Real World Auto Machine Learning Bootcamp: Build 14 Projects

Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real–world business problems is time–consuming, resource–intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought–after professionals in the job market right now.

Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top–ranked data scientists to make data science more accessible across the organization.

“Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products”

As data science evolves and gains new “instruments” over time, the core business goal remains focused on finding useful patterns and yielding valuable insights from data. Today, data science is employed across a broad range of industries and aids in various analytical problems. For example, in marketing, exploring customer age, gender, location, and behavior allows for making highly targeted campaigns, evaluating how much customers are prone to make a purchase or leave. In banking, finding outlying client actions aids in detecting fraud. In healthcare, analyzing patients’ medical records can show the probability of having diseases, etc.