Machine Learning Intermediate
This course provides a comprehensive guide to fundamental and advanced topics in Machine Learning (ML). It is tailored for learners who wish to grasp the essentials of machine learning concepts, data preprocessing techniques, model training, neural networks, and practical applications, thus enabling them to apply ML techniques effectively in real-world scenarios. By the end of this course, learners should be able to understand key ML algorithms, handle data using preprocessing techniques, build predictive models, and undertake projects demonstrating learned skills.
Fundamentals of Machine Learning
Data Preprocessing Techniques
Building and Training Models
Model Validation Techniques
Practical Applications and Case Studies
Introduction to Neural Networks
Tooling and Environment Setup
Version Control and Collaboration
Creating a Machine Learning Portfolio
Hyperparameter Tuning and Model Evaluation
Optimizing hyperparameters and assessing model performance are key to improving model accuracy and preventing overfitting, crucial elements in any predictive modeling process.
Hyperparameter Tuning Explained in 14 Minutes
How to evaluate ML models | Evaluation metrics for machine learning