[리뉴얼] 처음하는 MongoDB(몽고DB) 와 NoSQL(빅데이터) 데이터베이스 부트캠프 [입문부터 활용까지] (업데이트)
안녕하세요? 21번 강의에서 4:04 초에 보면, db.users.find({hobbies: {$in: ["축구", "요리"]}}) -> 이 부분을 sql 로 변형하면 select * from users where hobbies like "%축구%", or hobbies like "%요리%" 와 유사하다고 하셨는데 아래 sql 이 더 가까운게 아닌지 문의드립니다. SELECT * FROM users WHERE hobbies IN ('축구', '요리') gemini 한테도 물어보니까 $in 은 정확히 일치하는 항목을 찾는거니까 select .....hobbies in ('축구', '요리') 가 더 비슷할거 같다고 하더라고요.
생성자와 메서드 8:49 상속 관련하여 알려주고 있는데 해당 코드 관련해서 자식이 태어나려면 부모꺼를 먼저 호출하라고 들었습니다. 호출 답 관련해서 상세하게 설명요청드립니다. 어떤 것은 부모 호출을 먼저한 뒤 자식을 호출하고 어떤 것은 부모 호출 없이 자식만 호출하는 것 차이를 모르겠습니다.
(AI 퀀트) 코드 한 줄 안 쓰고 주식 자동 분석 시스템 만들기 feat. Claude CLI
hello.py 파일을 만들어줘. "안녕하세요! 바이브 코딩을 시작합니다." 를 출력하는 코드. 만들고 나서 실행까지 해줘. 이거를 입력하고 나서 이런 사진이 떴고 엔터를 누르니 이렇게 떴고 동영상 순서에 따라 터미널 +눌러서 python.py 를 쳐보니 이렇게 떴습니다. 어떻게 해야할지 모르겠습니다.
(AI 퀀트) 코드 한 줄 안 쓰고 주식 자동 분석 시스템 만들기 feat. Claude CLI
안녕하세요. 호두감자님 강의 잘 보고 따라하고 있습니다. 올라온것까지 다 따라서 구현했는데요. 앞으로 남은 강의 목차확인 가능할까요? 실제로 한투 API까지 연결해서 주식주문을 내고 싶은데, 그것까지 포함인지 궁금합니다. 💡 수강생 안내 사항 (커뮤니티 가이드) 학습 관련 질문 : 구체적으로 적을수록 정확한 답변을 받을 수 있습니다. 작성 팁 : 마크다운과 단축키를 활용하면 글을 더 편리하게 작성할 수 있습니다. 사전 검색 : 질문을 남기기 전, 커뮤니티 질문 & 답변에 비슷한 내용이 있었는지 먼저 검색해 보세요. 커뮤니티 매너 : 서로 예의를 지키며 존중하는 분위기를 함께 만들어가요. 서비스 문의 : 인프런 서비스 관련 문의는 1:1 문의하기 를 이용해 주세요.
(AI 퀀트) 코드 한 줄 안 쓰고 주식 자동 분석 시스템 만들기 feat. Claude CLI
💡 수강생 안내 사항 (커뮤니티 가이드) 학습 관련 질문 : 구체적으로 적을수록 정확한 답변을 받을 수 있습니다. 작성 팁 : 마크다운과 단축키를 활용하면 글을 더 편리하게 작성할 수 있습니다. 사전 검색 : 질문을 남기기 전, 커뮤니티 질문 & 답변에 비슷한 내용이 있었는지 먼저 검색해 보세요. 커뮤니티 매너 : 서로 예의를 지키며 존중하는 분위기를 함께 만들어가요. 서비스 문의 : 인프런 서비스 관련 문의는 1:1 문의하기 를 이용해 주세요.
for문과 if문을 통해 가장 첫번째 달성하는 순간 return(i,j)까지는 이해 했는데요. 해당 코드를 봤을때, 첫번째 i for문과 두번째 j for문이 있는데 가장 밖에 return -1은 첫번째 i for문에 return이 아닌 함수 fp(arr)의 return으로 봐야하는건가요? i for문과 j for문에 해당하는 리턴값이 없을경우에 영향을 미치는 걸까요? return(i,j) 값을 받아 함수를 탈출할때, return -1은 영향이 없는지 궁금합니다.
안녕하세요, 강사님. 수업에서 다루지 않은 내용이지만 질문할 곳이 없어 이렇게 글을 남깁니다. 저는 혼자서 코드 필사를 하며 머신러닝을 공부하고 있는데, 일부 코드가 이해가 잘 되지 않아 문의드립니다. 제가 질문하고 싶은 코드의 흐름은 다음과 같습니다. 트리 깊이에 따라 모델 성능 비교 적절한 트리 깊이 선택 선택한 트리를 시각화 코드 필사를 하면서 트리 깊이를 선택하는 과정은 이해했지만, 트리 깊이를 선택한 후에 트리를 시각화하기 전에 학습 데이터만 사용하여 모델을 fit하는 것 이 의문이 들었습니다. model = DecisionTreeClassifier(max_depth=5, criterion='entropy') model.fit (X.iloc[train_idx], Y.iloc[train_idx]) 개인적으로는 타겟값 원인 분석을 위해 학습 데이터와 검증 데이터를 함께 사용하는 것이 더 나을 것 같은데, 이 부분이 맞는지 확신이 서지 않아 문의드립니다. 혹시 이에 대해 설명해주실 수 있을까요? 아래는 질문 관련 코드입니다. # Parameter Searching ==> Depth 2 ~ 10 for i in range(2,11,1): print(">>>> Depth {}".format(i)) model = DecisionTreeClassifier(max_depth=i, criterion='entropy') model.fit (X.iloc[train_idx], Y.iloc[train_idx]) # Train Acc y_pre_train = model.predict(X.iloc[train_idx]) cm_train = confusion_matrix(Y.iloc[train_idx], y_pre_train) print("Train Confusion Matrix") print(cm_train) print("Train Acc : {}".format((cm_train[0,0] + cm_train[1,1])/cm_train.sum())) # Test Acc y_pre_test = model.predict(X.iloc[valid_idx]) cm_test = confusion_matrix(Y.iloc[valid_idx], y_pre_test) print("Valid Confusion Matrix") print(cm_test) print("TesT Acc : {}".format((cm_test[0,0] + cm_test[1,1])/cm_test.sum())) print("-----------------------------------------------------------------------") print("-----------------------------------------------------------------------") # Depth가 깊어질 수록 정확도는 높게 나오지만 해석력에 대한 가독성을 위해 Depth 5를 선택함 model = DecisionTreeClassifier(max_depth=5, criterion='entropy') model.fit (X.iloc[train_idx], Y.iloc[train_idx]) from sklearn import tree # Creating the tree plot # Creating the tree plot (left = True, Right = False) tree.plot_tree(model, filled=True, feature_names=X.columns, class_names = ['BAD', 'GOOD']) plt.rcParams['figure.figsize'] = [40,10]
[리뉴얼] 처음하는 MongoDB(몽고DB) 와 NoSQL(빅데이터) 데이터베이스 부트캠프 [입문부터 활용까지] (업데이트)
안녕하세요, 현재 studio 3t 설치하려면 첨부한 이미지에서 다운받는게 맞나요? 근데 개인정보 입력시 이메일 부분에 gmail.com 같은 일반 이메일말고 회사 이메일을 적용해야 next 로 넘어갈 수 있는 것 같습니다. 아직 회사에 다니는 상황이아닌데 다르게 다운 받는 방법 있으면 안내부탁드립니다 .
1. 현재 학습 진도 2챕터 과제, 끝에서 K번째 노드값 구하는 문제 LinkedList 2. 어려움을 겪는 부분 fast 포인터를 k만큼 움직이고 slow, fast를 동시에 움직여 fast노드가 끝에 도달때까지 반복해 k만큼 떨어진 slow노드를 반환하는것은 이해했습니다. 그런데 fast 노드를 끝까지 보내는 While 문 조건에서 저는 fast.next가 없을 때 가 마지막 노드라고 생각했습니다. 하지만 선생님이 실제로 구현할 때는 이전에 구두로 설명하신것과는 약간 다르게 fast 가 마지막 노드가 아닌 거기서 한번 더 반복되어 fast == None 일때까지 움직여 k번째 노드를 찾으시더라고요. 왜 마지막노드에서 멈추는게 아니라 설명과 좀 다르게 2개의 포인터를 한칸씩 더 움직여 none이 될때까지 움직이도록 했는지 궁금해 강의 끝 시각화 해주신 부분을 봤습니다. 강의 끝에 시각화 하는 부분에서도 첫번째 풀이방법의 답은 10, 두번째 풀이방법의 답은 9가 나오면서 각 풀이방법의 정답이 달라진것 처럼 나오게 됐습니다. 구두로 설명하셨던 것보다 결국 index를 한칸 더 가서 fast가 None이 돼야 정답이 되던데 일반적으로 LinkedList 문제에서 마지막 노드까지 반복문을 돌린다고 가정하면 원래 currentNode가 None이 될때까지 돌려서 푸는건가요??? 이렇게 구체적으로 알려주시면, 더 정확하고 도움이 되는 답변을 드릴 수 있습니다! 😊
(AI 퀀트) 코드 한 줄 안 쓰고 주식 자동 분석 시스템 만들기 feat. Claude CLI
pdf파일에 있는 프롬프트를 복사항 붙여넣기 하면 아래와 같은 모습으로 붙여넣기가 되어 보기가 불편합니다. 해결방법 알려주시면 감사하겠습니다. ❯ get_top_gainers 함수를 코스피 , 코스닥 둘 다 호출해서 합친 뒤 등락ㄹ뉼 순으로 정렬하는 코드를 추가해줘 . 상위 15 개만
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Why Real Scenarios Matter for Cisco 500-443 Preparation If you are preparing for the Cisco 500-443 exam, you already know this is not a theory-heavy test. It checks how well you can think through real AppDynamics situations. Many candidates read documentation but still struggle when a question describes a slow application, a spike in response time, or an unexpected business transaction issue. The exam expects you to connect metrics, events, and configurations in a practical way. That is where scenario-based preparation becomes critical. In real exam questions, you are often placed in the role of an engineer who needs to identify the root cause of performance issues. You are not just recalling features. You are interpreting data from dashboards, understanding baselines, and deciding what action makes sense. Understanding Application Performance Baselines and Deviations One common scenario in the exam focuses on baselines. You might be shown a normal performance pattern and then asked to analyze a deviation. Many candidates miss the detail that AppDynamics uses dynamic baselines, not fixed thresholds. If you treat every spike as an issue without comparing it to historical behavior, you will likely choose the wrong answer. A typical question may describe increased response times during peak hours. The trap here is assuming it is always a problem. In reality, the system might already account for that pattern. You need to check if the deviation is outside the learned baseline range. This is where hands-on familiarity helps. You start to see how AppDynamics distinguishes normal variation from real anomalies. Business Transactions and Flow Maps in Troubleshooting Another area where candidates lose marks is business transaction analysis. The exam often presents a scenario where a transaction is slow, and you must trace it through multiple tiers. This is where flow maps come in. You need to understand how AppDynamics maps calls between services and how delays propagate. A slowdown in a database call can affect the entire transaction chain. Many questions test your ability to pinpoint where the delay starts, not just where it is observed. When practicing with Cisco 500-443 questions, especially realistic ones, you begin to see patterns. That is why many candidates look for buy cisco cceaar 500-443 questions by certprep.io to get exposure to these scenario-driven problems that mirror the exam style closely. Metrics, Snapshots and Root Cause Analysis The exam also places strong emphasis on metrics and transaction snapshots. You may be given a snapshot with detailed timing breakdowns and asked to identify the issue. This is not about memorizing metric names. It is about understanding what each metric represents in context. For example, if you see high CPU usage along with increased response time, you need to decide whether CPU is the cause or just a symptom. Similarly, slow database queries might appear as backend call delays. The key is to correlate metrics instead of looking at them in isolation. Candidates often rush through these questions. A better approach is to pause and mentally reconstruct the system behavior. Ask yourself what changed, where the delay started, and what data supports that conclusion. Health Rules, Alerts and Policy Actions Health rules and alerting policies are another core part of the exam. You might be asked how to configure alerts for specific conditions or how to reduce noise from false positives. Many candidates fail here because they focus only on default configurations. Real scenarios test whether you can fine-tune health rules based on application behavior. For example, setting a static threshold for all environments rarely works. You need to align rules with baselines and business impact. Policy actions also come into play. The exam may describe a situation where an alert should trigger a specific response. You need to know how AppDynamics connects health rule violations to automated actions. Preparing with the Right Practice Approach At this stage, the biggest gap for most candidates is not knowledge but application. You might understand each feature individually, but the exam combines them into layered scenarios. That is why targeted practice matters. Using cisco prep materials by certprep.io can help bridge this gap. Their approach focuses on realistic exam-style questions that reflect how AppDynamics problems appear in the actual test. Instead of random practice, you get structured exposure to full syllabus coverage, which helps reduce exam stress and builds confidence. If your goal is to pass quickly without second guessing your answers, this kind of focused preparation can make a clear difference.
