DS & ML Portal

Machine Learning Fundamentals

• ML_Tools
• Supervised Learning
• Unsupervised Learning
• Reinforcement learning
• Deep Learning

Model Training and Optimisation

• Learning rate
• Overfitting
• Regularisation
• Hyperparameter
• Hyperparameter Tuning
• Model Optimisation
• Model Selection
• Vanishing and exploding gradients problem

Feature Engineering and Data Handling

• Feature Selection
• Feature Engineering
• Imbalanced Datasets
• Outliers
• Anomaly Detection
• Multicollinearity
• Dimensionality Reduction
• Clustering

Machine Learning Models

Classification Models

• Classification
• Binary classification
• Support Vector Machines
• Decision Tree
• Random Forests
• K-nearest neighbours
• Logistic Regression

Regression Models

• Regression
• Linear Regression

Boosting and Optimisation

• Gradient Descent
• Gradient Boosting
• XGBoost

Deep Learning and Neural Networks

• BERT
• LSTM
• Recurrent Neural Networks
• Transformer
• Attention mechanism
• Neural network

Model Evaluation and Metrics

• Cost Function
• Loss function
• Cross Entropy
• Evaluation Metrics
• Model Evaluation
• Accuracy
• Precision
• Recall

Algorithms and Frameworks

• Machine Learning Algorithms
• Optimisation techniques
• Optimisation function
• Model Ensemble
• Batch Processing
• Apache Spark
• Sklearn

Statistical and Data Analysis Concepts

• Distributions
• Statistics
• Correlation
• Data Analysis
• Data Quality
• Principal Component Analysis

Misc

• Interpretability
• RAG