AI & ML Roadmap
🚀 AI & Machine Learning: The 6-Phase Roadmap
This section transforms you from a data observer into an AI Architect. Every technology here follows our rigid 6-phase implementation to ensure foundations come before optimization.
🏗️ The 6-Phase Roadmap
Phase 1: Foundations
- Focus: Linear Regression, Logistic Regression, and the Scikit-Learn pipeline.
- Goal: Get a “Hello World” model running with a deep understanding of Bias and Variance.
Phase 2: Supervised Logic
- Focus: Decision Trees, Random Forests, XGBoost, and SVMs.
- Goal: Master non-linear decision making and ensemble techniques.
Phase 3: Unsupervised & Features
- Focus: K-Means Clustering, PCA (Dimensionality Reduction), and Feature Engineering.
- Goal: Discover hidden structures and optimize the “Information Density” of your data.
Phase 4: Deep Learning Foundations
- Focus: Multilayer Perceptrons (MLP), Backpropagation, and PyTorch/TensorFlow.
- Goal: Understand the execution pipeline of artificial neural networks.
Phase 5: Generative AI & Modern NLP
- Focus: Transformers, Attention Mechanisms, and Advanced RAG.
- Goal: Build production-ready GenAI applications using Vector Databases.
Phase 6: Agents & Model Tuning
- Focus: AI Agents, Fine-Tuning (LoRA/PEFT), and RLFH.
- Goal: Moving from simple chatbots to autonomous reasoning systems.
🛠️ Essential Toolbox
- Libraries:
scikit-learn,pytorch,xgboost,pandas. - Infrastructure:
MLflow(Tracking),DVC(Data versioning). - Math: Linear Algebra (Vectors/Matrices), Calculus (Gradients).