Hands-On Problem Solving for Machine Learning

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-  

Target Audience 

This course assumes that readers have some basic knowledge of Python programming. However, he/she need not have any knowledge of quantitative finance or working with financial data. If you're new to quantitative finance and Python is your go-to language, this is the perfect course for you.   

Business Outcomes

• Resolve challenges in su+I81pervised 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.