patterns. The module covers both supervised and unsupervised learning methods, providing practical guidance on selecting and applying appropriate algorithms. Additionally, it emphasizes the importance of model evaluation and validation to ensure reliability and accuracy in real-world applications.

Core concepts and importance of predictive modeling in data analysis
Understanding supervised learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction)
How to prepare data for modeling: training, testing, and validation sets
Techniques to evaluate and validate models effectively to ensure generalization
Hands-on knowledge of common algorithms like linear regression, decision trees, k-means clustering, and more
How to interpret model results and improve performance through tuning and feature selection

Course Content

Fundamentals of Predictive Modeling

Introduction to predictive modeling and its importance in data analysis
Key concepts: target variable, features, training and testing datasets
Overview of common predictive models
Practical examples

Supervised and Unsupervised Learning

Model Evaluation and Validation

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About Course

What Will You Learn?

Course Content

Fundamentals of Predictive Modeling

Introduction to predictive modeling and its importance in data analysis

Key concepts: target variable, features, training and testing datasets

Overview of common predictive models

Practical examples

Supervised and Unsupervised Learning

Definition and differences between supervised and unsupervised learning

Supervised learning techniques: classification and regression algorithms

Unsupervised learning techniques: clustering and dimensionality reduction

Use cases for each type of learning with practical datasets

Model Evaluation and Validation

Importance of evaluating model performance to avoid overfitting or underfitting

Common evaluation metrics: accuracy, precision, recall, F1 score, RMSE

Techniques for validation: cross-validation, train-test split

Interpreting results and improving model performance through tuning and feature selection

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Our Courses

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About Course

What Will You Learn?

Course Content

Fundamentals of Predictive Modeling

Introduction to predictive modeling and its importance in data analysis

Key concepts: target variable, features, training and testing datasets

Overview of common predictive models

Practical examples

Supervised and Unsupervised Learning

Definition and differences between supervised and unsupervised learning

Supervised learning techniques: classification and regression algorithms

Unsupervised learning techniques: clustering and dimensionality reduction

Use cases for each type of learning with practical datasets

Model Evaluation and Validation

Importance of evaluating model performance to avoid overfitting or underfitting

Common evaluation metrics: accuracy, precision, recall, F1 score, RMSE

Techniques for validation: cross-validation, train-test split

Interpreting results and improving model performance through tuning and feature selection

Student Ratings & Reviews

Contact us

Our Courses