Executive Professional Certification Program in Generative AI & Machine Learning

SQL Fundamentals

• Introduction to Databases and SQL
• Fundamentals of Structured Query Language
• Tables in SQL
• Clauses in SQL
• Joins in SQL

Advanced concepts in SQL

• Functions in SQL
• Subqueries in SQL
• Rules and Views in SQL
• String Functions, Pattern Matching, and Nested Queries
• Math and Date-Time Functions

User-Defined Functions in SQL

• Different Types of User-Defined Functions
• Introduction to Inline Table Values and Multi-Statement Table
• Rank Function, Stored Procedures, and ROLLUP

Optimization and Performance Using SQL

• Searching and Sorting Operations in SQL
• Record Grouping and Clustered Indexes
• Common Table Expressions Using SQL

Introduction to Python

• History of Python
• Environment Setup and IDEs in Python
• Jupyter Notebook Support, etc

Basics of Python Programming

• Python Basics
• Data Types in Python
• Control Flow using Python
• Loops and Functions in Python
• Decorators and Lambda Functions

Object-Oriented Programming and Python

• Classes and Objects in Python
• Inheritance in Python
• Encapsulation in Python
• Polymorphism in Python
• Abstraction in Python

Data Handling with NumPy

• NumPy Arrays, CRUD Operations,
• Linear Algebra – Matrix Multiplication, CRUD Operations, Inverse, Transpose, Rank, Determinant of a Matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

• Loading the Data, Dataframes, Series, CRUD Operations, Splitting the Data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature Engineering, Feature Scaling, Normalization, Standardization, etc.
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-Variance Trade-Off, Cross Validation Techniques, Train-test Split, etc.

Data Visualization

• Bar Charts, Scatter Plots, Count Plots, Line Plots, Pie Charts, Donut Charts, etc. with Python Matplotlib
• Regression Plots, Categorical Plots, Area Plots, etc, with Python Seaborn

Statistics and Descriptive Analytics using MS Excel

• Measure of Central Tendency, Measure of Spread, Five-Point Summary, etc.
• Probability Distributions and Probability in Business Analytics
• Probability Distributions, Binomial Distribution, Poisson Distribution, Bayes Theorem, Central Limit Theorem, etc.

Python for Descriptive, Diagnostic, and Inferential Statistics

• Correlation, Covariance, Confidence Intervals, Hypothesis Testing, F-Test, Z-Test, T-Test, ANOVA, Chi-Square Test, etc.

Getting Started with Machine Learning

• Working of Supervised and Unsupervised Learning with Example
• The Concept of scikit-learn

Working with Regression

• Regression Problems
• Identifying a Regression Problem Using Dependent and Independent Variables
• Training the Model in a Regression Problem
• Assessing the Model for a Regression Problem
• Maximizing the Regression Model’s Efficiency

Working with Classification

• What are Classification Problems?
• Identification of a Classification Problem Using Dependent and Independent Variables
• Training the Model for a Classification Problem
• Assessing the Model for a Classification Problem
• Maximizing the Classification Model’s Efficiency

Working with Clustering

• Concepts of Clustering Problems
• Identification of a Clustering Problem Using Dependent and Independent Variables
• Training the Model for a Clustering Problem
• Assessing the Model for a Clustering Problem
• Maximizing the Clustering Model’s Effectiveness

Performing Text Mining, Cleaning, and Pre-processing

• What are Various Tokenizers?
• Define Tokenization
• The Concept of Frequency Distribution
• Stemming
• What is POS Tagging?
• Working of Lemmatization
• Articulate the Essentials of Bigrams
• The Concepts of Trigrams & Ngrams, Lemmatization, and Entity Recognition

Working with Text Classification, NLTK, and Sentiment Analysis

• Overview of Machine Learning Algorithms
• The Mechanisms of Words, Term Frequency, Count vectorizer, and Inverse Document Frequency
• Using the Text conversion, Confusion Matrix, and Naive Bayes Classifier

Grasping Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

• Working with Language Modeling
• Explain Sequence Tagging, Sequence Tasks, and Predicting Sequence of Tags
• Exploring Syntax Trees and Context-Free Grammars
• How to Use Chunking, Automatic Paraphrasing of Texts, and Chinking

AI Chatbots and Recommendations Engine

• Develop an AI Chatbot Assistant and a Recommendation Engine Based on NLP Principles

Fundamentals of Programming in Generative AI

• Performing Data Cleaning and Preprocessing with Numpy and Pandas
• Machine Learning Use Cases
• Fundamentals of Natural Language Processing
• How to Use Text Processing and Classification with examples
• What is Prompt Engineering?
• Use cases of Prompt Engineering

Transformers

• Overview of Transformers
• Architecture for Transformers
• How Transformers Operate
• Transformer Applications

BERT

• Overview of BERT
• BERT Architecture
• How BERT Operates
• BERT Application

GPT

• Introduction to GPTs
• GPT Architecture
• Working of GPTs
• Applications of GPT

LLM

• Introduction to LLMs
• LLM Architecture
• Working of LLMs
• Applications of LLM

Recommendation: Chatbot Powered by Generative AI

• Methods for Developing Workflow and Product Design
• Learn about Text Generation with Transformers, RLHF, and Pre-trained LLMs
• Working with the Fundamentals of LangChain
• How to Use Recommendation Engines
• Performing Evaluation Metrics for various models
• Using Prompt Engineering
• Using Gradio to Develop Generative AI Applications
• Creating and Distributing Recommended Using Gradio

RBM and DBNs & Variational AutoEncoder

• The Concepts of RBM and Autoencoders
• DeployingRBM for Deep Neural Networks
• Use RBM for Collaborative Filtering
• The Concepts of Autoencoders Features and Applications of Autoencoders

Object Detection Using Convolutional Neural Net

• How to Use Constructing a Convolutional Neural Network Using TensorFlow
• Describe the Convolutional, Dense, and Pooling Layers of CNNs
• Learn about Filtering Images Based on User Queries
• Using Deep Generative Models and Neural Style to Generate Images
• Describe Automated Conversation Bots
• Working with the Generative Model and the Sequence-to-Sequence Model (lstm)