Today, data is more than just a corporate asset. As data consumers, we’ve become accustomed to having up-to-the-minute analytics for any event that might affect our business or personal lives. We’re becoming used to the influence of IoT in our homes, cities, clothing, phones, and more—and the resulting instant access to analytics. To manage all this data and provide fast insights and analytics, we have created machine learning and deep learning systems based on the last 50 years of statistical and artificial intelligence algorithms.
Interested in learning what machine learning is and what analytics it is delivering? Join us for a half-day class on this hottest subject of the current analytics industry.
Prerequisites
There are no prerequisites for this course.
There are no prerequisites for this course.
Data professionals interested in machine and deep learning.
- The foundations of machine learning in chaos theory, game theory, and algorithms
- What “machine talk” is and how we architect for it
- How to bring search implementation theory to data with deep learning
- Introduction to TensorFlow, a library of open source algorithms
- Implementing machine and deep learning, including what processing techniques are involved
- Available analytics insights, outcomes, mashups, and KPIs
- How to visualizethe results of machine and deep learning
