Hands-On Problem Solving For Machine Learning

Intuitive strategies to deal with messy data, weak models, and leaky machine-learning pipelines.

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!

Rudy Lai is the founder of Quant-Copy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, Quant-Copy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance?key analytics that all feedback into how our AI generates content.

Prior to founding Quant-Copy, Rudy ran High-Dimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High-Dimension, IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn about reinforcement learning and supervised learning topics in detail, in a commercial setting.

Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

Some basic knowledge of Python programming

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