Professional Program in Applied AI & Data Science

Data Handling with NumPy:

• NumPy Arrays, CRUD Operations, etc.
• 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.

SQL Basics:

Fundamentals of RDBMS & SQL, ACID properties, Normalization, understanding of SQL Tables, Joins, and Variables

Advanced SQL:

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching, Mathematical functions, Date-time functions, etc.
• Deep Dive into User Defined Functions, Types of UDFs, Inline table value, multi-statement table.
• Stored Procedures, Rank Function, SQL ROLLUP, etc.
• SQL Optimization and Performance
• Record grouping, searching, sorting, etc.
• Indexing – Clustered Indexes, Common Table Expressions

• American Sign Language Recognition

Train custom models using pre-trained ones to identify American Sign Language gestures.

• Gesture Recognition

Use pre-trained models for real-time gesture recognition with deep learning and Computer vision techniques.

• Shark Species Classification

Use deep learning library like TensorFlow for image classification to identify the shark species.

• Stock Market Forecasting

Forecast the stock market trends and prices with historical data using deep learning techniques like LSTM.

• Cyber Threat Detection Using NLP

Applying Natural Language Processing and deep learning techniques in detecting cybersecurity threats.

• Introduction to AI & Data Science, CNN & RNN
• Introduction to Keras API, Tensorflow and Neural Networks
• Single Cell (perceptron)
• Multi cell perceptron Topology, gradient-descent, Back-propagation
• Weights & Biases
• Build a Neural Network from scratch
• Use cases of Deep Learning in the industry
• Difference between Data Science, Machine Learning, Deep Learning & AI & Data Science
• Lifecycle of Deep Learning Project

• NLP: Learn how to make computers understand, interpret, and generate human language.
• Text Pre-processing: Tokenization, stemming, lemmatization, N-grams, and entity recognition.
• Text Classification: Sentiment analysis, Naive Bayes, Count Vectorizer, and TF-IDF.
• Language Modeling: Sequence tagging, syntax trees, and automatic paraphrasing.
• AI Applications: Building chatbots and recommendation engines using NLP.
• Computer Vision: Constructing CNNs with TensorFlow, object detection, and image filtering.
• Deep Learning: Generating images with neural style, deep generative models.

• LSTM: What is LSTM? How does LSTM work, Applications of LSTM, etc.
• Transformers: What are transformers, how does a transformer work in deep learning, applications of transformers, types of transformers, encoder-decoded, self-attention, etc.
• BERT: Language Models, what is BERT, how does BERT work, how is BERT different from LSTM, applications of BERT, etc.
• GPT: What are generative pre-trained models (GPT), how does a GPT work? Real-life examples of GPT, etc.
• LLM: NLP and Language models, what are LLMs, how does an LLM work, applications of LLM, etc.
• VAEs: Introduction to Variational Autoencoders, Architecture of VAEs, creating VAEs for image generation.
• LangChain: Intuition for LangChain, LangChain applications, LangChain architecture, how to work with LangChains, etc.

• Prompt Engineering: Science behind Prompts, Prompt Engineering Basics, Impact and usage of Prompt Engineering, Prompt Engineering tools, Effectiveness of Prompts, etc.
• Image-Based Applications of Generative AI: Image-based workflows and artifacts using Generative AI with image generation, text-image generation, etc.
• Text-Based Applications of Generative AI: Leveraging Text summarization and text generation for text-based applications using Generative AI tools.
• Audio-Based Applications of Generative AI: Text to Audio generation, Audio processing, Audio generation leveraging Generative AI tools to create end-to-end applications.

• Understand the meaning of Agentic AI, and why it matters for ML workflows
• Know the role of reasoning, memory, and tool use in building AI agents
• Understand Reasoning + Action (ReAct) + Toolformer + CoT prompting
• Learn to build Agno or LangChain Agents with Pandas for data preprocessing and Scikit-learn for model selection and training
• Hands-on:
  • Build a basic agent to load a dataset, clean it, and perform EDA
  • Output: Summary report with missing values, distributions, and outliers

• Learn to add decision logic for model suggestion & hyperparameter selection
• Decide how to use memory and intermediate outputs for model building
• Get to know how to autonomously evaluate multiple models and summarise results.
• Get introduced to API-based tools via MCP (Model Context Protocol)
• Hands-on: Learn to build agents for:
  • Comparing classification algorithms
  • Scoring models on cross-validation
  • Returning a recommendation with justification

Introduction to MLOps

• MLOps lifecycle
• MLOps pipeline
• MLOps Components, Processes, etc

Deploying Machine Learning Models

• Introduction to Azure Machine Learning
• Deploying Machine Learning Models using Azure

Power BI Basics

• Introduction to Power BI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, Data Extraction with Power BI.
• SaaS Connectors, working with Azure SQL database, Python, and R with Power BI
• Power Query Editor, Advanced Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.

DAX

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics 

• Slicers, filters, Drill Down Reports
• Power BI Query, Q & A, and Data Insights
• Power BI Settings, Administration, and Direct Connectivity
• Embedded Power BI API and Power BI Mobile
• Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

• Introduction to AWS: Mastering AWS basics, global infrastructure, and core services.
• Elastic Compute Cloud (EC2): Learn to install and manage virtual server instances with EC2.
• Simple Storage Service (S3): Explore scalable storage solutions with S3.
• Virtual Private Cloud (VPC): Design secure networks and implement them in the AWS cloud.
• Identity and Access Management (IAM): To manage user permissions and policies effectively.
• Relational Database Service (RDS): Establish and operate relational databases in the cloud.
• AWS Lambda: Learn about serverless computing and function-as-a-service concepts.
• CloudFormation: Provide resource provisioning using infrastructure as code.

• Introduction to Big Data on AWS: Gain insights into big data solutions and services offered by AWS.
• Data Collection: Leverage AWS services like Kinesis and IoT to ingest real-time data.
• Data Storage: The data storage solutions have to be implemented using services such as S3, Glacier, and many other services.
• Data Processing: Full-scale processing of large data with AWS Lambda, Glue, and EMR (Elastic Map Reduce).
• Data Analysis: Analyze data employing Redshift, Athena, and QuickSight for business intelligence.
• Data Visualization: Craft interactive dashboards and visualizations of data insights.
• Data Security: Maintain data security using AWS KMS, appropriate IAM policies, and encryption methods.