Introduction: This foundational module equips students with essential technological skills required for AI and Machine Learning (AIML). Students will learn to harness the power of various tools to manage, analyze, and visualize data effectively.

Key Topics:

• Power BI: Data visualization and business intelligence
• Python: Programming for AI and ML
• SQL: Database management and querying

Excel: Data analysis and manipulation

Introduction: This module covers fundamental statistical concepts and decision science techniques critical for data analysis and interpretation. Students will gain a strong understanding of probability distributions, hypothesis testing, and variance analysis.

Key Topics:

• Measures of Central Tendency
• Probability Distribution
• Hypothesis Testing (z-test, t-test, F-test)
• Chi-square test
• Type of Error
• Analysis of Variance (ANOVA)

Sample Project: A well-respected FMCG company wants to determine the effect of detergents on dirt removal. The company has been branding its novel technology but has been challenged by the washing machine manufacturers about the efficacy of the detergent. As the lead data scientist, can you provide your insights on this data?

Introduction: This module introduces students to unsupervised machine learning techniques used to uncover hidden patterns and structures in data. Students will explore clustering methods and dimensionality reduction techniques.

Key Topics:

• Clustering (Hierarchical and non-Hierarchical)
• Principal Component Analysis
• Factor Analysis

Sample Project: A financial services firm has data on customer spending across various items. It aims to identify customer groups to market products better, identify potential defaulters, and generate higher ROI. As a data scientist, can you help this firm achieve its objectives?

Introduction: Students will delve into supervised machine learning techniques, learning to build models that predict outcomes based on labeled data. This module covers a variety of algorithms and their practical applications.

Key Topics:

• Naïve Bayes rule
• K-Nearest Neighbour (KNN)
• Simple Linear Regression
• Multiple Linear Regression
• Logistics Regression
• Classification and Regression Trees
• Support Vector Machines
• Artificial Neural Network

Sample Project: An FMCG company has created clusters of customers and is now interested in predicting customer purchase behavior. Given a large dataset with rich information but also missing values and incorrect data, conduct exploratory data analytics followed by the application of supervised machine learning to predict customer actions.

Introduction: This module focuses on reinforcement learning, where students will learn to develop algorithms that make decisions by optimizing rewards through trial and error. Different approaches to reinforcement learning will be explored.

Key Topics:

• Value Based
• Policy Based
• Model Based

Sample Project: Using the data provided by the FMCG company, apply reinforcement learning to improve the prediction of customer behavior.

Introduction: This module dives into deep learning techniques, exploring advanced neural network architectures and their applications in various fields. Students will gain practical experience in building and training deep learning models.

Key Topics:

• Deep Learning
• Convolution Neural Network
• Recurrent Neural Network
• Autoencoder
• Generative Adversarial Network

Sample Project: Choose a project that suits your current needs in conjunction with faculty guidance. Possible applications include prediction, image recognition, and fraud detection.

Introduction: This module covers natural language processing (NLP) and the emerging field of generative AI, enabling students to develop applications that understand, process, and generate human language. Students will learn various NLP techniques, including those used in large language models (LLMs) and generative AI.

Key Topics:

• Data Processing
• Cosine rules, TF-IDF, etc.
• Sentiment Analysis
• Applying Machine Learning to Text Data
• Introduction to Large Language Models (LLMs)
• Generative AI and Applications

Sample Project: Analyze and process text data to develop models for tasks such as sentiment analysis, text classification, and more. Additionally, create a generative AI application, such as a chatbot or content generator, leveraging the techniques learned in this module.