We help software developers, academics, data analysts and industry insiders make the transition to a career in Artificial Intelligence.
- Set yourself up for succes in the job market of the future
- Increase your chance to land a job in the field of AI
- Work on portfolio projects, mentored by industry experts
Speed up your Transition into AI
Tought by Industry Experts
- We source the very best experts and teachers with a track record in Data Science and Deep Learning.
- Our experts will teach you a state-of-the-art curriculum.
- As you'll advance through the program, you'll find yourself able to solve more complex problems.
Project-Based
- Use real-world dataset to solve problems with Deep Learning.
- Design, implement and present hands-on portfolio projects.
- The projects are a chance to show employers your new skillset.
One-on-One Mentoring
- An online self-study can be hard. To achieve better results you get help from someone who is more experienced than you.
- You won’t be alone – our Experts will mentor you along the way until you reach your goals.
- 24/7 Support in the forums and weekly Zoom-Calls
REQUIREMENTS
In order to participate in this program you must be a software developer, academic (PhD or Post Doc), data analyst or a domain expert in an industry (e.g. finance, automotive, health-care etc.). Further requirements are:
- Basic understanding of programming
- You must have one hour a day to work for the next 3 months
- An open mind and willingness to learn
- Participations in portfolio-projects
- No hesitance to ask questions
WHAT GRADUATES SAY
Great, well structured program, ideal for those who want to work on real deep learning projects. Exercises and code examples are also very helpful to solidify the knowledge. I recommend this program to anyone who wants to work in the field of Deep Learning professionally.
Johannes Lueken, CEO MiiMedia
This program goes beyond neural networks. Instead, it covers topics that are crucial for a professional career in Deep Learning. Just to name a few: Best practices for implementing neural network models, data-preprocessing, efficiency, GPU-Training, model evaluation, deployment of the models to production etc.
Vitali Rudi, Industrial Engineer
The best thing for me personally was to see the different steps of a typical deep learning project and how to approach these steps in practice. I'm now more confident about taking part in a project myself and solving problems with neural networks.
Adile Kilic, Economical Engineer