Pau Labarta Bajo, a Math turned ML expert, discusses transitioning from Excel to Python, importance of data in ML, essential skills for new devs. Talks about ML vs Stats, challenges in deploying models, leveraging domain knowledge in coding, freelancing as an ML engineer, and learning through failures in coding projects.
Transitioning from Excel to Python is crucial for entering the field of machine learning.
Understanding the distinction between statistics and machine learning helps in choosing suitable problem-solving approaches.
Deep dives
Transition from Mathematics to Applied Statistics and Data Science
Starting with a passion for solving problems in his childhood, Paul transitioned from competing in mathematical contests to studying mathematics in university. Feeling the need for real-world applications, he shifted to work as a quantitative analyst in a bank in Vienna, focusing on building statistical models for financial derivatives risk assessment.
Tools and Coding Evolution in the Quantitative Analysis Field
In his initial job, Paul primarily used Excel, MATLAB, and later incorporated Python as the language started gaining popularity in statistics and machine learning. The job demanded quick and practical solutions often achievable in Excel. This shift reflected the evolving trend towards utilizing Python for more sophisticated statistical and machine learning applications.
Distinction Between Statistics and Machine Learning in Problem Solving
Paul explained that both statistics and machine learning aim to identify patterns in data but vary in approach. Statistics relies on traditional models and tests, suitable for solving simpler problems, while machine learning leverages complex, opaque models like neural networks to tackle intricate data patterns, even facilitating language generation tasks. The distinction between these fields lies in their historical development and computational sophistication.
Learning Machine Learning through Real-World Challenges
Embarking on a journey from self-learning to professional application, Paul emphasized the need to develop machine learning skills on the job. He shared the experience of organically learning machine learning while working, emphasizing the importance of hands-on projects with real data sets. The immersive learning process involved the migration from theoretical concepts to practical deployment and maintenance of machine learning solutions.
Meet Pau Bajo, Machine Learning Engineer and Educator at Real-World Machine Learning. Pau talks to Saron about transitioning from working daily in Excel to Python, why data is everything, and what skills early developers need to foster if they want a career in machine learning.
Pau is a Mathematician turned Machine Learning Engineer, turned Machine Learning educator. He creates hands-on content about Real-World Machine Learning and shares it with the world almost always for free and sometimes “for a fee” because he has a mortgage to pay. He tries to make people laugh too. The mortgage thing is not a joke.
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