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@NerdNoCap
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👉 That’s all for today! I hope these tips and questions will help you prepare for your machine learning interview. Good luck and happy learning! 😊
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❓ How do you prune a decision tree? 🌳 ❓ What’s the difference between supervised learning and unsupervised learning? 🙌 ❓ How do you evaluate the effectiveness of your machine learning model? 📈 ❓ How do you prefer to visualize your results? 🎨
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👉 Here are some common machine learning interview questions that you should prepare for: ❓ How do you handle missing or corrupted data in a data set? 💥 ❓ Explain the difference between deep learning, artificial intelligence (AI), and machine learning. 🤖
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🔥 If you’re unsure of an answer, it’s OK to say so. Don’t try to bluff or make up an answer. Instead, admit that you don’t know the answer, but show how you would approach the problem or where you would look for more information. 🙋♂️
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🔥 Be sure to write clearly. You may be asked to write some code or pseudo during the interview. Make sure your code is readable, commented, and follows coding practices. Use a code editor or an online platform like Google Colab or Jupyter Notebook to write and test your code.
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🔥 Research the company. Find out what kind of machine learning problems they are working on, what tools and frameworks they use, and what their goals and values are. This will help you tailor your answers and show your interest and fit for the role. 🕵️♂️
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🔥 Focus on what you know. If you mention a method in your answer, chances are the interviewer will ask you more about it. Be prepared to explain how it works, why you chose it, and what are its advantages and limitations. 🤓
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🔥 Apply concepts and work on your relevant skills. Connect your answers with real-life examples, especially ones that reference your own work. 📊
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👉 Machine learning interviews are a great opportunity to showcase your skills, knowledge, and work. You can expect to be asked about the technical and conceptual aspects of machine learning, as well as your experience and passion for the field. 🧠
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5. Practice writing complex queries that involve multiple tables and logic with resources like Hackerrank's SQL tutorial. #ComplexQueries #PracticeMakesPerfect
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3. Database design principles like normalization and relationships will help you create efficient databases. Stanford Database Course is an excellent resource for learning about it. #DatabaseDesign #Efficiency
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2. Understanding data types and SQL functions like COUNT, SUM, AVG, and MAX is crucial. Check out Mode Analytics' SQL tutorial to learn about them. #DataTypes #SQLFunctions
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1. Start with understanding the basics of SQL syntax and keywords with resources like w3schools' SQL tutorial. #SQL #BeginnerFriendly
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👉 Talk Stats Forum - https://t.co/cYb28IfkJU A free online community where you can ask questions and get answers from other statistics enthusiasts Browse through previous threads and learn from other people’s experiences and insights
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👉 StatTrek for AP Statistics - Covers the topics required for the AP Statistics exam Learn how to design experiments, collect data, summarize data, draw conclusions, and communicate results https://t.co/8oEdNjoLVy
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👉Statistics: Methods and Applications (StatSoft) - Covers a wide range of statistical methods and applications, from exploratory data analysis to multivariate techniques Learn how to use Statistica software for data analysis https://t.co/8Rr5KsaaZg
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