Es un científico de datos que busca realizar la transición a un rol de aprendizaje automático. ¿Cuáles son tus primeros pasos?

In the changing field of machine learning, regularly evaluating your skills is essential for beginners and experienced practitioners. In data science, the focus is on skills such as exploratory data analysis, data visualization, and communicating data insights, which are crucial for interpreting and applying raw data. Machine learning, in contrast, centers on creating, training, and testing predictive models, requiring a thorough understanding of algorithms and various frameworks. Proficiency in machine learning is marked by the ability to select and use the most suitable algorithm for a given task.

Based on my experience, it involves these key steps: - Understanding ML Concepts: Deep understanding of fundamental machine learning concepts like supervised learning, unsupervised learning, and reinforcement learning. You need to dive into specific algorithms, and understand their strengths, challenges, and use cases. -Enhance Programming Skills: Improve your programming skills in Python (or R), and learning relevant ML libraries like TensorFlow or PyTorch is a key requirement. -Explore Specialized Areas: Identify and explore specific ML areas like NLP, computer vision, or reinforcement learning. -Hands-On Experiences and Projects: Working on practical ML projects and understanding the challenges of real-world data and tasks.

First, figure out your life's purpose and, hypothetically, the specific difference you'd like to make in the world by applying a mindset, skillset, and toolset that reflects your natural calling. Next, determine what needs to change in the world and in you so that you can make that difference. Finally, map out your inside-out and outside-in transformation plans—inclusive of your mission, methods, and (effectiveness) measures...and return to the first step as you take action and observe your real-world results (especially outlier events).

You can follow the following approach: 1. Evaluate your current skills in data science. 2. Learn ML Concepts 3. Take Online Courses 4. Work on ML projects using datasets from platforms like Kaggle. 5. Showcase your ML projects on GitHub or a personal website. 6. Collaborate on ML projects on GitHub. 7. Attend ML meetups and join online communities. 8. Follow ML experts on social media and read research papers. 9. Practice ML interview questions and coding challenges.

Machine Learning comes with big data. This means that on top of being a good data scientist, able to understand and manipulate data according to the business need, you need to master machine learning algorithms suitable for the type of data and business problem. Accordingly you need to be a good software engineer to be able to optimize and deploy ML algorithms for big data on an infrastructure. Additionally, you need also to be an expert of frameworks and computational resources to scale up ML models like PyTorch, Keras, Tensorflow, Jax as well as when it is the case to use CPU vs GPU vs TPUs often in distributed environment depending on the size of the data, model you are using and business requirement.

To assess your data science skillset, start by evaluating your grasp of machine learning algorithms: supervised (classification, regression), unsupervised (clustering, dimensionality reduction), and reinforcement learning. Gauge your proficiency in programming languages like Python or R, crucial for implementation. Familiarize yourself with key libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn. Identifying gaps in these areas will guide your learning to enhance expertise in data science and machine learning.

Data scientist definitely has experience in building, training, and testing models. However, most of them are written in a Jupyter Notebook. To transition smoothly, try rewriting your past projects in a python script and make each step reproducible. Afterwards, try deploying the model using API frameworks (such as Flask or FastAPI). Finally, containerize the deployed model using docker.

The basis is to understand maths behind machine learning and the rest is an investment of time in understanding the algorithms and mastering frameworks.

Design and build real end-to-end solutions. A training notebook or script is virtually useless without a deployment, an API, something modular that you can combine with an existing software system. “Kaggle data science” will only get you so far, true impact lays in the less hyped, classic development.

To assess my skills, I would begin by reviewing my proficiency in programming languages commonly used in machine learning, such as Python or R. Next, I would evaluate my understanding of key machine learning concepts, algorithms, and frameworks like TensorFlow or PyTorch. Additionally, assessing my statistical knowledge, data preprocessing skills, and ability to work with real-world datasets would be crucial. Lastly, I would consider any experience with model evaluation, deployment, and staying updated on the latest advancements in the field.

While courses and books provide foundational knowledge, applying that knowledge to real-world projects is where you solidify your skills. Platforms like Kaggle offer datasets and competitions to challenge yourself, while repositories and personal projects allow you to explore specific interests.

In an experience within a demand forecasting project in the music industry, we utilized ML algorithms to create a model that analyzed consumption patterns and predicted popularity peaks to optimize launch strategies.

Practical application, experimenting with algorithms, and engaging in real projects significantly enhance the understanding of machine learning principles. It's the hands-on experience that solidifies theoretical knowledge, fostering a deeper comprehension of model building, evaluation, and fine-tuning. By immersing oneself in projects, grappling with real datasets, and iterating through solutions, a data scientist transforms into a proficient machine learning practitioner, equipped with invaluable insights gained through practical application.

To learn machine learning through hands-on experience, start by choosing a beginner-friendly language like Python. Familiarize yourself with libraries such as scikit-learn and TensorFlow. Work on small projects, like predicting house prices or classifying images, to apply theoretical concepts. Engage in Kaggle competitions or contribute to open-source ML projects to gain practical exposure. Seek guidance from online courses, tutorials, and forums when facing challenges. Iterative learning and real-world application will solidify your understanding

Cursos en Línea y Recursos: Explora cursos en plataformas como Coursera, edX o Khan Academy. Libros como "Hands-On Machine Learning with Scikit-Learn and TensorFlow" son excelentes recursos. Además, sigue blogs y podcasts destacados como "Towards Data Science" en Medium o "Data Skeptic" para mantenerte actualizado. Aplicación Práctica en Problemas Reales: Utiliza plataformas como Kaggle, UCI Machine Learning Repository o Google Colab para acceder a conjuntos de datos y desafíos del mundo real. Trabaja en proyectos personales o de código abierto que te interesen, aplicando los conocimientos adquiridos en situaciones prácticas.

Participación en Comunidades en Línea: Únete a comunidades en línea como Stack Overflow, Reddit o Discord. Aquí podrás hacer preguntas, compartir tus proyectos y obtener valiosos comentarios de otros entusiastas del aprendizaje automático. La interacción con la comunidad puede proporcionar nuevas perspectivas y soluciones a los desafíos que puedas enfrentar. Colaboración y Aprendizaje Continuo: Colabora con colegas o participa en proyectos de código abierto. La colaboración te permitirá aprender de otros y mejorar tus habilidades de resolución de problemas. Mantente al día con las últimas tendencias y avances en aprendizaje automático para asegurarte de estar utilizando las mejores prácticas.

Recuerda que la práctica constante es clave para internalizar los conceptos. Aplicar tus conocimientos a proyectos del mundo real te dará una comprensión más profunda y te ayudará a desarrollar habilidades prácticas.

After acquiring foundational knowledge, the next step is practical learning. Dive into online courses, books, and resources, but the most effective learning happens when you apply your skills to real-world problems. Platforms like Kaggle, UCI Machine Learning Repository, or Google Colab provide datasets and challenges. Explore open-source or personal projects that interest you. Join online communities like Stack Overflow or Reddit to engage with fellow enthusiasts, ask questions, and share your work for valuable feedback.

Showcase your portfolio when applying for jobs because it demonstrates your creativity and passion for machine learning and also brand or rebrand your CV to the specific role in question.

Es ist wichtig, dass Sie Projekte auswählen, die eine Bandbreite von Fähigkeiten demonstrieren, von der Datenaufbereitung über die Modellentwicklung bis hin zur Ergebnisinterpretation. Denken Sie auch über die Storytelling-Aspekte Ihres Portfolios nach: Wie haben Sie Herausforderungen gemeistert? Was haben Sie aus Fehlschlägen gelernt? Wie haben Ihre Projekte Einfluss genommen? Plattformen wie GitHub sind ideal, um den technischen Prozess zu dokumentieren, während ein Blog auf Medium eine großartige Möglichkeit sein kann, die Geschichten hinter den Projekten zu erzählen. LinkedIn wiederum bietet eine hervorragende Plattform, um Ihre berufliche Marke aufzubauen und mit der Branche in Kontakt zu treten.

Building a strong portfolio as a data scientist or machine learning enthusiast involves showcasing your skills and projects. Create a personal website to display your bio, skills, and contact information. Include detailed project descriptions, explaining the problem, your approach, and outcomes. Share code repositories on platforms like GitHub, emphasizing clean and well-documented code. Add visualizations, results, and insights to make your projects more engaging. Incorporate any relevant certifications, courses, or educational background. Regularly update your portfolio with new projects to demonstrate continuous learning and growth.

It is advisable to create a portfolio which showcases all the aspects of model building. Start from EDA, data handling, feature selection, hyper parameter tuning, model selection, model tuning, segmentation, automl libraries, comparison of automl and your custom models, model evaluation, model validation and all the other aspects. Create multiple projects using the publicly available datasets and showcase these in a way that is easily interpretable.

1. Seleccion de proyectos destacados: Elige proyectos significativos que destaquen tus habilidades y logros. Pueden ser desafíos de Kaggle, proyectos personales o contribuciones a proyectos de código abierto. Asegúrate de que cada proyecto muestre una variedad de habilidades, desde la preparación de datos hasta la implementación de modelos. 2. Documentacion Completa: Proporciona una documentación completa para cada proyecto en tu portafolio. Incluye detalles sobre el problema abordado, el enfoque utilizado, la selección de modelos, la evaluación de rendimiento y cualquier desafío específico que hayas superado. Esto permite a los revisores entender tu proceso de trabajo. 3. Codigo fuente y Modelos:

Hospeda tu código en plataformas como GitHub. Asegúrate de que tu código sea limpio, bien comentado y fácil de entender. Incluye enlaces directos a los modelos entrenados, para que los interesados puedan revisar y reproducir tus resultados. 4. Resultados: Presenta los resultados de tus proyectos de manera clara y visual. Utiliza gráficos, tablas y visualizaciones para destacar los insights clave. Esto proporciona una comprensión rápida y efectiva de tus logros. 5. Informes: Agrega informes detallados y presentaciones a tu portafolio. Estos documentos deben explicar el contexto del problema, tu enfoque, los resultados y las lecciones aprendidas. Esto demuestra tu capacidad para comunicar de manera efectiva los resultados técnicos.

O portifolio ajuda a mostrar seu ponto de vista. Por mais que sejam, muitas vezes, projetos seminais, o portifolio mostra sua maneira de pensar, resolver problemas e trabalhar.

The third step involves building your portfolio, a collection showcasing your projects and achievements to highlight your skills and experience in machine learning. It can encompass code, models, results, reports, and presentations. Platforms like GitHub, Medium, or LinkedIn can be used to host and share your portfolio with potential employers or clients. A portfolio is crucial for demonstrating your value, creativity, and passion for machine learning.

Develop a portfolio showcasing your machine learning projects, including problem statements, methodologies, and results. Highlight your contributions, insights, and the impact of your projects on solving real-world problems.

Start attending meetups and listen to what others have to share and don't feel shy sharing your experiences with others. Follow known people in the relevant field on LinkedIn and religiously read up on the latest developments in this space.

In my opinion and what I have heard from first hand experience specially with respect to people transitioning from one tech domain to another and may be as a fresher, networking is a very important aspect. I will take linkedin for example, here the individual needs to follow and later interact with prominent personalities in the field of ML. Follow HR of major companies in the field. Participate in discussion threads and put across your view point. Slowly these little efforts will help you develop your network and can help you transition. To conclude your Network is your Networth and invest time and energy to build your network or networth Best wishes

Engage with ML communities, attend workshops, and connect with experts to gain insights. Apply newfound knowledge through side projects or contributions to real ML initiatives. Networking enables exposure to diverse ML perspectives, while practical applications amplify skills, making the transition seamless and impactful.

The final step is networking and applying. Leverage your network, including former colleagues, mentors, friends, or family, to explore machine learning opportunities and seek referrals. Utilize platforms like LinkedIn, Indeed, or Glassdoor to search for machine learning jobs or projects aligning with your skills and interests. Attend events such as meetups, webinars, or conferences to connect and learn from fellow professionals and machine learning experts. When applying, tailor your resume, cover letter, and portfolio to the specific role and company. Be ready to address technical and behavioral questions, showcasing problem-solving skills and effective communication.

Connect with professionals in the machine learning field through online communities, forums, and networking events. Explore job opportunities in companies actively seeking machine learning talent and tailor your applications to showcase your relevant skills and experience.

Machine learning is the applied version of data science. Deepening your understanding of machine learning algorithms, model deployment, and optimization is the place to start. Familiarize yourself with popular ML frameworks. Expand to some real world feature engineering, and gain hands-on experience with model training and evaluation.

I think the concept of moving from Data science to Machine Learning role is rather dependent on the organization. On top of your data science role, you can consider adding infrastructure and MLOps skills. Your core work might have a fundamental shift from building models to serving them which deals more with infrastructure and monitoring (depending on the organisation).

Machine Learning engineering is in high demand. On the other hand there is a lack of expertise on the market. It is actually difficult to find someone with both expertise in science and engineering. Therefore, if you are an expert on one don't be scared to upskill on the other one and enjoy the challenge!

In a world of so many algorithms getting developed every single day, do not try to master all of them. I suggest to do more of projects, understand the kirks of it and then do your R&D for algo’s available that can suit up your need. With every pro & con of that particular algo, you make your way to the path of optimising your model and enrich your experience; thats when you become an ‘Engineer’, who tries - fails - retries and keeps working on the loop of trying, despite failure or success. A machine learning engineer is the one who is ready to take over the work of data aggregation, data analyst, data scientist, business presentations and documentation work together in the industry along with the technical machine learning knowledge!

Machine learning is a critical component of data science - think of them as a dynamic duo that turn data into super useful insights and make genius decisions. Step 1- Researching and understanding different specializations within ML is a good place to start. Some common ones -Computer vision, NLP, Reinforcement Learning, Recommendation Systems and Time Series Analysis. Step 2- should be assessing your interests and passions - Consider what types of problems or applications within machine learning excite you the most. Think about the projects or aspects of your data science work that you enjoyed the most. Then follow by Step 3 - assessing your current skillset and filling the gaps with courses/ Hands-on Projects.

Engineering is more then theory. First understand all the constraints to the use case, then understand the tech limitations to the models. Only then will you find a solution that actually works in production.

Transitioning from data science to a machine learning role involves more than skill acquisition and project showcasing. It's crucial to delve into specialized domains within machine learning, like deep learning, natural language processing, or computer vision, depending on your career aspirations. Considering interdisciplinary collaborations, gaining domain-specific knowledge, and understanding ethical implications of AI applications are paramount. Additionally, exploring advanced techniques, staying updated with industry trends, and continuously refining your learning process will ensure a smoother transition and long-term success in a machine learning career.

ML Engineers are the builders and deployers of Machine learning / AI models, focusing on the technical aspects of model building, deploying , orchestration of pipelines while Data Scientists are the analysts and problem solvers, exploring data and experimenting various algorithms to give best output which are accurate and more close to business requirements . Both roles are integral to the successful implementation of machine learning / AI models and data-driven solutions in organizations.

Transitioning from a data scientist to a machine learning role involves: + Solidify Fundamentals: Review machine learning concepts and brush up on math/statistics. + Gain Practical Experience: Work on real-world ML projects (Kaggle, GitHub collaborations). + Create a Portfolio: Showcase projects and write articles to demonstrate expertise. + Network and Learn: Join ML communities, seek mentorship, and stay updated with courses and certifications.

Ask yourself why you want to transition. Data science is a fundamental underpinning to machine learning and machine learning is just one way to use data.