Data Mining is intimately intertwined with Statistics. Knowledge of basic statistics is essential for a successful analyst. Many ‘Small data’ techniques such as correlation, testing of hypothesis, data-transformation and others need to be learnt to fully understand data. Concepts of inferential statistics are used in comparing machine learning models. Descriptive statistics is invariably used in data pre-processing. In this Course we refresh as also learn statistical fundamentals and essential inferential statistics

• Measures of Central Tendency and Dispersion
• Probability Theory (Different Approaches, Rules of Probability, Bayes’ Theorem)
• Random Variables and Probability Distributions Discrete Probability Distributions
• Continuous Probability Distributions – Normal Distribution
• Correlation and Regression Analysis: Simple & Multiple Regression
• Concept Of Hypotheses Testing, Type I & Type II Errors, Power Of The Test, Hypothesis Testing of Mean and Proportion, Two Sample Tests, Tests for Difference in Means and Proportions.
• Chi-Square Goodness-of-Fit Test, Test of Independence

We practice those modelling techniques that consistently garner high performance, are relatively fast and are well known in ML community. Thus, these will be of immense use in many predictive applications. These techniques do not perform that well with image or video data.

1. Introduction to Machine Learning Technology
2. Data visualization and discovering structure in data. (Techniques include t-sne, parallel coordinates, and mosaic plots) and Feature Importance
3. Unsupervised learning techniques
   • K-means clustering
   • Hierarchical clustering
   • Expectation-Maximization algorithm
   • T-SNE & UMAP manifold learning technique
   • Dimensionality reduction
   • Principal Component Analysis (PCA)
4. Supervised learning techniques for Classification and Regression
   • Decision trees
   • Ensemble modeling using Random Forest
   • Gradient Boosting Techniques
     • Gradient Boosting Learner
     • XGBoost
     • LightGBM
   • Performance measures: Accuracy, Precision and Recall, F-measure; Area Under the Curve, Cohen’s Kappa, Sensitivity, Specificity
   • Hyper-parameter optimisation techniques—Bayes Optimization
   • Interpreting Machine Learning Models

• Introduction to Neural Networks (NN)
• Experiments with MLP networks
• Regularising NN
  • Dropouts
  • Batch-normalization
  • l1 and l2 regularization
  • Starting weight initialization
• Deep Learning with Convolution Neural Networks
  • Data Augmentation
• Using very Deep Convolution Networks
  • Transfer learning with VGG16
  • Transfer learning with ResNet50
  • Transfer learning with InceptionV3
• Recurrent Neural Networks
  • LSTM, GRUs, and Bi-directional LSTM

• General Architecture of Transformers
• Zero-shot classification and few-shot learning
• Ollama and anything LLM installation
• Embedding, vector databases and search
• Prompt Engineering
• Developing knowledge products in healthcare using web UIs for LLM