Advanced Predictive Techniques with Scikit-Learn and TensorFlow
Use advanced techniques to produce more powerful models
Created by Alvaro Fuentes
Explore advanced ways to boost predictive model performance using Scikit-Learn and TensorFlow. You'll work with ensemble methods, dimensionality reduction, and deep learning to create more accurate and robust models. Gain practical skills that help you tackle real-world predictive analytics challenges.
Packt | Nov 2017 | 224 min
What You Will Learn
You'll get hands-on with tools and techniques that make predictive models more effective. Step by step, you'll use Python libraries to implement ensemble methods, optimize features, and build neural networks. Practical examples and guided exercises help you apply each technique to real data problems.
Key Features
- Combine multiple predictors using ensemble methods for stronger model accuracy
- Apply feature selection and dimensionality reduction to streamline your data
- Build and evaluate neural network models with TensorFlow for deep learning tasks
Target Audience
Ideal for data analysts, data scientists, and developers with Python experience who want to move beyond basic predictive models. If you already understand predictive analytics fundamentals and want to create more powerful models for business or research, you'll find these skills essential.
