What you'll learn

Introduction to the field of Data Science

How to build a Data Scientist's mindset

Data Science approach

Supervised Models: Regression

Supervised Learning Models: Classification

Various types of classification models for binary and multi-class classification

Performance metrics: Confusion Matrix, F1, Precision, Recall, Accuracy - which one to use in which case, with examples

Unsupervised Learning: Clustering, performance metrics

Model Improvement Methods

What does model improvement mean? What are the different options at our disposal and when to use them?

Feature Selection and Dimensionality reduction methods: Lasso Regression, Ridge Regression, PCA

Hyperparameter Tuning: Various methods, deep dive into Grid-search and Random-search

Cross-Validation

Application to hyperparameter tuning

Introduction NLP with various common NLP tasks

How is Natural Language data different from Panel or Tabular Data?

Steps for processing natural language inputs: NLP pipeline (Tokenisation, Stopword Removal, Stemming, Lemmatisation)

Various methods of word embedding: Bag of Words, Word2vec, Glove, and using Large Language Models

Brief introduction to Recurrent Neural Networks and Long Short Term Memory models, the building blocks for all LLMs

Pre-trained models and transfer learning

Requirements

No previous knowledge required! Suitable for candidates from any domain.