> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Course Roadmap What Youll Learn > Roadmap overview of machine learning fundamentals from history and core components to training, evaluation, learning paradigms, neural networks and practical outcomes This lesson maps the full journey you'll take through machine learning fundamentals — from the historical context to modern large models and evaluation practices. Below is a concise roadmap followed by a short description of each stage and the practical outcomes you should expect. ## Roadmap overview * History and definition * Core ingredients * How models learn * Generalization and evaluation * Types of learning * Neural networks and modern models A handwritten diagram titled "Course Journey" showing a left-to-right progression: History → Core Ingredients → Machine Learning → Neural Networks → Modern. Each stage has brief notes such as "machine learning," "data, features, labels, models," "training, loss functions, gradient descent," and "GPT, evals." This roadmap is intentionally linear: you’ll start with conceptual foundations and build toward practical model design, evaluation, and an introduction to how modern large models are trained and assessed. ## What each section covers ### 1) History and definition A brief history of machine learning and a working definition of the field. * Why ML emerged (statistical learning, compute and data availability). * What “machine learning” means today vs. classical programming. * Key milestones that shaped modern ML. Expected outcome: You’ll be able to explain what ML solves and name major historical shifts (e.g., statistical learning → neural networks → deep learning → large language models). ### 2) Core ingredients The fundamental building blocks every ML practitioner should know: * Data: how data is collected and basic quality concerns. * Features: representation and feature engineering basics. * Labels: supervised targets and labeling considerations. * Models: hypothesis space and common model families. * Parameters & weights: how a model’s behavior is controlled. Expected outcome: Identify dataset types and outline how model choice and feature design affect performance. ### 3) How models learn Key mechanics behind training: * Training procedures and pipelines. * Loss functions — how we measure errors. * Optimization techniques, with emphasis on gradient descent and variants (SGD, Adam). Expected outcome: Given a simple model, describe the training loop, pick an appropriate loss, and select an optimizer. ### 4) Generalization and evaluation How to determine if a model works on new data: * Train/validation/test splits and cross-validation. * Metrics for classification vs. regression. * Overfitting vs. underfitting and regularization strategies. Expected outcome: Choose the correct evaluation metric and design an experiment that estimates real-world performance. ### 5) Types of learning Categorization of learning paradigms: * Supervised learning (classification, regression). * Unsupervised learning (clustering, dimensionality reduction). * Brief look at semi-supervised, self-supervised, and reinforcement learning. Expected outcome: Map real problems to appropriate learning paradigms and pick starter algorithms. ### 6) Neural networks and modern models How modern models are structured and trained, plus current trends: * Neural network architectures and training dynamics. * Large models (e.g., GPT-style transformers): pretraining, fine-tuning, and scaling behavior. * Modern evaluation practices, safety considerations, and active research directions. Expected outcome: Explain the transformer pretraining/fine-tuning workflow and basic safety/evaluation issues for large models. ## Quick reference table | Module | Main topics | What you’ll be able to do | | - | - | - | | History & definition | Origins, definitions, milestones | Explain the evolution and role of ML | | Core ingredients | Data, features, labels, models | Design data and features for a task | | How models learn | Loss, optimization, training loop | Implement and tune a basic training loop | | Generalization & evaluation | Metrics, validation, overfitting | Evaluate model performance robustly | | Types of learning | Supervised, unsupervised, others | Select an appropriate learning paradigm | | Neural networks & modern models | Architectures, transformers, safety | Summarize large-model workflows and risks | ## Learning outcomes & next steps By the end of this lesson you should be able to: * Trace the path from raw data to a trained model and deployed predictions. * Choose the right model and evaluation strategy for a simple real-world problem. * Describe how neural networks and large pretrained models are trained and evaluated. Further reading and references: * [A brief introduction to machine learning (Wikipedia)](https://en.wikipedia.org/wiki/Machine_learning) * [Deep Learning Book (Goodfellow, Bengio, Courville)](https://www.deeplearningbook.org/) * [scikit-learn: Machine Learning in Python](https://scikit-learn.org/) If you’re new to ML, a basic familiarity with linear algebra, probability, and Python will make the concepts easier to follow. Consider refreshing those fundamentals before deep-diving into model training. By the end of this lesson, you should understand the complete path from raw data through model training to a deployed system that makes useful predictions. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.