- Courses
- LPU
- MCA Semester 1
- CAB107
Introduction to AI and Data Science
6 units · 26 topics · 260 questions · 26 topic notes
Unit 1 Foundations of AI and data science
Unit 2 Python for AI and data science
Unit 3 Data collection, preprocessing and visualization
- Data collection methods, structured and unstructured data10 questions · MTE
- Handling missing values and outliers, data cleaning10 questions · MTE
- Feature engineering, data transformation and normalization10 questions · MTE
- Descriptive statistics, correlation and covariance10 questions · MTE
- Visualization with Matplotlib, Seaborn and interactive dashboards with Plotly10 questions · MTE
Unit 4 Machine learning fundamentals
- Supervised, unsupervised and reinforcement learning10 questions · ETE
- Linear and polynomial regression10 questions · ETE
- Logistic regression, k-NN, decision trees, random forests and SVM10 questions · ETE
- Accuracy, precision, recall, F1-score and ROC-AUC10 questions · ETE
- MAE, MSE, RMSE, R2 score and cross-validation10 questions · ETE
Unit 5 Artificial intelligence techniques
- Knowledge representation and intelligent agents10 questions · ETE
- Search algorithms and heuristic search10 questions · ETE
- Rule-based expert systems10 questions · ETE
- K-Means, hierarchical clustering, DBSCAN and PCA10 questions · ETE
- Recommendation systems and reinforcement learning concepts10 questions · ETE