80% of Machine Learning Candidates Get Rejectedโฆ Because They Canโt Answer These Questions!
From โWhatโs a Model?โ to โLetโs Deploy It!โ
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๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ ๐๐ฒ๐๐ฒ๐น
1. What is Machine Learning?
2. Types of ML (Supervised, Unsupervised, Reinforcement)
3. Key Terminologies (Features, Labels, Training, Testing)
4. Data Preprocessing (Handling Nulls, Encoding, Scaling)
5. Train-Test Split & Cross-Validation
6. Evaluation Metrics (Accuracy, Precision, Recall, F1)
7. Overfitting vs Underfitting
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8. Linear & Logistic Regression
9. Decision Trees & Random Forests
10. K-Nearest Neighbors (KNN)
11. Support Vector Machines (SVM)
12. Naive Bayes Algorithm
13. Clustering (K-Means, Hierarchical)
14. Dimensionality Reduction (PCA, t-SNE)
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15. Ensemble Techniques (Bagging, Boosting, XGBoost)
16. Deep Learning (ANN, CNN, RNN Basics)
17. NLP Basics (Text Preprocessing, TF-IDF, Word Embeddings)
18. Time Series Forecasting (ARIMA, LSTM)
19. Model Deployment (Flask, Streamlit, FastAPI)
20. Hyperparameter Tuning (GridSearch, RandomizedSearch)
21. Real-world ML Projects to Build
22. Top Datasets for ML Practice
23. Common Mistakes & Best Practices
24. Free Tools: Google Colab, Scikit-learn, TensorFlow, PyTorch