Stage 1
Foundation 14 steps 1 Programming Fundamentals 2 Python Basics → Prerequisite 3 Python Data Structures → Prerequisite 4 Object-Oriented Programming in Python → Prerequisite 5 Functional Programming in Python ⇒ Derives from 6 Scientific Python Stack 7 Data Manipulation with Pandas → Prerequisite 8 Sorting and Searching Algorithms 9 Graph Algorithms ⇒ Derives from 10 Dynamic Programming ⇒ Derives from 11 Linear Algebra: Vectors and Spaces 12 Linear Algebra: Matrices and Decompositions → Prerequisite 13 Calculus: Differentiation 14 Calculus: Integration → Prerequisite
Stage 2
Core Concepts 14 steps 15 Probability: Random Variables and Distributions 16 Probability: Joint and Conditional Distributions → Prerequisite 17 Descriptive Statistics 18 Inferential Statistics → Prerequisite 19 Gradient Descent and Variants 20 Constrained Optimization 21 Introduction to Machine Learning 22 Linear Regression → Prerequisite 23 Logistic Regression ⇒ Derives from 24 Regularization Techniques ⇒ Derives from 25 Support Vector Machines ⇒ Derives from 26 Decision Trees ≈ Related 27 Bagging and Random Forests ⇒ Derives from 28 Boosting Methods ⇒ Derives from
Stage 3
Advanced Topics 14 steps 29 Unsupervised Learning: Clustering 30 Dimensionality Reduction ⇒ Derives from 31 Model Evaluation and Validation 32 Feature Engineering ≈ Related 33 Introduction to Neural Networks 34 Backpropagation and Computational Graphs → Prerequisite 35 Optimizers for Deep Learning → Prerequisite 36 Weight Initialization and Normalization ≈ Related 37 Convolutional Neural Networks 38 Modern CNN Architectures ⇒ Derives from 39 Recurrent Neural Networks 40 Attention Mechanisms → Prerequisite 41 Transformer Architecture → Prerequisite 42 NLP: Tokenization and Text Preprocessing
Stage 4
Specialization 14 steps 43 NLP: Word Embeddings 44 NLP: Sequence-to-Sequence Models → Prerequisite 45 Large Language Model Architectures 46 LLM Pretraining ⇒ Derives from 47 Prompt Engineering and In-Context Learning ⇒ Derives from 48 Fine-tuning Large Language Models ≈ Related 49 Retrieval-Augmented Generation: Retrieval 50 Retrieval-Augmented Generation: Integration → Prerequisite 51 RAG Evaluation and Optimization → Prerequisite 52 AI Agents: Frameworks and ReAct 53 AI Agents: Memory and Planning ⇒ Derives from 54 Evaluating AI Agents → Prerequisite 55 Computer Vision: Backbones and Pretraining 56 Object Detection and Segmentation → Prerequisite
Stage 5
Application 14 steps 57 Vision Transformers and Multi-modal Models 58 MLOps: Experiment Tracking and Versioning 59 MLOps: Data and Model Versioning → Prerequisite 60 MLOps: ML Pipelines and Orchestration → Prerequisite 61 Model Serving and APIs 62 Production Scaling and Optimization ⇒ Derives from 63 Production Monitoring and Alerts → Prerequisite 64 CI/CD for Machine Learning ≈ Related 65 Ethical AI and Fairness 66 AI Infrastructure: GPUs and Distributed Training 67 Cloud AI Platforms and Services ≈ Related 68 AI Project Planning and Scoping 69 End-to-End AI Implementation Project → Prerequisite 70 Production Handover and Documentation → Prerequisite
ALLPATHFoundation