Apply Neural Networks for Car Price Prediction

By the end of this course, learners will be able to analyze structured datasets, prepare features for machine learning, build and evaluate neural network regression models, and apply regularization techniques to improve predictive performance. They will gain hands-on experience transforming raw car pricing data into actionable insights using industry-standard Python libraries and neural network workflows. This course guides learners through a complete, real-world project focused on car price prediction, moving step by step from data acquisition and exploratory data analysis to model training, evaluation, and optimization. Learners will develop practical skills in data preprocessing, feature encoding, scaling, and distribution analysis, followed by constructing and assessing a neural network model using regression metrics such as mean squared error. What makes this course unique is its project-driven, end-to-end approach that mirrors real data science workflows. Instead of isolated concepts, learners apply each technique in context, building confidence in handling structured datasets and neural network models. By completing this course, learners strengthen their job-ready skills in machine learning, data analysis, and predictive modeling, making it ideal for aspiring data scientists, machine learning practitioners, and analytics professionals seeking practical neural network experience

• Prepare and preprocess structured data for neural network regression
• Build and evaluate neural network models using regression metrics
• Apply regularization and feature engineering to improve predictions

• No deep prior experience is required, but basic computer and internet skills are helpful
• Ability to read course instructions in English and complete short practice activities