Test your skills in Feature Engineering, ML Algorithms (XGBoost/Random Forest), Metrics (ROC/AUC), and Deep Learning.
Sub Category
- Other IT & Software
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Objectives
- Evaluate your Data Preprocessing skills, handling Missing Data, Outliers, One-Hot Encoding, and preventing Target Leakage.
- Test your Algorithm knowledge, knowing exactly when to use Logistic Regression, K-Means Clustering, SVMs, or XGBoost.
- Assess your Model Evaluation proficiency, mastering the Confusion Matrix (Precision/Recall), ROC/AUC curves, and K-Fold Cross-Validation.
- Validate your Deep Learning & NLP skills, understanding Convolutional Neural Networks (CNNs), Word Embeddings (Word2Vec), and Transfer Learning.
Pre Requisites
- A foundational understanding of general statistics and data analysis. Basic familiarity with machine learning terminology (e.g., Training Data, Algorithms, Predictions). A desire to pass rigorous, technical whiteboard interviews for Data Science or ML Engineering roles.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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