AI LAB

Introduce the basics of Machine Learning, including supervised and unsupervised learning, regression, and classification

Teach the basics of Python programming for data analysis and Machine Learning, including data types, loops, and functions

Teach students about data preprocessing techniques, including data cleaning, data normalization, and feature scaling

Introduce Python libraries for data preprocessing, such as Pandas and NumP

Teach the concept of linear regression and polynomial regression

Teach students how to implement regression algorithms in Python using Scikit-Learn library

Discuss common issues like overfitting and underfitting

Teach the concept of binary classification and multiclass classification

Teach students how to implement classification algorithms in Python using Scikit-Learn library

Discuss common issues like class imbalance, decision boundary, and evaluation metrics

Teach the concept of clustering and its types, including K-Means, Hierarchical clustering, and DBSCAN

Teach students how to implement clustering algorithms in Python using Scikit-Learn library

Discuss common issues like choosing the number of clusters and cluster interpretation

Concept of dimensionality reduction and its techniques, including PCA, t-SNE, and LLE

How to implement dimensionality reduction algorithms in Python using Scikit-Learn library

Discuss common issues like choosing the number of dimensions and its impact on the performance

Teach the concept of ensemble learning and its types, including bagging, boosting, and stacking

Teach students how to implement ensemble learning algorithms in Python using Scikit-Learn library

Discuss common issues like ensemble selection and boosting strategies

Teach the concept of neural networks and its types, including MLP, CNN, and RNN

Teach students how to implement neural network algorithms in Python using TensorFlow library

Discuss common issues like hyperparameter tuning, optimization techniques, and regularization methods