Time has come to give machine learning and AI a try in your lab. Having in-house expertise would be a great win to avoid costly mistakes and bring results faster.
Congratulations! You have decided that you want to use AI and machine learning in your research. And you see R or Python as the most suitable tool for this task. Perhaps your team uses already MATLAB for their day to day work, which makes it a better choice. The only issue? There is a steep curve to master all the right tools, and since your research project is highly visible there is pressure to deliver results quickly. Auditing a semester-long machine learning lecture from your colleagues is not an option.
Kicking off a project with new technology is stressful
You need to get your team up to speed quickly, but there are so many important questions to answer:
- Where should we start?
- What are the “best practices”
- How do we structure our code and data?
- How can we ensure our analysis is reliable?
- What common pitfalls are there and how can we avoid them?
- We found some examples on the web but don’t quite understand them!
- What should we know right now and what can we learn later?
What if your team could make some quick-wins fast?
With the right mentoring, your team can hit the ground running and bring value faster. But it is hard to get this right. You might rely on enthusiastic interns, summer students or PhD candidates to get started, but what happens when they leave? Furthermore, you might not be able to maintain and upgrade such pilot projects, and would be eventually shelved, effectively wasting a lot of time and effort.
The solution: a hands-on machine learning course delivered by experts
There are many training providers out there, and there is no shortage of low-cost, online alternatives. But while the initial cost seem low, there are long-term costs to consider. An onsite machine learning course tailored to your needs is a much more effective alternative in the mid-term.
Furthermore, lecturers of data science bootcamps often lack expertise with academic-quality research. Hence, the techniques you learn in such data science course are not usable in production.
2/3 core curriculum, 1/3 tailored to your needs
Our data science training introduces common business use cases as motivation for the algorithms and techniques developed further. We start from the high-level solution of the problem to break it down in specific tasks to be accomplished. At this point we introduce the algorithms needed and how to use and fine-tune them.
Besides the core curriculum, we discuss in advance your concrete needs to include customized case studies and their solution that apply to your concrete business. This ensures that our training will be useful to your team.
Our machine learning training offer
Day One: On this day we cover a high-level overview of the data science methodology, including distinction between supervised and unsupervised learning, validation methods and tools for data exploration.
Day Two: We address more advanced issues, like data preprocessing techniques and dimensionality reduction. Powerful algorithms such as deep learning and gaussian processes regression are introduced. We address also the question of model interpretability.
Day Three: On this day we use data science techniques to cover specific use cases of interest to your focus areas. For instance, this could be a one-day workshop in solving common use cases of text mining. Or tackling challenging problems in time series classification, or modelling sensor data for computer vision applications. Or perhaps addressing how to handle which data needs to be collected to improve your model. The curriculum of the third day is agreed in advance with you to make the training successful.
Who can participate?
To get the most out of our training, we expect that the participants have a basic background in mathematics and probability. Some experience with data manipulation and mathematical modelling (be it in Python, R, MATLAB or other languages) can be useful.
If necessary, some background material will be provided to get ready. Some hands-on practice from real life or from DataQuest in your language of choice (R or Python) would be helpful. If you prefer MATLAB, we can provide a quick get started guide and exercises to complete ahead of the workshop.
How do I get started?
- Full name
- Email address, company name and location.
- Goals for the workshop and background of the participants.
We are based in Prague, Czech Republic, but we can travel as needed. However, we are able to hold training on-site once or twice per month only, so book your training fast! Get the training your team deserves now!