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🚗 Introduction to Machine Learning with Cars Data
We start with data about cars, including characteristics (features) and prices (target). A Machine Learning (ML) model can be used to extract patterns from known information (data) about some cars in order to predict car prices based on their characteristics. -
🧠 Rules-Based Systems vs. Machine Learning
- Rules-Based Systems: It is necessary to manually convert rules into code using a programming language and apply them to data. Extracting patterns manually can become complex and challenging.
- Machine Learning: Instead of manually coding rules, ML models automatically extract patterns from data using Mathematics and Statistics.
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🔍 Supervised Machine Learning
In supervised learning, models learn from labeled data (with known outcomes) to make predictions on unseen data. -
🛠️ CRISP-DM (Cross Industry Standard Process for Data Mining)
A structured methodology for organizing ML projects, consisting of the following steps:- 💼 Business Understanding
- 🔎 Data Understanding
- 🧹 Data Preparation
- 🤖 Modeling (choosing and training models, then selecting the best one)
- 📊 Evaluation
- 🚀 Deployment
This process is iterative, allowing for continuous improvement.
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🏆 Model Selection
Split data into training, validation, and test sets. Train different models, validate them, select the best performing one, and then test it on the test set to ensure generalization. -
💻 Setting Up the Environment
Install necessary tools like Python, Numpy, Pandas, Matplotlib, Scikit-learn. Anaconda is the easiest option. Eventually create an AWS account for cloud resources. -
🔢 Introduction to Numpy
Numpy is crucial for manipulating numerical data, providing efficient operations on arrays and matrices. -
🔗 Linear Algebra
Covering all types of multiplication with vectors and matrices, including the creation of identity matrices using functions likenp.eye(). -
📊 Introduction to Pandas
Pandas is a Python library used for processing and analyzing tabular data efficiently.
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