Artificial Intelligence and Machine Learning Course

This course equips beginners with Python basics, laying the foundation for AI and ML. It also covers Generative AI concepts, including ChatGPT and other AI tools, with practical demonstrations of their business applications.

This course is a training module for learners with limited or no programming exposure. It enables them to be at par with learners with prior programming knowledge before the program commences.

• Overview of Python

• Fundamentals of Python Programming

• Overview of Numpy and Pandas

• Overview of Visualization in Python

• Overview of Data Preprocessing in Python

This course provides you with a fundamental understanding of the basics of Python programming and builds a strong foundation of the basics of coding to build AI and Machine Learning (ML) applications 

• Introduction to Python Programming

• AI Application Case Study

This course provides you with an overview of Generative AI, what ChatGPT is and how it works, business applications of ChatGPT, and an overview of other generative AI models/tools via demonstrations

• ChatGPT and Generative AI - Overview

• ChatGPT - Applications and Business

• Generative AI Demonstrations

This course guides you to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and business recommendations by performing exploratory data analysis using some of the most widely used Python packages.

 Python Programming

• Variables and Data Types

• Data Structures 

• Conditional and Looping Statements

• Functions 

Python for Data Science

• NumPy Arrays and Functions

• Creating, Accessing and modifying NumPy arrays

• Saving and loading NumPy arrays

• Creating, Accessing and modifying Pandas Series

• Pandas DataFrames

• Creating ,Accessing and modifying, Combining DataFrames

• Pandas Functions

• Various data manipulation functions in Pandas

• Saving and Loading Datasets using Pandas

• Saving DataFrames to CSV, Excel, etc.

• Loading datasets into DataFrames

Exploratory Data Analysis (EDA)

• Data Overview

• General statistics of the dataset (e.g., describe(), info())

•  Univariate analysis (Histogram, Boxplots, and Bar graphs)

• Bivariate/Multivariate Analysis((Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)

• Customizing of Plots

• Missing value treatment,

• Outlier detection and treatment

Analyzing Text Data

• Text Processing

• Stopword removal

• Stemming

• Removing special characters and whitespace

• Text Vectorization(Bag of Words,n-grams)

This course helps you build an understanding of the concept of learning from data, build linear and non-linear models to capture the relationships between attributes and a known outcome, and discover patterns in and segment data with no labels.

Linear Regression

• Introduction to learning from data

• Types of machine learning

• Business Problem and Solution Space 

• Regression, Correlation and Linear Relationships

• Simple and Multiple Linear Regression

• Categorical Variables in Linear Regression

•  Regression Metrics

Decision Trees

•  Business Problem and Solution Space - Classification

• Introduction to Decision Trees

•  Impurity Measures and Splitting Criteria

• Classification Metrics

•  Pruning

• Decision Trees for Regression

K-means Clustering

• Business Problem and Solution Space - Clustering

•  Distance Metrics

•  Introduction to Clustering

•  Types of Clustering,

• K-means Clustering

•  t-SNE for visualizing high-dimensional data

This course helps you explore how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs.

Bagging

• Introduction to Ensemble Techniques

•  Introduction to Bagging

•  Sampling with Replacement,

• Introduction to Random Forest

Boosting

• Introduction to Boosting

•  Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)

• Stacking

Model Tuning

• Feature Engineering

• Cross-validation

•  Oversampling and Undersampling,

• Model Tuning and Performance

•  Hyperparameter Tuning

• Grid Search

•  Random Search

• Regularization

This course helps you implement neural networks to synthesize knowledge from data, demonstrate an understanding of different optimization algorithms and regularization techniques, and evaluate the factors that contribute to improving performance to build generalized and robust neural network models to solve business problems

Introduction to Neural Networks

• Deep Learning and history

• Multi-layer perceptron

• Types of Activation functions,

• Training a neural network

• Backpropagation

Optimizing Neural Networks

• Optimizers and their types

•  Weight Initialization and its techniques

•  Regularizations and its techniques,

• Types of neural networks

This course will help you get introduced to the world of natural language processing, gain a practical understanding of text embedding methods, gain a practical understanding of the working of different transformer architectures that lie at the core of large language models (LLMs), explore how retrieval augmented generation (RAG) integrates information retrieval to improve the accuracy and relevance of responses from an LLM, and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.

Word Embeddings

• Introduction to NLP

• History of NLP

•  Sentiment Analysis

•  Introduction to Word Embeddings

•  Word2Ve

• , GloVe

• Semantic Search

Attention Mechanism and Transformers

• Introduction to Transformers

• Components of a Transformer

•  Different Transformer Architectures

• Applications of Transformers

 Large Language Models and Prompt Engineering

• Introduction to LLMs

•  Working of LLMs

•  Applications of LLMs

•  Introduction to Prompt Engineering

•  Strategies for Devising Prompts

Retrieval Augmented Generation

• Introduction to Embeddings and Tokenization. 

• Byte-pair encoding (BPE) Tokenization

• Computation and Application of Sentence Embeddings

•  Retrieval-Augmented Generation (RAG)

This course will introduce you to the world of computer vision, demonstrate an understanding of image processing and different methods to extract informative features from images, build convolutional neural networks (CNNs) to unearth hidden patterns in image data, and leverage common CNN architectures to solve image classification problems.

Image Processing

• Overview of Computer Vision

• Color pixel and image representation

•  Edge Detection

• Kernels

•  Padding, Strides and Pooling

• Flattening to a 1D Array

Convolutional Neural Networks

• ANN Vs CNN

• CNN Architecture

• Introduction to Transfer Learning

•  Common CNN Architectures

This course will help you comprehend the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.

Introduction to Model Deployment

• Introduction to Model Deployment,

• Serialization

•  Deployment using Streamlit

Containerization

• Introduction to Containerization

•  Docker

•  Deployment using Flask

• Introduction to Data Science
• Data Visualization and Predictive Modeling
• Mathematics of Artificial Intelligence and Machine Learning
• Data Storage Technologies
• Capstone Project  

• Examine the data science lifecycle from data collection, storage, query and result presentation.
• Investigate the various types of data sources and repositories.
• Evaluate various statistical analysis techniques that can be used to analyze data sets.
• Build a variety of Machine Learning models to make data-informed predictions.
• Apply skills and knowledge in preparing data for analysis and conducting data queries.
• Assess the use of visualization and other presentation technologies.

DATA VISUALIZATION AND PREDICTIVE MODELING:

The goal of this course is to expose students to visual representation methods and techniques that enhance the understanding of complex data. Students will learn how to take raw data, extract meaningful information, use statistical tools and make visualizations to improve comprehension, communication and decision-making.

Upon successful completion of this course, you will be able to:

• Evaluate the key techniques and theory used in data visualization.
• Apply appropriate data visualization methods to different types of data.
• Explain how data visualization fits into the predictive analytic process.
• Build and evaluate visualization systems.

CAPSTONE PROJECT:

Qualitative And Exploratory Research Methods (712)

This course explores non-statistical forecasting and other qualitative research methods. Qualitative research methodologies have become more prevalent in research as a viable and valid form of inquiry, especially as they pertain to human behavior in organizations. Qualitative research techniques examined include ethno methodology; grounded theory; and phenomenological research. Nonparametric statistical analysis will also be examined. By the conclusion of this class, you will gain a solid foundation regarding the qualitative research approach and its various traditions along with their theoretical and applied constructs. This will allow you to prepare a qualitative problem for research as well as structure a valid qualitative research design for conducting the actual research itself (i.e., your doctoral dissertation or future research problems in your area of interest or specialization).

Quantitative Research Methods I (713)

Data Management And Non-Experimental: This course is a combination of quantitative research methods, multivariate statistics, and forecasting. The course assumes the doctoral student has had a graduate level statistics/quantitative methods course covering parametric statistics and hypothesis testing.

• DOCTORAL RESIDENCY I This course is the first of three residencies. The residencies occur simultaneously with coursework throughout the student's doctoral journey. The intent of a residency experience is to provide students with a chance to connect directly with faculty/mentor and fellow students within the doctoral program. Students will attend information sessions, meet with faculty/mentor regarding subject matter and research methodology experts, and present their problem/purpose statement to a review board for feedback and direction.Outcome: Finalisation of project problem and purpose statements.

• Doctoral Residency II: Transition from coursework to dissertation research. Develop your dissertation proposal, gain ethical research approval, and begin collecting data for your study.
   

• Doctoral Residency III: Advance your dissertation research and writing. Analyze your data, draft your dissertation chapters, and receive ongoing feedback from your dissertation committee.

This dissertation program provides the structure and resources to complete your doctoral research and successfully defend your thesis.
 

• Dissertation I - Chapter 1: Develop a strong foundation for your dissertation. Learn to write a compelling introduction, craft a clear research question, and define your methodology.
   
• Dissertation II - Chapter 2: Understand the literature review. Explore relevant research, identify theoretical frameworks, and demonstrate your understanding of the existing scholarship.
   
• Dissertation III - Chapter 3: Focus on your research methodology. Refine your data collection plan, discuss data analysis techniques, and ensure ethical research practices.
   
• Dissertation IV- Chapter 4: Analyze your research data. Learn to interpret findings, draw conclusions, and identify potential limitations.
   
• Dissertation V - Chapter 5: Write your discussion and conclusion. Integrate your findings with existing literature, discuss the study's significance, and outline future research directions.