{"product_id":"110318","title":"Inside Machine Learning Interview ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYEEOAuSkvXlWIzXHiTBzr2Erkg2W.png?v=1765099683\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Inside Machine Learning Interview \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/125294888\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003eFrom the basics of ML interviews to practical approaches for real-world applications.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e To confidently answer any ML interview question, you need to clearly define the entire ML workflow and related core concepts.\u003cbr\u003e This book covers everything from ML fundamentals and coding interviews to system and infrastructure design interviews, step by step, examining the problems and solution strategies that applicants must prepare for.\u003cbr\u003e Drawing on his experience interviewing nearly a thousand candidates at Amazon, Twitter, and AI startups, the author uncovers 194 frequently asked questions in big tech ML interviews and offers tips for formulating the best answers.\u003cbr\u003e Let's use the book's keyword-focused, clear answers and interview tips to reinforce your strengths and thoroughly address your weaknesses, leading to a successful interview.\u003cbr\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003eChapter 1: Preparing for an ML Interview\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Technical phone screen\u003cbr\u003e ML Basics Knowledge Interview\u003cbr\u003e ML coding interview\u003cbr\u003e ML System Design Interview\u003cbr\u003e Other interviews\u003cbr\u003e Essential Elements of a Good Answer\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: ML Basics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q2.1 Dataset Collection Step\u003cbr\u003e Q2.2 Problems with data collection\u003cbr\u003e Q2.3 Considerations when Collecting Data\u003cbr\u003e Q2.4 Handling Label Imbalance\u003cbr\u003e Q2.5 Handling missing labels\u003cbr\u003e Q2.6 Input Feature Type\u003cbr\u003e Q2.7 Feature Selection and Importance\u003cbr\u003e Q2.8 Feature Selection Method\u003cbr\u003e Q2.9 Missing feature values\u003cbr\u003e Q2.10 Modeling Algorithm\u003cbr\u003e Q2.11 How Logistic Regression Works\u003cbr\u003e Q2.12 Logistic Regression Loss Function\u003cbr\u003e Q2.13 Gradient Descent Optimization\u003cbr\u003e Q2.14 Hyperparameter Tuning\u003cbr\u003e Q2.15 Handling model overfitting\u003cbr\u003e Q2.16 Normalization Techniques\u003cbr\u003e Q2.17 Linear Regression and Logistic Regression\u003cbr\u003e Q2.18 Neural network activation function\u003cbr\u003e Q2.19 Decision Trees, Random Forests, and Gradient Boosting Decision Trees\u003cbr\u003e Q2.20 Boosting and Bagging\u003cbr\u003e Q2.21 Unsupervised Learning Techniques\u003cbr\u003e Q2.22 How k-means works\u003cbr\u003e Q2.23 Semi-supervised learning techniques\u003cbr\u003e Q2.24 Loss Function Types\u003cbr\u003e Q2.25 Convexity of loss function \u003cbr\u003eQ2.26 Classification Model Evaluation Indicators\u003cbr\u003e Q2.27 Regression Model Evaluation Indicators\u003cbr\u003e Q2.28 Model Optimization\u003cbr\u003e Q2.29 Model Performance Improvement\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3 ML Coding\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q3.1 k-means\u003cbr\u003e Q3.2 k-nearest neighbors\u003cbr\u003e Q3.3 Decision Tree\u003cbr\u003e Q3.4 Linear Regression\u003cbr\u003e Q3.5 Evaluation Criteria\u003cbr\u003e Q3.6 Reservoir Sampling\u003cbr\u003e Q3.7 Probability Problem\u003cbr\u003e Q3.8 Hash Table and Distributed Programming Problems\u003cbr\u003e Q3.9 Graph Problem\u003cbr\u003e Q3.10 String Problem\u003cbr\u003e Q3.11 Array Problem\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: ML System Design 1 - Recommender Systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q4.1 System Purpose\u003cbr\u003e Q4.2 System Indicators\u003cbr\u003e Q4.3 Recommended Content Types\u003cbr\u003e Q4.4 Recommended Content Mix\u003cbr\u003e Q4.5 System operating parameters\u003cbr\u003e Q4.6 System Components\u003cbr\u003e Q4.7 Cold start problem\u003cbr\u003e Q4.8 Dataset Type\u003cbr\u003e Q4.9 Dataset Collection Techniques\u003cbr\u003e Q4.10 Dataset Bias\u003cbr\u003e Q4.11 Mitigating Serving Bias\u003cbr\u003e Q4.12 Mitigating position bias\u003cbr\u003e Q4.13 Source of recommended candidates\u003cbr\u003e Q4.14 Recommendation Candidate Generation Step\u003cbr\u003e Q4.15 Recommendation Candidate Generation Algorithm\u003cbr\u003e Q4.16 Embedding Technology\u003cbr\u003e Q4.17 Candidate Scoring for Large-Scale Recommender Systems\u003cbr\u003e Q4.18 New Content Indexing\u003cbr\u003e Q4.19 Merging and organizing recommended candidates \u003cbr\u003eQ4.20 Pre-ranking model training\u003cbr\u003e Q4.21 Pre-ranking model evaluation indicators\u003cbr\u003e Q4.22 Pre-ranking model algorithm\u003cbr\u003e Q4.23 Pre-ranking model optimization\u003cbr\u003e Q4.24 Key Features of the Ranking Model\u003cbr\u003e Q4.25 Text or ID-based features\u003cbr\u003e Q4.26 Count-based features\u003cbr\u003e Q4.27 Training a Heavy Ranking Model\u003cbr\u003e Q4.28 Heavy Ranking Model Algorithm\u003cbr\u003e Q4.29 Ranking Model Architecture\u003cbr\u003e Q4.30 Ranking Model Predicted Value Correction\u003cbr\u003e Q4.31 Ranking Model Evaluation Indicators\u003cbr\u003e Q4.32 Multi-task model and individual model\u003cbr\u003e Q4.33 Model Serving System\u003cbr\u003e Q4.34 Caching\u003cbr\u003e Q4.35 Model Update\u003cbr\u003e Q4.36 Online Experiment\u003cbr\u003e Q4.37 Model Load\u003cbr\u003e Q4.38 Model Experiment Considerations\u003cbr\u003e Q4.39 Offline Evaluation Index\u003cbr\u003e Q4.40 Online performance degradation\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 ML System Design 2 - Applications\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q5.1 Document Parsing\u003cbr\u003e Q5.2 Sentiment Analysis\u003cbr\u003e Q5.3 Topic Modeling Techniques\u003cbr\u003e Q5.4 Document Summary\u003cbr\u003e Q5.5 Natural Language Understanding\u003cbr\u003e Q5.6 Supervised Learning Labels\u003cbr\u003e Q5.7 Unsupervised Learning Features\u003cbr\u003e Q5.8 Discriminative Problem Features\u003cbr\u003e Q5.9 Generative Model Features\u003cbr\u003e Q5.10 Building an Information Extraction Model\u003cbr\u003e Q5.11 Information Extraction Evaluation Index\u003cbr\u003e Q5.12 Building a Classification Model \u003cbr\u003eQ5.13 Building a Regression Model\u003cbr\u003e Q5.14 Topic Assignment\u003cbr\u003e Q5.15 Topic Modeling Evaluation Indicators\u003cbr\u003e Q5.16 Building a Document Clustering Model\u003cbr\u003e Q5.17 Clustering Evaluation Indicators\u003cbr\u003e Q5.18 Building a Text Generation Model\u003cbr\u003e Q5.19 Text Generation Evaluation Index\u003cbr\u003e Q5.20 Modeling Workflow\u003cbr\u003e Q5.21 Offline Forecast\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: ML Infrastructure Design\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q6.1 Accelerate model development\u003cbr\u003e Q6.2 Accelerate Model Learning\u003cbr\u003e Q6.3 Model Training Distribution\u003cbr\u003e Q6.4 Model Training Pipeline Evaluation\u003cbr\u003e Q6.5 Distributed Learning Error\u003cbr\u003e Q6.6 Model Update\u003cbr\u003e Q6.7 Model Optimization\u003cbr\u003e Q6.8 Serving System Components\u003cbr\u003e Q6.9 Problems during serving\u003cbr\u003e Q6.10 Feature Sign Language Improvement\u003cbr\u003e Q6.11 Latency Improvement\u003cbr\u003e Q6.12 Handling Multiple Requests\u003cbr\u003e Q6.13 Model update during serving\u003cbr\u003e Q6.14 Model Deployment and Rollback\u003cbr\u003e Q6.15 Server Monitoring\u003cbr\u003e Q6.16 Performance degradation during serving\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Advanced ML Problems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Q7.1 Delayed Label\u003cbr\u003e Q7.2 Learning without labels\u003cbr\u003e Q7.3 Pricing Model\u003cbr\u003e\u003cbr\u003e Appendix A Generative Models: From Noisy Channel Models to LLM\u003cbr\u003e A.1 Machine Translation (MT)\u003cbr\u003e A.2 Automatic Speech Recognition (ASR)\u003cbr\u003e A.3 Convergence to transformers \u003cbr\u003eA.4 Fine-tuning for real-world challenges\u003cbr\u003e\u003cbr\u003e References\u003cbr\u003e Search\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4438\/MidCate007\/443766391.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eA veteran interviewer with experience at Amazon, Twitter, and AI startups.\u003cbr\u003e Tips for a Successful ML Interview from an ML Engineer\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Applicants facing ML interviews may have many concerns, such as what skills to develop, what topics to focus on, and what to consider when answering questions.\u003cbr\u003e This book provides a comprehensive overview of the skills needed for employment in the ML field, along with robust, practical problem-solving strategies, appendices, and reference materials reflecting the latest technologies.\u003cbr\u003e\u003cbr\u003e First, essential practical workflow knowledge required for employment in the ML field.\u003cbr\u003e Contains 194 frequently asked questions from ML interviews at big tech companies, including FAANG.\u003cbr\u003e It consists of concise questions and clear, keyword-based answers, making it useful for organizing content before the interview. \u003cbr\u003eTo aid understanding, we have added extensive footnotes to difficult concepts or ambiguous expressions.\u003cbr\u003e\u003cbr\u003e Second, step-by-step instructions for preparing for basic to advanced problems.\u003cbr\u003e It consists of five interview sessions (ML Basics - ML Coding - ML System Design - ML Infrastructure Design - Advanced ML Problems), making it easy to find content based on the position you are applying for, difficulty level, and needs.\u003cbr\u003e\u003cbr\u003e Third, a strategy to construct a powerful answer centered on core keywords.\u003cbr\u003e We'll introduce answer-structuring strategies for scoring well in ML interviews, including extracting key keywords from the question, comparing different approaches, and discussing pros, cons, and tradeoffs.\u003cbr\u003e\u003cbr\u003e Fourth, a practical problem-solving approach based on real-world scenarios.\u003cbr\u003e Covering practical concepts and scenarios across the ML lifecycle, it helps both job seekers preparing for interviews and experienced practitioners solidify their fundamentals.\u003cbr\u003e \u003cbr\u003e\u003cb\u003eReaders who need this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Job seekers hoping to pursue a career in ML\u003cbr\u003e - Practitioners who want to increase their competitiveness in the ML field \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e March 15, 2024\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 332 pages | 776g | 183*235*19mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169212120\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1169212123 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893771010090,"sku":"110318","price":37.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/dd3a28997bd571372ed26d6330a913af.jpg?v=1765415925","url":"https:\/\/librairie.coreenne.fr\/en\/products\/110318","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}