What do you do if your Machine Learning projects need a portfolio boost?

It is not only about technical aspects, which are of course essential. For instance, feature engineering is also about context, requiring substantial data analysis and domain knowledge. If you are not an expert of the context, you have to ask someone and work alongside. On top of that, for each phase, be prepared to iterate a lot. With regards to the models, start with statistics first. Then progress into essential algorithms.

When enhancing your Machine Learning portfolio, especially within the dynamic environment of a tech startup, consider adopting a strategy I found effective: Core Concepts: Start by reinforcing your grasp of crucial ML algorithms and where to apply them. This foundational knowledge is key. Data Preprocessing: Invest time in honing your data cleaning and feature engineering skills. These are vital for boosting your models' performance. Complex Projects: Once comfortable, elevate your portfolio by incorporating complex projects that demonstrate advanced algorithms and meaningful outcomes. Ongoing Learning: Keep yourself in the loop with the latest ML trends through continuous learning. Online courses at BOTCampusAI

To enhance your machine learning portfolio, consider the following approaches: ? Undertake personal projects: Tackle real-world problems or explore interesting datasets independently. Document your process, methodologies, and outcomes to showcase your capabilities. ? Contribute to open-source projects: Participate in collaborative projects related to machine learning on platforms like GitHub. Your contributions demonstrate not only technical skills but also your ability to work in a team. ? Engage in machine learning competitions: Participate in competitions such as those on Kaggle. Even if you don’t win, the experience and solutions you develop can be valuable additions to your portfolio.

To refine the basics in machine learning, revisit core concepts such as regression, classification, and clustering. Practice implementing algorithms from scratch to deepen your understanding. Work on projects that require these fundamentals to apply them in practical scenarios. Reading books and taking online courses can also help reinforce your knowledge.

Real-time data presents unique challenges such as data velocity, volume, and variety, requiring specialized techniques for data preprocessing, feature engineering, and model deployment. By successfully addressing these challenges, machine learning projects involving real-time data showcase the ability to develop robust and scalable solutions that can operate in dynamic environments. Moreover, tackling real-time data highlights proficiency in implementing streaming data pipelines, online learning algorithms, and real-time analytics, which are highly valued skills in industries such as finance, healthcare, e-commerce, and IoT.

Dive into real data to unlock the secrets hidden within. By tackling real-world datasets, you demonstrate not only your technical prowess but also your ability to navigate the complexities of messy, unstructured data—a skill highly valued in the world of machine learning.

NAVIGATE THE CHAOS: REAL-WORLD DATA IN MACHINE LEARNING - SEEK OUT CHALLENGING DATASETS: Explore platforms hosting data science competitions or open data repositories for real-world datasets. This exposure to complex data sharpens your problem-solving skills. - MASTER DATA CLEANING: Develop a proficiency in managing unstructured, messy data. Your ability to clean and preprocess data is a critical skill that employers highly value. - SHOWCASE INSIGHT EXTRACTION: Demonstrate your capability to explore data and extract meaningful insights. Projects that reveal your analytical prowess with real datasets highlight your readiness for industry challenges.

Obviously real data are crucial, but it is not always possible to have them on hand. Many data scientists tend to stop without data. However, there are plenty of sources - Github groups - Kaggle/UCI/Google - Azure open datasets However, data must be carefully chosen 1- Must be relevant to the case and your ultimate goal 2- They must present some issue in terms of cleaning, completeness, etc, otherwise they are not real data :) 3- You must be able to use them in your model, effectively Then, real-real data will always be another league :)

For a standout Machine Learning portfolio, it's essential to engage with real-world data: Find Real-World Datasets: Leverage platforms hosting data science competitions or explore open data repositories. Demonstrate Data Handling: Showcase your proficiency in managing real, often messy data—cleaning, exploring, and deriving insights. Illustrate Industry-Ready Skills: Through projects with real datasets, exhibit your readiness to tackle industry-level challenges, emphasizing your problem-solving and analytical abilities.

The greatest benefit to mankind (data scientistkind to be precise) is the availability of open source models and work from top companies and data scientists. To start with you might not need to build your entire code base from scratch, if you get inspired by a certain library, fork and clone the repository and make tweaks to your copy on GitHub, this way you can start building a portfolio of interesting projects easily and then expand your library with your own personal projects. The other way is to compete in kaggle and make the code available on GitHub and kaggle, hugging face, streamlit are other community based platforms where you can share your code and app.

To enhance your Machine Learning portfolio: Use GitHub: Host your project code on platforms like GitHub, demonstrating openness and collaboration. Maintain Clean Code: Ensure your code is clean and well-commented for easy understanding. Include Detailed README: Add a README file detailing your project’s objectives, methodology, and results, showcasing your ability to articulate your work. Showcase Documentation Skills: Highlight your capability to document and communicate your process, crucial for teamwork.

In general, for a data scientist being competent in using Git repositories is essential. In this context, here are what I suggest. - Set up a personal portfolio website on GitHub. There are tons of easy-to-follow templates on the Internet. - For each project set up a repo with a clear name and README file. You can use AI to get recommendations for README using the project descriptions. - Push your code to the repo. Try to have a minimal clean code. If you are using Notebooks try to have a proper markdown for each cell. For actual codes have proper comments whenever necessary. - Set up a page on your portfolio website with hyperlinks to your projects. - Share your code with experts and ask them to give you feedbacks.

Develop a complete solution around your model, such as a web or mobile application. This demonstrates your ability to apply machine learning in practical, user-oriented contexts, showing off not just your ML skills but also your software development capabilities. Publish your complete project code on GitHub or a similar platform. Ensure it’s well-organized and documented, making it easy for others to understand, use, and contribute.

Il faut créer des projets de bout en bout en mettant en exergue la plus value apportée à chaque fois. Depuis le besoin jusqu'à la mise en production. Cela montre que vous pouvez traiter un projet de bout en bout en vous rapprochant de l'objectif fixé au début du projet. Et tout employeur rêve de quelqu'un qui ma?trise non seulement la technique mais aussi la pratique. Il faut partager aussi son code et expliquer pourquoi on a préféré une méthodologie plut?t qu'une autre.

- Join ML community - Participate in hackathons and competitions - Attend meetups - Engage on linkedin or other socials Always be prepared to change your mind and to adapt to learning new stuff, especially if they contradict your point of view. ML and AI are about experiments and research

To incorporate feedback effectively: Seek Peer Insights: Engage with the machine learning community for feedback, using forums or meetups. Embrace Constructive Criticism: Show openness to learning by valuing constructive feedback. Demonstrate Professional Growth: Highlight your adaptability and eagerness to enhance your skills based on feedback. Identify Learning Opportunities: Use the feedback to pinpoint and address any knowledge or skill gaps.

Without feedbacks you are driving blind. It is the best way to remove bias from your evaluations. Often, feedback from customers directly translates to feedback from the market. It requires constant improvement based on the feedback

One solid project is worth a thousand poor ones. With ChatGPT and GitHub, it’s very easy to show multiple projects. But as an industry insider, i know that it takes just one question to destroy this house-of-cards project portfolio which has been either cloned or created using LLMs. It’s not a sin to use AI tools, but one should know why a certain code has been used instead of another. Most importantly data science is a means to an end. The focus should always be solving the business problem. With this context, take one business problem, and do a thorough job. Don’t go after fancy algorithms. Using statistical tests alone can give you the results without employing a complex machine learning model. As always “KYC” - Know Your Code

By focusing on a specialized area, we can undertake projects that delve deeply into the intricacies and challenges of that domain, showcasing our ability to tackle complex problems and deliver innovative solutions. Specialized projects not only highlight technical proficiency but also signal a commitment to continuous learning and improvement in a specific field, making portfolios more appealing to potential employers or clients seeking specialized expertise. Moreover, specialized skills in high-demand areas like NLP or Computer Vision can open doors to opportunities in industries that require these focused skills, offering unique and competitive advantages over generalists in the Machine Learning field.

To effectively explore specialization in machine learning: Identify Your Interest Area: Choose a niche within machine learning, like natural language processing or computer vision. Deep Dive into the Specialization: Commit to gaining in-depth knowledge in your chosen area. Showcase Dedicated Projects: Create projects that reflect your expertise in the specialization. Stand Out in the Job Market: Use your specialized skill set to differentiate yourself from generalists, targeting industries that value these specific abilities.

KEEP AHEAD OF THE CURVE: STAYING UPDATED IN MACHINE LEARNING - FOLLOW INDUSTRY TRENDS: Regularly engage with the latest research, news, and advancements in machine learning. Utilize platforms like arXiv for the newest papers and technology blogs for trends. - INTEGRATE NEW TECHNIQUES: Actively incorporate cutting-edge algorithms and methodologies into your projects. This demonstrates your ability to apply the latest solutions to real-world problems. - SHOWCASE INNOVATION: By staying updated and applying fresh techniques, you signal to employers your commitment to innovation and your potential to contribute to the forefront of technology.

If your solution has a large language model (LLM) as an irreplaceable component, I would recommend using an open weight model and building your pipeline on open-source code in order to stay up-to-date. As LLMs evolve rapidly, using a closed model poses risks of being locked in. Most researchers release open-source code alongside their papers, and the open-source community (such as Hugging Face) makes it very easy to switch to the latest technology. Making your solution LLM-agnostic sounds promising but may not be feasible, as there can be model-specific quirks that you have to incorporate into your prompt or dataset to ensure proper functioning.

If you think your projects portfolio isn't getting you the job you want, you can do a project closely related to the field you want to work. For example, if it's in research, work on a research project in a field your proposed company works. If it's in engineering, learn about cloud computing and server side management and build projects in that area. Having this mix will definitely make you stand out.