Kirill Eremenko

Join Kirill Eremenko, CEO of Super Data Science, and Hadelin de Ponteves, a leading AI educator, as they delve into the transformative power of foundation models for businesses. They discuss the eight-step lifecycle for implementing these models, offer criteria for selecting the best fit, and explore clever customization techniques. The duo also introduces AWS generative AI tools, making it easier for companies to leverage AI without breaking the bank. Their insights are a treasure trove for anyone looking to navigate the generative AI landscape!

Kirill Eremenko, Founder and CEO of SuperDataScience.com and a leading data science instructor on Udemy, shares captivating subscriber success stories in AI and data science. He discusses the essential skills needed, revealing why Python and SQL remain vital. Networking and in-person events are highlighted as keys to landing top roles. Eremenko also explores the balance of foundational knowledge and adapting to industry changes, emphasizing the importance of communication and continuous learning in a rapidly evolving field.

Kirill Eremenko, Founder of SuperDataScience, shares insights from his innovative AI engineering bootcamp. He outlines the curriculum's key topics, spanning from foundational Python skills to advanced AI system deployment. Kirill reveals how to utilize AI for business impact and explains the transformation of concepts into practical applications. He also discusses the importance of mindset shifts for AI engineers and highlights the role of employer sponsorship in education, providing a roadmap for aspiring tech educators.

Data scientist Kirill Eremenko discusses the basics of transformers and LLMs, emphasizing the five building blocks of transformer architecture and why transformers are so powerful. Topics include AI recruitment, a new course on LLMs, and the impact of LLMs on data science jobs.

Machine learning expert Kirill Eremenko discusses decision trees, random forests, and the top gradient boosting algorithms: XGBoost, LightGBM, and CatBoost. Topics include advantages of XGBoost, LightGBM efficiency, and CatBoost's handling of categorical variables.

Kirill Eremenko, CEO of Super Data Science, shares six key insights on data science careers, emphasizing practical skills, strong portfolios, mentorship, and collaboration. He discusses the importance of incorporating machine learning into web apps for enhancing employability and highlights the diverse backgrounds of remote-working data scientists.

In this podcast, Kirill Eremenko, the SuperDataScience founder, discusses full encoder-decoder transformers with Jon Krohn. They cover topics like how cross-attention works, the importance of masking during self-attention, and the collaboration dynamics in transformer research. The episode provides a detailed explanation of encoder-decoder transformers, language models, and the use of transformers in natural language processing.

Kirill Eremenko and Hadelin de Ponteves discuss the importance of learning cloud computing for data scientists, essential AWS services, database options, running analytics, and the benefits of AWS certification on the podcast.

Data science instructors Kirill Eremenko and Hadelin de Ponteve discuss essential ML concepts like logistic regression, feature scaling, and the Elbow Method. They introduce their new course and cover supervised vs unsupervised learning, false positives/negatives, and linear regression assumptions.

Kirill Eremenko, Founder and CEO of Super Data Science, returns to share invaluable insights on landing your first data science job. He debunks common myths, such as the necessity of specific degrees or prior experience, emphasizing the importance of passion and transferable skills. Kirill outlines a structured 99-day plan, highlighting essential skills like coding and networking. He encourages listeners to reflect on their motivations and stresses that anyone can break into the field, regardless of background, making data science accessible and exciting!