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Machine Learning Engineers and Data Scientists analyze data and build models to uncover insights and enable automation. These roles might be titled as Applied Scientist, Data Engineer, or Predictive Analytics Specialist depending on the organization.

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ML Conceptual Topics

(AnalytixLabs)

  1. Supervised Learning Models (Linear Regression, Logistic Regression, KNN,
  2. Unsupervised Learning
  3. Overfitting and Regularization
  4. Model Evaluation Metrics
  5. Hyperparameter Tuning

ML Algorithms

(Geeksforgeeks)

  1. Regression (Linear, Logistic)
  2. Classification (Decision Trees, SVM, KNN)
  3. Clustering (K-Means, DBSCAN)
  4. Ensemble Methods (Random Forest, XGBoost)
  5. Neural Networks and Deep Learning
  6. Optimization Techniques

Coding Questions

(SimpliLearn)

  1. Implement Linear Regression from Scratch
  2. Write a K-Means Clustering Algorithm