Best Theory Course for ML
The “Machine Learning Theory (Basic)” course offers a thorough introduction to the core principles and foundational concepts of machine learning, making it an ideal starting point for beginners. This course is designed to demystify the complex world of machine learning by breaking down the essential topics that form the backbone of this rapidly growing field. Students will begin with understanding the basics of data collection, learning where and how to gather relevant data, a critical first step in any machine learning project.
What you’ll learn
- Where to Collect Data For Machine Learning? | Data Collection.
- Data Preprocessing Techniques/Steps.
- Feature Engineering for Machine Learning.
- Supervised vs Unsupervised vs Reinforcement Learning.
Course Content
- Where to Collect Data For Machine Learning? | Data Collection –> 1 lecture • 12min.
- Data Preprocessing Techniques/Steps –> 1 lecture • 8min.
- Feature Engineering for Machine Learning –> 1 lecture • 7min.
- Supervised vs Unsupervised vs Reinforcement Learning –> 1 lecture • 7min.
- Mastering Missing Data Handling –> 1 lecture • 10min.
Requirements
The “Machine Learning Theory (Basic)” course offers a thorough introduction to the core principles and foundational concepts of machine learning, making it an ideal starting point for beginners. This course is designed to demystify the complex world of machine learning by breaking down the essential topics that form the backbone of this rapidly growing field. Students will begin with understanding the basics of data collection, learning where and how to gather relevant data, a critical first step in any machine learning project.
As the course progresses, students will delve into data preprocessing techniques, which are vital for transforming raw data into a format suitable for modeling. This includes learning how to clean data, handle missing values, and normalize datasets, ensuring that the data is in optimal condition for analysis.
Feature engineering, another key topic, will teach students how to create and select the most relevant features to enhance model performance. This skill is crucial as it directly impacts the accuracy and effectiveness of machine learning models.
The course also provides a comprehensive overview of the different learning paradigms—supervised, unsupervised, and reinforcement learning—offering students insight into when and how to apply each method. By the end of this course, students will have gained a strong theoretical foundation in machine learning, equipping them with the knowledge to advance to more specialized studies or to begin applying these concepts to real-world problems with confidence.