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PT27666
Training Summary
Introduction to Data Science, Machine Learning & AI (Python version) covers every stage of the Data Science Lifecycle, from working with raw datasets to building, evaluating and deploying Machine Learning (ML) and Artificial Intelligence (AI) models that create efficiencies for the organization and lead to previously undiscovered insights from your data.
Prerequisites
There are no prerequisites for this course. A working knowledge of data is helpful.
Duration
5 Days/Lecture & Lab
Audience
Budding data scientists, data analysts, statisticians, BI developers, data engineers, enterprise and application architects, machine learning scientists and engineers
Course Topics
It begins by teaching you how to use Python libraries, such as Pandas, Numpy and SciPy, to work with all types of data in Python, including everything from data in a Relational Database to Google Images. You’ll learn how to manage, transform and visualize data in every conceivable way, in order to unearth the real value in your current and historic data. You’ll then use Python libraries such as Scikit- Learn to understand how to build, evaluate and deploy many Machine Learning (ML) and Artificial Intelligence (AI) models that not only predict into the future but constantly learn from data as new events unfold. By the end, you will be able to confidently apply many ML & AI techniques to both enhance your organization’s efficiencies and, through predictive modelling, be prepared for future possibilities.