- Acquire a toolbox for machine learning in Python in just 30 minutes.
- Clean messy datasets from the real world and use them in Python
- Fix linear models that predicted wrong numbers
- Resolve issues with classification models that mislabel data points
- Deal with overfitting and making sure models generalize to the future
- Future-proof your machine-learning pipeline
Machine learning is all the rage, and you have been tasked with creating models for your business. What looked simple on the surface quickly becomes a nightmare of messy data and non-performing models. What do you do?
Hands-On Problem Solving for Machine Learning is packed with intuitive explanations of how machine learning works so that you can fix your models when they break. It presents a wide array of practical solutions for your machine learning pipeline, whether you are working with images, text, or numbers. You’ll get a real feel for how to tackle challenges posed during regression and classification tasks.
If you want to move past calling simple machine learning libraries, and start solving machine learning problems with real-world messy data, this course is for you!
All the code and supporting files for this course are available on GitHub at – https://github.com/PacktPublishing/Machine-Learning-Problems-Solved-V-
Style and Approach
This fast-paced, solution-focused course quickly brings you to the heart of any machine learning problem; it supplies streamlined explanations around what is wrong, how it is wrong, and what needs to be done to solve it, and also hands-on demonstrations of the solution implemented.
- Resolve challenges in supervised learning: misbehaving classifiers and wrong regressors.
- Practical solutions for building production-ready machine-learning pipelines that don’t break
- Intuition-driven practical tour through machine learning, packed with step-by-step instructions, working examples, and helpful advice.