AI/ML Journey

My structured path through the world of Artificial Intelligence and Machine Learning. Starting from the basics and building towards creating AI-powered applications.

Courses Completed

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Projects Built

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Research Papers

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Models Trained

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Currently Learning

Machine Learning Fundamentals

Working through Andrew Ng's Machine Learning course, focusing on supervised learning algorithms and practical implementation with Python.

Interactive Learning Roadmap

Navigate through my AI/ML learning journey. Click on any node to explore resources, notes, and insights from that area of study.

AI/ML Mastery Journey - From Zero to Million Dollar SaaS

Comprehensive roadmap to master AI/ML, build custom models, and create scalable AI-powered SaaS businesses

in-progress

Mathematical Foundations Refresh

Strengthen mathematical foundations for advanced ML concepts

planned

Python for Advanced ML

Master Python ecosystem for ML and deep learning

planned

Statistics & Probability for ML

Deep understanding of statistical methods and probability theory

planned

Machine Learning Fundamentals

Core ML algorithms and theory

planned

Data Engineering for ML

Data pipelines, ETL, and big data processing for ML

planned

Deep Learning Foundations

Neural networks, backpropagation, and optimization

planned

Advanced Machine Learning

Ensemble methods, advanced optimization, and model selection

planned

Transformer Architecture & LLMs

Understanding and building transformer-based models

planned

Advanced Deep Learning

GANs, VAEs, attention mechanisms, and advanced architectures

planned

LLM Training & Fine-tuning

Training large language models from scratch and fine-tuning techniques

planned

RAG Systems & Knowledge Graphs

Advanced RAG architectures and knowledge graph integration

planned

Multimodal AI

Vision-language models, audio processing, and multimodal architectures

planned

Distributed Training & Scaling

Training models across multiple GPUs and nodes

planned

Model Optimization & Compression

Quantization, pruning, distillation, and efficient architectures

planned

MLOps & Production Systems

Deploying, monitoring, and maintaining ML models in production

planned

DSA for ML Systems

Data structures and algorithms optimized for ML applications

planned

Graph Neural Networks

GNNs for recommendation systems and knowledge graphs

planned

System Design for ML

Designing scalable ML systems and architectures

planned

Distributed Systems

Building distributed systems for ML at scale

planned

Vector Databases & Similarity Search

Efficient storage and retrieval for embeddings

planned

Advanced Computer Vision

Object detection, segmentation, and generative models

planned

Audio Processing & Speech AI

Speech recognition, synthesis, and audio generation

planned

Cloud Platforms for ML

AWS, GCP, Azure for ML workloads

planned

Edge AI & Mobile Deployment

Deploying ML models on edge devices and mobile platforms

planned

ML Monitoring & Observability

Monitoring model performance and data drift in production

planned

AI Research & Paper Writing

Conducting AI research and publishing papers

planned

Bayesian Methods & Uncertainty

Bayesian inference and uncertainty quantification in ML

planned

Reinforcement Learning

RL algorithms and applications in AI systems

planned

Business Strategy & AI Product Management

Building AI products and business strategy

planned

AI SaaS Development

Building and scaling AI-powered SaaS products

React Flow