Machine Learning Roadmap
A complete Gate Smashers Machine Learning roadmap covering foundations, supervised learning, evaluation, unsupervised learning, and advanced methods in a clean beginner-to-advanced path.
Learning outline
Expand a stage, choose a topic and start its Gate Smashers lectures.
01Foundations4 topics · 10 lectures0 / 10
▶ML Roadmap & Problem-Solving Workflow0 / 2 lectures
▶Learning Paradigms in ML0 / 2 lectures
▶Statistical Foundations for ML0 / 4 lectures
▶Probability & Bayes Foundations0 / 2 lectures
02Supervised Learning2 topics · 5 lectures0 / 5
▶Regression Foundations0 / 3 lectures
▶Regularization & Regression Error0 / 2 lectures
03Classification Models5 topics · 11 lectures0 / 11
▶Logistic Regression & Model Comparison0 / 2 lectures
▶Naive Bayes Classification0 / 1 lectures
▶kNN Classification & Regression0 / 3 lectures
▶Decision Trees & ID30 / 3 lectures
▶Support Vector Machines0 / 2 lectures
04Evaluation & Data Preparation4 topics · 11 lectures0 / 11
▶Cross Validation Techniques0 / 3 lectures
▶Bias, Variance, Leakage & Confusion Matrix0 / 3 lectures
▶Data Preprocessing & Cleaning0 / 3 lectures
▶Imputation & Feature Preparation0 / 2 lectures
05Unsupervised Learning3 topics · 7 lectures0 / 7
▶Clustering Foundations0 / 2 lectures
▶Hierarchical Clustering Techniques0 / 3 lectures
▶K-Medoids Clustering0 / 2 lectures
06Advanced Methods3 topics · 11 lectures0 / 11
▶Ensemble Learning Methods0 / 5 lectures
▶LDA & PCA for Dimensionality Reduction0 / 2 lectures
▶From Perceptrons to Modern Neural Models0 / 4 lectures
Topics covered in this roadmap
Use this stage-by-stage outline to understand the complete learning path before opening the interactive roadmap.
Foundations
ML Roadmap & Problem-Solving Workflow
Machine learning starts with understanding what kinds of problems it solves and how an end-to-end ML project moves from idea to deployment. These videos introduce the broader data science and ML landscape, then break the workflow into practical steps so the learner sees the complete picture before studying algorithms.
Learning Paradigms in ML
Supervised and unsupervised learning form the first major split in machine learning. This node explains what labelled and unlabelled data mean, how the two paradigms differ, and why the choice of learning setup determines which family of algorithms makes sense.
Statistical Foundations for ML
Before modelling, data must be summarized and inspected using basic statistics. These videos cover central tendency, dispersion, simple Python implementation, and correlation so learners can reason about data shape, spread, and relationships before training a model.
Probability & Bayes Foundations
Probability provides the language for reasoning about uncertainty in machine learning. These lessons explain conditional probability, total probability, and Bayes theorem so learners can understand how new evidence changes the likelihood of an event.
Supervised Learning
Supervised Learning Track
In supervised learning, the model learns from labelled examples and is trained to predict known targets. This stage groups the core regression topics that establish how models fit numeric targets, compare objective functions, and control overfitting.
Regression Foundations
Regression predicts continuous outputs such as price, marks, or sales. These videos introduce regression intuition, explain linear regression clearly, and extend the idea to multiple linear regression so learners understand both single-feature and multi-feature prediction.
Regularization & Regression Error
A useful regression model must fit the data without becoming unnecessarily complex. This node covers ridge and lasso regression together with mean squared error, showing how we measure prediction error and use penalties to control overfitting.
Classification Models
Classification Algorithms
Classification predicts discrete labels such as yes or no, spam or not spam, or disease classes. This stage brings together linear, probabilistic, distance-based, tree-based, and margin-based methods so learners can compare how different classifiers make decisions.
Logistic Regression & Model Comparison
Logistic regression is a classification algorithm even though its name contains regression. These videos explain logistic regression with simple intuition and compare it with linear regression so learners know why the output type and decision boundary matter.
Naive Bayes Classification
Naive Bayes applies Bayes theorem to classification by estimating how likely each class is for the observed features. The method uses a simplifying feature-independence assumption, making it fast, practical, and easy to interpret for many classification problems.
kNN Classification & Regression
k-Nearest Neighbours predicts by looking at nearby training examples in feature space. This node builds intuition, shows how kNN works for both classification and regression, and includes a numerical example so the voting and distance logic becomes concrete.
Decision Trees & ID3
Decision trees split the feature space into simple rule-based regions and are easy to interpret. These videos cover the tree-building idea, the ID3 algorithm, and a worked example using entropy and information gain so learners understand how a tree chooses a split.
Support Vector Machines
Support Vector Machines classify data by finding a separating boundary with maximum margin. This node pairs conceptual explanation with numerical treatment so learners understand support vectors, hyperplanes, and why margin maximization is central to SVM performance.
Evaluation & Data Preparation
Model Evaluation & Data Preparation
Building a model is only part of machine learning; the rest is checking whether the model can be trusted and whether the data is ready in the first place. This stage groups validation methods, quality diagnosis, cleaning, and feature preparation into one practical workflow.
Cross Validation Techniques
A model should be evaluated on data that was not used to fit it. This node explains standard cross-validation, k-fold cross-validation, and leave-one-out cross-validation so learners can estimate generalization performance more reliably than with a single split.
Bias, Variance, Leakage & Confusion Matrix
A model can fail because it is too simple, too complex, or evaluated incorrectly. This node explains bias-variance trade-off, data leakage, and confusion matrix terms so learners can diagnose classification mistakes and identify sources of misleading performance.
Data Preprocessing & Cleaning
Real-world data usually contains missing values, inconsistent formats, and transformations that must be handled before learning begins. These videos introduce preprocessing, discuss missing values, and explain the fit-transform pattern that supports reusable ML pipelines.
Imputation & Feature Preparation
Once basic cleaning is in place, the next step is deciding how to recover missing information and represent useful signals. This node focuses on kNN imputation and feature extraction, showing how thoughtful data preparation improves model readiness and downstream performance.
Unsupervised Learning
Unsupervised Learning Track
In unsupervised learning, the data has no target labels and the goal is to discover structure. This stage focuses on clustering methods that group similar observations and help learners understand exploratory pattern discovery.
Clustering Foundations
Clustering is a core unsupervised task in which similar points are grouped together. These videos introduce k-means and hierarchical clustering at a high level so learners understand the difference between partition-based clustering and tree-structured clustering.
Hierarchical Clustering Techniques
Hierarchical clustering changes behaviour depending on how distances between clusters are defined. This node covers single linkage, complete linkage, and a worked example so learners can compare chaining behaviour with compact-cluster behaviour.
K-Medoids Clustering
K-medoids is a clustering variant that represents each cluster using an actual data point instead of an arithmetic mean. This node explains the method and the cost-update process that helps determine whether a proposed medoid swap improves the clustering.
Advanced Methods
Advanced ML Methods
After the core supervised and unsupervised ideas are in place, learners can move to more powerful and more scalable modelling ideas. This stage groups ensemble learning, dimensionality reduction, and neural-model foundations as the next step in ML depth.
Ensemble Learning Methods
Ensemble learning combines multiple learners so the group performs better than an individual model alone. This node covers ensemble intuition, random forest, bagging, boosting, and stacking so learners can compare how different combination strategies improve stability and accuracy.
LDA & PCA for Dimensionality Reduction
Dimensionality reduction compresses information into fewer dimensions while trying to keep the most useful structure. This node pairs LDA and PCA to highlight the difference between supervised class-separating projection and unsupervised variance-based projection.
From Perceptrons to Modern Neural Models
Neural learning begins with simple perceptron-based ideas and grows into multilayer models with much richer representational power. This node also includes the LLaMA token-and-parameter video as a bridge from classical neural basics to the terminology used in modern large language models.
Completion
Machine Learning Roadmap Complete
This final node marks the shift from content coverage to practice and revision. After finishing the roadmap, the learner should solve end-to-end ML problems, revisit weak stages, and gradually connect theory with projects, experimentation, and interview preparation.