Machine Learning Bootcamp

>> Session 1: Regression Algorithms

Topics

• Introduction to Regression
• Linear Regression
• Multiple Regression
• Model evaluation metrics

Hands-On: Build and evaluate a regression model

>> Session 2: Classification Algorithms

Topics

• Classification concepts
• Decision Trees
• Random Forest
• Support Vector Machine (SVM)

Hands-On: Develop a classification model for prediction

>> Session 1: Clustering Techniques

Topics

• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• Cluster evaluation

Hands-On: Perform customer segmentation using clustering

>> Session 2: Dimensionality Reduction

Topics

• Curse of dimensionality
• Principal Component Analysis (PCA)
• Feature extraction
• Visualising high-dimensional data

Hands-On: Apply PCA for dimensionality reduction

>> Session 1: Model Optimisation

Topics

• Overfitting and underfitting
• Cross-validation
• Hyperparameter tuning
• Bias and variance

Hands-On: Optimise Machine Learning models

>> Session 2: Ensemble Learning

Topics

• Ensemble learning concepts
• Bagging
• Boosting
• Gradient Boosting and XGBoost concepts

Hands-On: Improve prediction accuracy using ensemble methods

>> Session 1: Introduction to Deep Learning

Topics

• Artificial Neural Networks
• Perceptrons
• Activation functions
• Forward and backward propagation

Hands-On: Design a basic neural network architecture

>> Session 2: Deep Learning Applications

Topics

• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning
• AI applications using Deep Learning

Hands-On: Build a simple Deep Learning solution

>> Session 1: Model Deployment & MLOps

Topics

• Machine Learning deployment concepts
• Model serving
• Monitoring model performance
• Introduction to MLOps

Hands-On: Deploy a trained Machine Learning model

>> Session 2: Explainable AI & Responsible Machine Learning

Topics

• Explainable AI (XAI)
• Model interpretability
• AI ethics
• Bias and fairness in Machine Learning

Hands-On: Evaluate and explain Machine Learning model predictions