ML Fundamentals & Data Preparation
Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve over time, categorized into supervised, unsupervised, semi-supervised, and reinforcement learning. The module outlines the machine learning workflow, emphasizing the importance of data preparation, including data loading, preprocessing, feature engineering, and exploratory data analysis. Key Python libraries essential for machine learning, such as NumPy, Pandas, and Scikit-learn, are introduced to facilitate these processes.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Machine learning is a powerful tool that allows systems to learn patterns from data.
- Data preparation is crucial to ensure accurate and efficient machine learning model performance.
- Key Python libraries like NumPy and Pandas are foundational for data handling and manipulation.
Key Concepts
- -- Supervised Learning
- A type of machine learning where models learn from labeled datasets.
- -- Unsupervised Learning
- A type of machine learning where models find patterns in unlabeled data.
- -- Feature Engineering
- The process of creating new features or transforming existing ones to improve model performance.
- -- PCA (Principal Component Analysis)
- A technique for reducing the dimensionality of data while retaining as much variance as possible.
Additional Learning Materials
Supplementary resources to enhance your learning experience.