Interview: Machine Learning Foundations — Full course
Course content
14 topics • 0 videos • 0m
The ML Map: Types, Workflow, Data Splits
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Overfitting, Bias-Variance, Regularization
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Linear and Logistic Regression
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Evaluation Metrics: Choosing the Right One
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Decision Trees, Random Forest, Gradient Boosting
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KNN, SVM and Naive Bayes
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Imbalanced Data and Rare Events
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Clustering: K-Means, DBSCAN, Hierarchical
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Anomaly Detection and PCA
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Feature Engineering and Data Preparation
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Time Series Forecasting
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Deep Learning for Vision and Arabic Text
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Deploying and Monitoring Models (MLOps)
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Statistics You Must Know
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