AI & ML VIRTUAL LAB
BASIC ELECTRICAL ENGINEERING DATA ANALYTICS DATA STRUCTURE AND ALGORITHM DATA ANALYSIS AND ALGORITHM AI & ML BLOCKCHAIN CRYPTOGRAPHY

1. Introduction to NumPy and pandas

Aim: Learn the basics of NumPy arrays and pandas DataFrames for data manipulation.

2. Exploratory Data Analysis (EDA)

Aim: Analyze and visualize datasets to understand data distribution and patterns.

3. Data Preprocessing Techniques

Aim: Apply techniques like normalization, encoding, and handling missing values.

4. Linear Regression

Aim: Implement and understand simple and multiple linear regression models.

5. Logistic Regression

Aim: Learn binary classification using logistic regression.

6. Decision Tree vs Random Forest

Aim: Compare decision tree and random forest models.

7. Support Vector Machine (SVM)

Aim: Classify data using SVM and understand hyperplanes.

8. K-Means Clustering

Aim: Apply unsupervised learning to cluster data using k-means.

9. PCA

Aim: Reduce dimensionality of datasets using Principal Component Analysis.

10. CNN for Image Classification

Aim: Implement convolutional neural networks for image classification tasks.

11. Evaluation Metrics

Aim: Understand model evaluation using accuracy, precision, recall, and F1-score.

12. Hyperparameter Tuning (GridSearch)

Aim: Tune model parameters using grid search techniques.





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