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Over the last year, I have written several articles that help you prepare for data science technical interviews. Here they are: 1️⃣ Machine Learning (ML) Interview 1. Types of ML Q& A In DS Interview https://shorturl.at/syN37 2. ML Interview Q & A for Data Scientists https://shorturl.at/HVWY0 3. Crack the ML Coding Q & A https://shorturl.at/CDW08 4. Deep Learning Interview Q & A https://shorturl.at/lHPZ6 5. Top LLMs Interview Q & A https://shorturl.at/wGRSZ 6. Top CV Interview Q & A [Part 1] https://rb.gy/51jcfi 7. Part 2 https://rb.gy/hqgkbg 8. Part 3 https://rb.gy/5z87be 2. SQL Interview Preparation 1. 13 SQL Statements for 90% of Your Data Science Tasks https://rb.gy/dkdcl1 2. SQL Window Functions: Simplifying Complex Queries for Efficient Data Analysis https://t.ly/EwSlH 3. Ace the SQL Questions in the Technical Interview https://lnkd.in/gNQbYMX9 4. Unlocking the Power of SQL: How to Ace Top N Problem Questions in Interviews https://lnkd.in/gvxVwb9n 5. How To Ace the SQL Ratio Problems https://lnkd.in/g6JQqPNA 6. Cracking the SQL Window Function Coding Questions https://lnkd.in/gk5u6hnE 7. SQL & Database Interview Q & A https://lnkd.in/g75DsEfw 8. 6 Free Resources For SQL Interview Preparation https://lnkd.in/ghhiG79Q 3️⃣ Programming Questions 1. Foundations of Data Structures: Essential Concepts for DS Coding Interviews [Part 1] https://lnkd.in/gX_ZcmRq 2. Part 2 https://lnkd.in/gATY4rTT 3. Top Important Python Questions [Conceptual Questions] https://lnkd.in/gJKaNww5 4. Top Important Python Questions [Data Cleaning and Preprocessing] https://lnkd.in/g-pZBs3A 5. Top Important Python Questions [Machine & Deep Learning ] https://lnkd.in/gZwcceWN 6. Python Interview Q & A https://lnkd.in/gcaXc_JE 7. 5 Python Tips for Acing DS Coding Interview https://lnkd.in/gsj_Hddd 4️⃣ Statistics 1. Mastering 5 Statistics Concepts to Boost Your Success https://lnkd.in/gxEuHiG5 2. Mastering Hypothesis Testing for Interviews https://lnkd.in/gSBbbmF8 3. Mastering A/B testing: Introduction to A/B Testing https://lnkd.in/g35Jihw6 4. Statistics Interview Q & A for Data Scientists https://lnkd.in/geHCCt6Q 5️⃣ Probability 1. 15 Probability Concepts to Review Before DS Interview [Part 1] https://lnkd.in/g2rK2tQk 2. Part 2 https://lnkd.in/gQhXnKwJ 3. Probability Interview Q & A [Conceptual Questions] https://lnkd.in/g5jyKqsp 4. Probability Interview Q & A [ Mathematical Questions] https://lnkd.in/gcWvPhVj ➡ You can find all the links in this GitHub Repo: https://lnkd.in/djcgcKRT ⭐ Subscribe to my newsletter To Data & Beyond to receive similar content: https://lnkd.in/dVkD-7WT 📒 My ebook Data Science Portfolio for Success: Your Ultimate Guide to Building a Data Science Portfolio: https://lnkd.in/dMs_53Wz

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I've interviewed around 80 candidates for ML/AI Engineer positions across various levels over the last 3 months. Many people have asked me to share my main takeaways from these interviews. Here are the key areas where many candidates faced challenges: 1. Lack of basic understanding of core ML concepts. Many engineers lack knowledge of fundamental concepts such as backpropagation, regularization, and loss functions. Every ML engineer needs to have a solid foundation in these basics. 2. Superficial knowledge of domain-specific architectures. Candidates for specialized roles often couldn't explain core concepts of their domain. For example, NLP Engineer candidates working on LLM projects struggled to explain how Transformers work. This superficial knowledge is usually insufficient for custom AI development, which requires deeper understanding. 3. Falling behind on the latest developments. Many candidates had good foundational knowledge but were unaware of the latest advanced techniques. Staying informed about new research, frameworks, and tools is essential for modern developers to maintain technological competitiveness and solve business problems effectively. These insights have inspired action. We're developing a curated list of essential topics for ML/AI engineers and committing to provide valuable, actionable feedback after each interview. Our goal is clear: to cultivate and empower AI talent in our dynamic, in rapidly evolving field. What AI/ML concepts seem to be the "Achilles' heel" for most candidates you've encountered?