Introduction to SDLC and Generative AI | AI-Powered Software Development – GenAInstein
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AI and DeepTech

Instructor: Arun Kumar A R Instructor - Brisa Technologies

Centre for Development of Advanced Computing (C-DAC)

Course Fees: ₹4,999/-

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Starting On:
May 1, 2025

Format:
Online + Interactive Demo
Language:
English
Duration:
3 hours
Certificate:
Digital Copy
Audience:
Students of VTU final year

Course Overview

This course provides a foundational and advanced exploration of artificial intelligence and deep learning technologies. It covers key concepts from data science, probability, and statistics to machine learning pipelines, neural networks, and deep learning architectures such as CNNs and RNNs. Students gain practical experience in model deployment, MLOps, and professional skills development, including Agile collaboration and ethical AI practices. The curriculum balances theoretical understanding with hands-on programming and a capstone project to solidify learning.

What You'll Learn

Grasp the evolution and foundational concepts of artificial intelligence, deep learning, and their real-world applications across diverse industries.Acquire hands-on experience in Python for data science, including essential libraries (NumPy, Pandas) for data wrangling and analysis.Master the end-to-end machine learning workflow: data preprocessing, feature engineering, model training, evaluation, and deployment.Build and evaluate a variety of AI and deep learning models from supervised and unsupervised algorithms to convolutional and recurrent neural networks.Apply practical skills in ML Ops: API development, containerization with Docker, monitoring, and model lifecycle management.Analyze ethical, societal, and regulatory implications of AI, emphasizing responsible deployment and bias mitigation.

Why Enroll in this Course?

Develop a deep understanding of artificial intelligence and deep learning, covering both foundational theory and hands-on, industry-aligned practice.

Acquire versatile programming and data science skills essential for building and deploying machine learning models with technologies like Python, TensorFlow, and PyTorch.

Learn to apply AI for real business impact from healthcare and finance to manufacturing using up-to-date workflows, tools, and project-based learning.

Gain expertise in essential processes: data wrangling, model evaluation, deployment, MLOps, ethics, and responsible AI.

Equip yourself with collaborative, Agile project experience and the capability to communicate technical findings to diverse stakeholders.

Validate your knowledge with a capstone project, building an impressive portfolio for employers and academic advancement.

Remain future-ready as AI transforms every major sector, ensuring your skills match what top employers and research labs are demanding now.

Course Syllabus

Detailed exploration of AI’s journey from symbolic AI (expert systems), through statistical methods, to contemporary deep learning and generative AI.
Discuss landmark achievements such as Deep Blue defeating Kasparov (1997), AlphaGo’s innovation in reinforcement learning (2016), and the transformative impact of GPT models in natural language understanding.
Discuss differences between AI, Machine Learning, Deep Learning, and Deep Tech innovation areas.
Students will select one breakthrough AI system (e.g., AlphaGo or GPT-3), research it through academic articles, videos, and blogs.
They will write a structured 1-page summary capturing the technology, challenges addressed, and societal impact (positive/negative).
Facilitate peer-to-peer oral sharing to promote communication skills and deepen understanding.
This fosters both comprehension and critical analysis.
Present detailed case studies from sectors including healthcare (diagnostic imaging AI), finance (fraud detection), manufacturing (predictive maintenance), and education (adaptive learning).
Highlight AI’s potential and limitations in each domain.
Discuss critical challenges including algorithmic bias, automation anxiety among workers, privacy risks, and the balance between innovation and regulation. Encourage thought experiments and debate exercises.
Assign students to pick an AI startup or project within a chosen domain (e.g., healthcare).
They conduct focused research using credible sources, prepare a concise 2-slide presentation outlining: (1) AI technology used, (2) problem addressed, (3) impact and challenges faced.
Presentations promote research skills, synthesis, and peer learning.
Instructor offers feedback emphasizing clarity, critical thinking, and technical accuracy.
Detailed walkthrough of course objectives, lecture topics, grading policies, and capstone projects.
Introduction to version control with Git: conceptual overview of repositories, commits, branches, pull requests.
Step-by-step practical guide to installing Git, creating a GitHub account, cloning repositories, making commits, pushing code, and pull request workflows.
Students validate setup with screenshots and troubleshoot collaboratively.
Introduction to project-based learning emphasizing Agile principles such as iterative work cycles, collaboration, and rapid prototyping.
Overview of reproducibility challenges in AI research and development. Detailed walkthrough of Jupyter Notebooks, including markdown for documentation and how notebooks support reproducible workflows.
Best practices in version control for notebooks and collaborative coding.
Students will install Anaconda or use Google Colab and create a reproducible AI notebook containing markdown cells for text and placeholders for code.
They will document workflow steps, add comments, and upload notebooks to GitHub repositories.
Emphasis on fostering good documentation habits and collaboration skills.
Sharing and peer review of notebooks will be encouraged to reinforce quality and reproducibility.
In-depth introduction to Python syntax including variables, data types (strings, integers, floats, lists, dictionaries), control flow (conditionals, loops), and functions.
Students will learn how to write clean, readable code with comments and simple scripts to perform data filtering, aggregation, and formatted output.
Students write Python scripts implementing example tasks such as temperature conversion between Celsius and Fahrenheit, classification of numbers (odd/even), and basic calculator functions.
These coding exercises will be done interactively using Jupyter or Google Colab.
Students submit notebooks via GitHub and explore curated Python beginner tutorials and exercises for reinforcement.
Exploration of online communities and resources like Python.org, Real Python, or W3Schools is encouraged.
Introduction to NumPy arrays, creation methods, indexing, slicing, reshaping, and broadcasting rules that enable vectorized operations over data arrays.
Exploration of basic numerical operations (addition, multiplication), random number generation, and simulation setups.
Students will grasp why NumPy is critical for efficient numerical computing in AI pipelines.
Understand stages of data science for AI projects: requirements, data collection, cleaning, preprocessing, and EDA. Students explore real-world messy datasets and map each step in the lifecycle, discussing challenges faced in industry.
Exercise: Students choose a public dataset, identify potential use-cases, and present initial questions they’d explore.
Dive into loading, exploring, and cleaning data with Pandas
Techniques: handling missing data, merging datasets, grouping, and performing aggregations.
Hands-on: Clean a “dirty” dataset (e.g., Titanic or US census data) and summarize missing vs. valid entries; reflect on the effect of poor data.
Students push notebooks to GitHub, fostering version control.
Introduction to plotting and visualizing data distributions, relationships, correlations, and outliers.
Students create histograms, scatterplots, boxplots.
Case study: Explore COVID-19 trends or stock market data with visualizations.
Each student explains how different plots reveal different insights.
Begin with events, sample space, classical & empirical probability, conditional statements, and independence.
Explore normal, binomial, and Poisson distributions through simulated examples.
interactive Exercise: Students roll dice or generate random numbers with NumPy to build distribution intuition.
Mean, median, variance, skewness, and visualization of these measures.
Coverage of hypothesis testing and p-values in the AI context.
Mini-project: Analyze real-world data for bias (e.g., salary or gender datasets), visualize findings, and discuss societal impacts.
Peer discuss ways to mitigate bias in AI.
Define and distinguish types of features: categorical, numerical, ordinal, text, etc.
Demonstrate encoding (one-hot, label encoding), normalization, scaling (min-max, standard), and feature selection algorithms.
Workshop: Implement encodings and scaling on a provided dataset, documenting results and issues in Jupyter notebooks.
Explore handling strategies: imputation (mean/median/mode), deletion, and KNN-imputation.
Discover outlier detection using Z-score, IQR, and visualization (boxplot).
Lab: Students profile real data, identify missing/outliers, and apply correction, comparing model performance before and after preprocessing.
Intuitively explain Principal Component Analysis; visualize how it works in reducing features while preserving variance.
Walk through the mathematics and practical pros/cons.
Hands-on: Apply PCA on “Iris” dataset, plot explained variance, choose new features, and interpret meaning.
Build an automated data preprocessing pipeline using Scikit-learn’s Pipeline API: include scaling, encoding, imputation, and PCA in a reproducible, documented workflow.
Group task: Teams take messy open datasets and produce a “production-ready” pipeline, reviewing each other's work.
In-depth guide to setting up Python environments, Jupyter extensions, VS Code, and GitHub Actions for automation.
Mini-project: research and present a recent AI data preprocessing paper—summarize the “state of the art” vs. basic methods.
Understand the typical workflow for ML: splitting data, labeling, model training/testing, metric selection, business framing.
Discuss project lifecycle (CRISP-DM).
Activity: Students diagram the flow from raw data to production ML solution.
Study linear regression, cost functions, overfitting, underfitting, and regularization (Ridge/Lasso).
Practical: build regression models on real datasets and visualize results.
Mini-project: Predict housing prices or exam scores.
Concepts: binary vs. multiclass, logistic regression, confusion matrix, ROC curve, F1-score.
Lab: Fit classifiers on datasets (e.g., cancer detection) and compare accuracy/recall.
K-fold cross-validation, train/test/validation split, selection bias.
Exercise: Compare several models using cross-validation on a shared dataset.
Discuss bias-variance tradeoff.
Students select a small dataset, formulate a business problem, apply all steps from preprocessing to training, evaluate multiple models, and present insights.
Notebooks and presentations reviewed in class.
Intuitive clustering, distance metrics (Euclidean, Manhattan), Elbow method for selecting cluster number, use-cases (customer segmentation, anomaly detection).
Demo: Cluster customer or animal datasets, visualize clusters.
Evaluate clusters quantitatively.
Use t-SNE or PCA for visualizing high-dimensional clusters.
Lab: Students calculate metrics and plot results for assigned datasets.
Find outlier detection methods, explore real-world examples from cybersecurity or IoT.
Workshop: Detect anomalies in credit card data or network traffic.
Core idea of autoencoders, structure/architecture, hands-on implementation in Keras or PyTorch for dimensionality reduction.
Mini-project: Visualize learned features.
Setting up Python environments for high-performance computation, GPU/Colab use.
Independent research and short reports on non-traditional clustering (e.g., DBSCAN, OPTICS).
Understand neurons, layers, activation functions (sigmoid, ReLU, tanh), weights, and biases.
Visualize forward and backward propagation.
Activity: Build a neural from scratch in Python (no frameworks).
Gradient descent, learning rate, weight initialization, overfitting, dropout, batch normalization.
Mini-project: Experiment with regularization effects on accuracy in small networks.
Environment setup, installation of frameworks, loading datasets, and constructing basic models.
Lab: Run simple classification using MNIST digits or Iris dataset, test changing architectures.
Hyperparameters, debugging training, visualization of training progress (loss/accuracy graphs), early stopping.
Case Study: Analyze where nets fail and why.
Design, train, and report on a simple neural network.
Share best/worst practices observed, document runs, and publish results to GitHub for peer feedback.
Explore convolution versus dense layers, feature maps, pooling, parameter sharing, and why CNNs suit image tasks.
Visuals and animations reinforce concepts.
Build CNNs for digit/image recognition.
Experiment with architecture depth, filters, pooling sizes.
Project: Compete in-class for best accuracy/result on a chosen image dataset.
Introduce RNNs, LSTMs, GRUs—handling sequences, memory, and vanishing gradients.
Examples in text (language modeling), time (sensor data).
Implement sequence model for simple text generation or sentiment analysis (IMDB dataset).
Report training challenges and solutions found.
Students try CNNs or RNNs on external data (music, speech, handwriting), document learnings; peer review sessions for code readability and reproducibility.
What is transfer learning? How to leverage ImageNet models for vision, BERT for text?
Example: Fine-tune a pre-trained ResNet for a new image classification.
Use data augmentation for robustness: rotates, flips, crops, noise addition, GANs (theory only).
Hands-on: Augment images and measure accuracy gains.
Explore methods to escape overfitting, debug model failures, analyze misclassifications, and optimize training resources.
Prepare models for deployment: serialization (pickle, ONNX), requirements files, optimizing for resource limits.
Task: Export an ML model, test with a sample API.
State-of-the-Art Share-Out: Students summarize recent research breakthroughs in transfer learning, present lessons learned, discuss implications for deployment.
Concept of RESTful APIs, Flask/FastAPI basics, creating endpoints for ML prediction.
Project: Shuttle a previously trained model into a deployed API and test with sample JSON.
Why containers? Build Dockerfiles for ML APIs, troubleshoot images, address dependency conflicts, and best practices for reproducible environments.
Continuous integration pipelines (GitHub Actions, Travis CI), test automation, automatic deployments, rollback strategies for ML.
Student setup: Jenkins or GitHub workflows for their repo.
Importance of model monitoring, open-source tools, detecting data and concept drift. Log metrics using MLflow or Weights & Biases.
Containerize, deploy, monitor, and expose a working ML app (simple classifier or image recognizer); students must handle debugging and share reliability reports.
Definitions of fairness, real-world cases of bias in decision systems, frameworks for responsible AI, and AI explainability (LIME, SHAP basics).
Students audit a simple model for bias, discuss mitigations.
Key AI-related regulations (GDPR, HIPAA, India’s legislation), industry obligations, and privacy-preserving ML (differential privacy, data minimization).
Guest talk or recorded session for current events.
Scrum, Kanban in technical teams; task management tools (Jira, Trello).
Simulate team standups, sprints, retrospectives.
Activity: Students plan a “sprint” for their class project.
Storytelling with data, slide best-practices, visual hierarchy.
Students prep a 5-minute technical talk and receive peer feedback.
Teams work collaboratively through planning, coding, code review, and group presentation rounds, mimicking professional environments.
Teams brainstorm ideas, finalize topics, draft objectives and milestones, and set expectations with instructors.
Each team writes a detailed project proposal.
Students survey prior work, datasets, similar solutions, and regulatory issues.
Deliverable: Literature review summary and dataset checklist.
Install all needed libraries, set up repos, cloud integration (Colab/Azure/AWS), confirm resource access, and adopt best practices from earlier weeks.
eams collect/prepare datasets, perform exploratory analysis, and document approaches.
Instructors conduct formative check-ins.
ross-team feedback sessions, problem-solving, and refining of approaches based on peer and mentor comments.
Data preprocessing, model selection, training, evaluation.
Iterative development with documented progress and mistakes.
Error analysis, documenting failures, model improvement based on results.
API or web app interface integration, deployment testing, user walkthrough creation.
Practice pitching technical results, handling feedback, and setting expectations.
Research and low-stakes experiments with emerging tools/techniques relevant to the project—e.g., new model architectures or visualization libraries.
Model validation, reproducibility checks, comprehensive documentation, prepping for demo day.
Teams deliver mock presentations to the class/instructor panel and receive constructive feedback.
How to craft tech resumes for AI/deep tech, mock interview sessions, and exploration of job boards.
Editing and submitting code, papers, and reports for open science, conferences, or sharing on Stack Overflow/GitHub.
Debrief challenges, instructor and peer Q&A, and discuss strategies for continuous learning.
Teams present to an evaluation panel; Q&A, technical demonstration, impact analysis.
Scoring and panel feedback.
Overview of course content, discussion of stand-out lessons and areas for further growth.
Industry professionals, alumni, or faculty share experiences and answer student questions about real-world AI/deep tech work.
MCQs, open-response questions, scenario-based coding or modeling assignments.
Performance review.
Feedback forms, certificate distribution, advice for advanced study, sharing resources: books, open courses, competitions, and communities.
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Instructor Profile

Arun Kumar A R
Arun Kumar A R

AI & ML | Data Science & Analytics | AI-ML Model Building | Generative AI | Corporate Trainer

 

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Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.
RM
Rahul M.
Software Engineer
As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!
SK
Sneha K.
Final Year Student
What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.
AS
Arjun S.
Developer
The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.
PD
Priya D.
Tech Lead

Certification Details

Free Access: Learn at no cost
Optional Certification Exam: ₹499/-
Certification Criteria: Minimum 75% score required
Certificate Type: Digital e-certificate (no hard copies)
Exam Date: To be announced after course completion

Certificate Sample