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- Develop a comprehensive understanding of data science and ML concepts and identify the best models to fit various business situations.
- Describe the impact of Data Science and ML on business processes
- Data Cleaning: Handle missing values, duplicates, outliers
- EDA (Exploratory Data Analysis): Understand patterns using stats & visualization
- Feature Engineering: Transform raw data into useful inputs
- Modeling: Train ML models
- Deployment: Serve model via API / pipeline
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3. Common Algorithms
📊 Supervised Learning
- Linear Regression → Predict continuous values
- Logistic Regression → Binary classification
- Decision Tree → Rule-based splits
- Random Forest → Ensemble of trees
- Gradient Boosting (XGBoost, LightGBM) → Boosting models
- SVM (Support Vector Machine) → Margin-based classifier
- KNN (K-Nearest Neighbors) → Distance-based
📊 Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- PCA (Dimensionality Reduction)
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Bias-Variance Tradeoff
Concept Meaning Bias Underfitting (too simple model) Variance Overfitting (too complex model)
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Types of Machine Learning
Type Description Examples Supervised Learning Labeled
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Regression, Classification Unsupervised Learning No labels Clustering, PCA Semi-supervised Few labels Image classification Reinforcement Learning Reward-based Game AI, robotics
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