Machine Learning, Data Science, and Natural Language Processing (NLP) are incredibly useful -- and daunting to master!
During the 1000+ hours I've met with students, I have helped individuals tackle machine learning topics from linear regression to deep neural networks, from natural language processing to computer science. My students succeed in classes and are hired for machine learning positions.
Where will Machine Learning take You?
WHAT STUDENTS SAY
Easy going and very knowledgeable
Christian is calm, thorough and very knowledgeable. It's very easy to bombard him with questions, he explains everything methodically, giving you the big picture. Exactly what I need as I'm embarking on a journey to transition my career into data science.
- Foad, Glen Head, NY
Worth Every Penny!
Christian was super knowledgeable and a patient teacher. He was encouraging when needed and understands how to help you learn. He knew at times where to help me just grind through it, and also when to step away and give more context and theory to the ML process. A great tutor and great guy.
- John, Zionsville, IN
Wide range of knowledge!
I hired Christian to help me with one of my deep learning projects. He was absolutely great at taking difficult concepts and explaining them through simpler concepts. I even asked him for help on more difficult subjects like my grad level AI class!
- Umair, Spring, TX
Q: How frequently does a typical student meet with you?
A: Different students have different needs. I have students who meet with me once or twice a week on a regular schedule. Others simply book me from time-to-time on an as-needed basis, and then I meet with some students nearly every day!
To get started in machine learning, I recommend we meet at least once a week for an hour. Meeting once a week gives you time between lessons to read and do assignments independently, while hour lessons give us enough time together to review and discuss your progress, answer questions that came up during the week, learn something new, and plan the tasks for the next week.
But I'll meet with you as often (or as intermittently) as suits your needs!
Q: How long does it take to master machine learning?
A: Machine learning is a large field, and as with any skill, it takes practice to master. You can learn the basics of machine learning in about 3 months by spending several hours per week studying and implementing algorithms. Going beyond the basics takes time. One of my most dedicated students found a new position in machine learning within a year of starting lessons.
Q: How long are sessions?
A: I typically offer 60 minute, 90 minute, and 2 hour lessons, but I'm open to longer paired working sessions as well.
Q: How much do lessons cost?
A: My base rate is $150 / hr. But I offer discounts up to 20% for repeat students. Please visit the A.I. Talks booking page for details.
Q: Do you conduct mock interviews?
A: Yes! I offer mock interviews for both Coding (e.g. Leet Code style) as well as Machine Learning System Design interviews. See the A.I. Talks booking page for details
Q: What is your cancelation policy?
A: Please give me 24 hours notice if you are going to miss a session.
Q: How does online mentoring work?
A: The tools available to communicate online are impressive. I typically use Zoom for video conferencing. Online whiteboards like bitpaper.io let us type, draw notes, and share diagrams. We'll try out these tools in the free initial session to make sure you are comfortable using them.
With a Ph.D. in Natural Language Processing and 9 years in industry as a Machine Learning Scientist, I know what it takes to work in machine learning. At Amazon (2013-2018) I extended the Alexa personal assistant's ability to map natural language into actionable commands. As a scientist at DefinedCrowd (2020-2021) I wrote a tool that cleaned and processed text data for training speech recognition systems. At Nuance Communications (2010-2013) I built and improved language models for speech recognition on mobile devices.
When I left Amazon, my team maintained more than 25,000 machine learned models of different sets of actions. In one project at Amazon I added functionality to train on out-of-vocabulary utterances. In another, I directly modified our model's loss function to regularize training toward a core model when training on less reliable data.
During my 2 and half years at Nuance I was part of a team of 5-10 scientists who scaled the number of languages that Nuance could recognize from less than 10 to more than 30. I was personally responsible for the language models for Korean, Turkish, and Hungarian, and worked on German and Mandarin as well.
My Ph.D. thesis at the Language Technologies Institute at Carnegie Mellon University was on unsupervised morphology induction. I built a system that learned to conjugate and inflect words in any language from nothing more than text data.
I live in the Seattle suburbs with my family (and I do love the Northwest weather :-). In my spare time I make video games in Unity, walk, play pickleball, and am learning to play the piano.