AI & Data Science from Drishti CPS IIT Indore - GENIUS PRO

Module 2: Python for Data Science – Advanced

• 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.

Module 3: RDBMS & SQL

• 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

Course Term 2 – Machine Learning – Supervised & Unsupervised Learning

Master the skills of Machine learning with supervised and unsupervised learning models

Regression Model: Learn to build, train, test, and implement regression models based on the data type.

• Linear Regression Model: Build models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Classification Models: Explore various classification models, learn to build, train, test, and optimize their performance.
• Algorithms: Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors

Clustering Algorithms: Understand clustering techniques for grouping data, and train, test, and optimize the efficiency of models.

Performance Metrics: Evaluate models using metrics such as recall, precision, F1-score, and support. Analyze classification reports and confusion matrices.

Dimensionality Reduction: PCA and LDA for handling multi-dimensional data and standardizing the features for easier computation.

Time Series Models: Work on different time series models to predict future trends by analyzing the past data.

Course Term 2 – Case Studies

• 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.

Course Term 2 – Deep Learning & Neural Networks

Introduction to Artificial Intelligence, 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 & Artificial Intelligence

Lifecycle of Deep Learning Project

Course Term 2 – NLP and Computer Vision

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.

Course Term 2 – Generative AI & LLM (Large Learning 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.

Course Term 2 – Prompt Engineering

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.

Course Term 2 – AWS (Amazon Web Services)

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.

Course Term 2 – AWS Big Data

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 so many others in 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.