The topics to be covered include: Introduction to ML, Supervised, Unsupervised & Semi-Supervised Learning, talking about data in ML, Similarity Measures, Train Test Split & Cross Validation, Bias-Variance Trade-off, Overfitting and Underfitting, Evaluation Measures, Linear regression, Naïve Bayes, KNN, Decision Tree, Support Vector Machine, K-Means Clustering, Perceptron, Dimensionality Reduction, Applying ML Algorithms on real data, Ensemble Learning.
- Teacher: Usman Baba