Highlights of the Course
Algorithmic Design & Complexity,
Premitive and Non-Premitive Structures, Arrays, Stacks, List, Graph, Trees Sets etc.
Advanced Search algorithms
Algorithmic Analysis
Highlights of the Course
Master GIS concepts
Core Spatial Analytics using GIS
2D and 3D Visualizations
Spatio-Temporal Analysis
Virtual Reality with GIS | Download Data
Download Map
Highlights of the Course
Complete learning path from core to advaced topics
Handling simple to complex data types
Data organisation & Statistical Analysis
Visual Analytics
Advanced pakages
Highlights of the Course
Fundamentals of AI, AI Agents,
Classical and Adversial Search, Logical Agents and propositional Logic, Inferencing system, Planning and Knowledge representation. Towards Machine Learning.
Highlights of the Course
Graphics Principles, 2D & 3D Graphics, Perspective projections, Raycasting, Shadows and Rendering effects, Color theory and applications, Image Processing concepts, Spatial and Frequency domain operations, Morphology etc
Highlights of the Course
Mobile Platforms, App Design principles, Ubiquitous Communications, Android Plaltform, Java and Kotlin based apps, Databases & Network communications, Sensors Networks, GUI Programming, Spatial Computing, Augmented Reality implementations
Chapter-wise slide decks are available. Video lectures are marked as updating soon.
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From IT enablement to transformation, digital drivers, maturity and frameworks.
Cloud, social media, IoT, cyber-physical systems, data assets, RPA and automation.
Digital strategy, platform thinking, ecosystems, KPIs and business model renewal.
Data-to-insight pipeline, analytics types, BI dashboards, experiments, metrics and governance.
AI and ML foundations, learning paradigms, and business applications.
LLMs, prompts, RAG, enterprise GenAI workflows, governance and operating models.
Agents, memory, planning, tool use, autonomous workflows, monitoring and validation.
Process redesign, process mining, automation, BPM, KPIs and change management.
Cloud, edge, IoT, digital twins, industrial platforms and vendor strategy.
AI-enabled CX, marketing analytics, personalization, sales copilots and responsible marketing.
Digital operations, supply-chain platforms, intelligent planning, robotics and sustainability.
Culture, collaboration, leadership, skills, governance and wellbeing in digital change.
Cyber risk, privacy, digital trust, securing AI systems, GRC and incident response.
AI governance, digital law, bias, explainability, safety, regulation and responsibility.
Manufacturing, banking, retail, healthcare, smart cities and cross-sector playbooks.
Roadmap design, North Star, portfolio planning, metrics, governance and maturity evolution.
Slides and videos for deep learning foundations, CNNs, RNNs, autoencoders, GANs and generative models.
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Neural networks, activation functions, forward propagation and learning intuition.
Gradient descent, chain rule, loss functions, momentum, RMSProp and Adam.
Kernels, feature maps, pooling, CNN architecture and image applications.
RNNs, LSTM, GRU, BPTT, vanishing gradients and NLP applications.
Denoising, sparse, contractive and variational autoencoders.
Generator, discriminator, GAN training, conditional GANs and creative applications.
Lecture resources for transformer architectures, pre-training, post-training, prompting, RAG, agents and responsible deployment.
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Attention, multi-head attention, embeddings, positional encoding and decoder blocks.
Causal language modeling, masked language modeling, scaling and data preparation.
Instruction tuning, preference optimization, RLHF, DPO and evaluation.
Prompt patterns, context windows, system prompts and structured outputs.
Retrieval pipelines, embeddings, vector databases, grounding and evaluation.
Tool use, planning, orchestration, safety, robustness and deployment governance.
Resources for predictive modelling, regression, classification, trees, ensembles, SVM and evaluation.
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Labels, features, training, validation, testing and model generalization.
Linear regression, logistic regression, regularization and business interpretation.
Entropy, information gain, pruning, 0R, 1R and RIPPER.
Bagging, Random Forests, AdaBoost, Gradient Boosting, voting and stacking.
Margins, kernels, soft-margin classification and business applications.
Confusion matrix, precision, recall, F1-score, ROC, cross-validation and tuning.
Resources for clustering, dimensionality reduction, anomaly detection, association rules and topic modeling.
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Similarity, distance measures, K-means, hierarchical clustering and cluster validity.
DBSCAN, Gaussian mixtures and discovering complex cluster structures.
PCA, t-SNE, UMAP and visualization of high-dimensional data.
Support, confidence, lift, Apriori and market-basket analysis.
Isolation Forest, One-Class SVM and outlier interpretation.
LDA, document-term matrices, word clouds and thematic discovery.
Lecture resources for trend, seasonality, smoothing, ARIMA, forecasting and model evaluation.
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Components, stationarity, autocorrelation and time-indexed data.
Moving averages, exponential smoothing, trend and seasonal decomposition.
Autoregressive and moving-average models with ACF and PACF interpretation.
Differencing, model identification, seasonal models and diagnostics.
MAE, RMSE, MAPE, rolling validation and forecast uncertainty.
Resources for ethical disclosure, penetration testing, vulnerability analysis, client-side exploits and malware analysis.
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Legal boundaries, responsible disclosure and professional conduct.
Reconnaissance, scanning, exploitation, reporting and remediation.
Framework-based exploitation, payloads, modules and controlled testing.
Passive analysis, static analysis, reverse engineering and exploit reasoning.
Windows access control, fuzzing, browser exploitation and secure mitigation.
Collecting samples, initial triage, behavior analysis and safe lab practices.
Resources for forensic process, evidence handling, file systems, memory, network, mobile and cloud forensics.
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Identification, preservation, acquisition, examination, analysis and reporting.
Forensic integrity, hashing, documentation and admissibility.
Partitions, metadata, deleted files, timelines and artifact recovery.
Volatile evidence, packet analysis, logs and incident reconstruction.
Device artifacts, cloud evidence, acquisition constraints and reporting.
Resources for GIS, spatial analytics, remote sensing, GeoAI, spatial decision support and geospatial intelligence.
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Coordinate systems, vector data, raster data, projections and map layers.
Buffers, joins, overlay, proximity and suitability analysis.
Satellite imagery, bands, indices, classification and change detection.
Spatial autocorrelation, interpolation, hot spots and uncertainty.
Machine learning for geospatial data, object detection, risk mapping and explainability.
Interactive maps, dashboards, spatial services and geospatial applications.
A focused resource section for deep learning methods, architectures, optimization and applications.
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Perceptron, multilayer networks, activations and loss functions.
Backpropagation, initialization, normalization, regularization and dropout.
Momentum, RMSProp, Adam, learning-rate schedules and convergence issues.
Convolution, pooling, feature extraction and image classification models.
Pre-trained models, fine-tuning, feature extraction and deployment considerations.