Comment se préparer à un entretien de machine learning sans expérience ?

I hope to share my recent interview at a fintech industry where I got through though I do not have a previous experience in the fintech industry. If you do not have any (industry) experience in ML, first hand it is not an excuse for you not to try anything on your own. Consider this cycle, Learn -> Build -> Publish in github like repositories -> Add the skill to your CV ->Share and get the feedback on LinkedIn. What is more important is the passion and the personal quality. I mean how soon you can grasp or learn any complex or job-related requirements is what makes the difference. Learn and Prepare like you have an interview by next week and it's your dream Job. Be truthful at the interview and admit If you don't know by the way.

To ace an ML interview with no prior experience, start by mastering the basics: get comfortable with linear algebra, calculus, statistics, and a programming language like Python, along with essential libraries. Take online courses, explore tutorials, and practice coding on platforms like HackerRank. Dive into hands-on projects using scikit-learn, TensorFlow, or PyTorch. Learn key ML algorithms, understand neural networks and deep learning basics, and grasp model evaluation metrics. Showcase your skills through a GitHub portfolio. Stay updated with the latest trends through blogs and conferences. Practice mock interviews to refine your communication and problem-solving skills. Focus on regular, incremental progress. Good luck!

Preparing for a machine learning (ML) interview without direct experience involves focusing on fundamental concepts, practical skills, and demonstrating your understanding of ML principles. - Understand Core Concepts:Mathematics, Stats, and ML Fundamentals - Learn python and tools/packages like pandas, sklearn, pytorch, keras, jupyter notebooks etc. - Enroll in online courses like Coursera's "Machine Learning" by Andrew Ng or "Deep Learning Specialization" or any similar quality MOOCs. - Practice on Kaggle Datasets and build personal projects. - Practise mock interview and look for ML interview questions on Github/Kaggle/LinkedIn

To excel in machine learning interviews, it's essential to grasp common techniques widely used in the field. Start by understanding supervised learning, unsupervised learning, classification, regression, clustering, dimensionality reduction, feature engineering, model selection, validation, and evaluation. Be prepared to explain how each technique functions, its strengths and weaknesses, and real-world use cases. Additionally, practice implementing popular algorithms like linear regression, logistic regression, k-means, principal component analysis, decision trees, random forests, support vector machines, and neural networks. Developing a deep understanding of these techniques is essential in acing the interview.

Study Fundamentals: Master core ML concepts like regression, classification, and model evaluation to build a solid foundation. Practice Projects: Work on personal projects using datasets from platforms like Kaggle to gain hands-on experience. Learn Algorithms: Understand popular algorithms like decision trees, SVMs, and neural networks, focusing on their applications. Mock Interviews: Practice with mock interviews or solve problems on platforms like LeetCode and HackerRank to refine problem-solving skills. Showcase Learning: Build a portfolio showcasing your projects, code on GitHub, or a blog to exhibit your knowledge and passion for ML.

I would like to add an ensemble learning i.e. combining predictions from multiple models. Familiarity with deep learning would be an additional asset.

Practicing real-world data sets is indispensable for honing your ML skills. The hands-on experience gained from tackling actual problems provides a better understanding of the entire project lifecycle. Utilizing diverse data sources like Kaggle, UCI Machine Learning Repository, or Google Dataset Search allows you to face the challenges that real-world data often presents. Creating your own data sets through web scraping, APIs, or sensor data collection adds an extra layer of complexity and realism to your practice. Following the complete workflow of an ML project is crucial for building a robust skill set. Documenting your process and code showcases your proficiency to potential employers.

Reviewing Kaggle notebooks is a very fast way to see how real data is handled. It also gives you the most efficient data-science and coding methods. If you have some missing points, don't hesitate to ask the content owner; the Kaggle community is very helpful!

An effective approach will be tackling real-world use cases, identify an use case. Apply your knowledge and try solving it various ways. Engage in collaborative brainstorming sessions with seasoned experts to glean insights into various perspectives. Explore analogous problems in interactive forums like Kaggle, observing how the community addresses comparable challenges for valuable learning experiences. This could involve an ability to frame a business problem as a machine learning problem and successfully address it from start to finish, encompassing tasks from data preprocessing to model evaluation.

This is the most important thing to do for ML interviews. This is what got me my first job in Machine Learning! Building a portfolio gives you 2 advantages: 1. It’s a proof that you have worked on a real project and delivered results 2. You learn better when doing so you actually know what you’re talking about from a practical standpoint. The second one is very crucial when layered with the first one. Now you can share the challenges you faced and how you solved them and point out to your projects. Refer back to different parts of your projects when answering different questions. This makes the interviews smoother and much easier. Comment PORTFOLIO on my latest post and I’ll send you a list of resources for creating your portfolio!

As a student pursuing Post Graduation in AI, gearing up for machine learning interviews involves a strategic approach. Recognizing the diverse nature of these interviews, encompassing technical, behavioral, and situational aspects, is crucial. Technical questions delve into your grasp of machine learning concepts and algorithms, spanning theoretical to practical applications. Behavioral inquiries probe into your personality, motivation, and communication skills, exploring facets like strengths, weaknesses, and achievements. Situational questions assess your problem-solving prowess in real-world scenarios. Preparation entails a comprehensive review of your resume, portfolio, and past projects

In my opinion, the question lacks a crucial part to identify the answer structure which is: what is the role (e.g. product manager) and what is the project scope (e.g. research project). Based on the answer, the approach can be structured.

The questions vary drastically based on the job requirements even when the title is Machine Learning Engineer. So prepare for the job roles and not for the title. A few common questions that are relevant for all of those roles are: 1. Difference between Classification and Regression 2. What is P value and when do you use it? 3. Different evaluation metrics and why use each of them.

Experience matters for an interview where you can relate all your questions to the work you have done and answer to the point. So it will be good to keep solving some real time examples. Along with it stay in trend and know the recent advancements and trends in machine learning. Follow some communities and blogs or websites which will be very helpful to be in trend.