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
Feature Selection and Engineering
Feature selection and engineering improve model efficiency and performance by selecting relevant attributes and constructing new ones. This can significantly enhance the predictive power of machine learning models.
Feature Selection | Wrapper | Filter | Embeded Intrinsic Method in Machine Learning by Mahesh Huddar
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Feature Selection in Machine Learning: Easy Explanation for Data Science Interviews
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