Machines that can LEARN!
The concept of Artificial Intelligence is used in sci-fiction movies to describe a virtual entity that crossed some critical threshold point and developed self-awareness. And like any good Hollywood movie, this entity will turn against humankind. OMG! It’s a great concept to fuel our basic survival fear, otherwise, no one will buy a ticket to the next Terminator movie ;-)
As you may guess, things, in reality, are completely different. Artificial Intelligence is one of the biggest revolutions in the software industry. It is a mind-shift on how to develop software applications. Instead of using hard-coded rules for performing something, we let the machines learn things from data, decipher the complex patterns automatically and then use it for multiple use cases.
- There are growing amounts of AI-powered applications in a variety of practical use cases. Web sites are using AI to better recommend visitors about products and services. The ability to recognize objects in real-time video streams is driven by machine learning. It is a game-changing technology and the game just started.
- The concept of AI and ML can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts.
- We are going to open the mysterious AI/ML black-box and take a look inside, get more familiar with the terms being used in the industry. It is going to a super interesting story. It is important to mention that there are no specific prerequisites for starting this training and it is designed for absolute beginners.
Would you like to join the upcoming Machine Learning revolution?
- There are no specific prerequisites for starting this training as it is designed for absolute beginners
- Artificial Intelligence, Machine Learning and Deep Learning
- Applied vs. Generalized AI
- Features, Labels, Examples
- The Process of Training a Model
- Under-fitting and Over-fitting
- Supervised/Unsupervised Learning
- Classification and Regression
- Clustering and Dimension Reduction
- Reinforcement Learning
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