Master the Machine Learning Interview

5 months ago

Paid

English (India)

54 students

Are you preparing for a career in Machine Learning or aiming to crack job interviews in AI-related roles? This course is designed to help you master the concepts and tackle the most frequently asked interview questions in Machine Learning. With a focus on making learners job-ready, the course is structured to build a solid foundation and provide clarity on key ML topics, ensuring you walk into interviews with confidence.

Topics Covered:

Machine Learning Basics:
- Introduction to supervised, unsupervised, and reinforcement learning.
- Key differences between regression and classification problems.
- Commonly used algorithms like Linear Regression, Decision Trees, and SVM.
Data Preprocessing and Feature Engineering:
- Data cleaning, normalization, and standardization.
- Feature selection techniques and dimensionality reduction (PCA, LDA).
- Handling imbalanced datasets.
Model Evaluation and Optimization:
- Understanding bias-variance tradeoff and overfitting.
- Cross-validation techniques (k-fold, leave-one-out).
- Evaluation metrics like precision, recall, F1 score, and ROC-AUC.
Ensemble Methods and Advanced Algorithms:
- Bagging, boosting, and stacking techniques.
- Algorithms like Random Forest, Gradient Boosting, and XGBoost.
- Understanding clustering algorithms (K-Means, DBSCAN) and recommendation systems.
Neural Networks and Deep Learning Fundamentals:
- Basics of artificial neural networks (ANNs).
- Activation functions and optimization algorithms (SGD, Adam).
- Introduction to CNNs, RNNs, and transfer learning.
Real-World Machine Learning Scenarios:
- Case-based questions on handling large datasets and model deployment.
- Discussing practical challenges like missing data and feature importance.
Behavioral and Situational Interview Preparation:
- Insights into how to answer "real-world problem" questions.
- Tips on explaining projects and demonstrating problem-solving skills.