The Complete Machine Learning Roadmap

Master the art of algorithms, data, and predictive modeling step by step.

Basic overview

What is Machine Learning?

Learn More

Why Learn Machine Learning?

Learn More

Estimated time commitment for the roadmap.

Learn More

Phases of the Machine Learning Roadmap

Phase 1: Fundamentals of Machine Learning

Introduction to Machine Learning

Learn More

Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

Learn More

Understanding the ML Workflow (Data Collection, Preprocessing, Model Training, Evaluation, Deployment)

Learn More

Phase 2: Prerequisites

Programming:

Python Basics (Variables, Loops, Functions, Libraries like NumPy, Pandas)

Learn More

Mathematics:

Linear Algebra (Vectors, Matrices) Probability and Statistics (Distributions, Bayes’ Theorem) and Calculus (Derivatives, Gradients for Optimization)

Learn More

Phase 3: Data Preprocessing and Cleaning

Understanding Data Types

Learn More

Handling Missing and Erroneous Data

Learn More

Feature Scaling and Encoding

Learn More

Exploratory Data Analysis (EDA) Techniques

Learn More

Phase 4: Supervised Learning

Regression Algorithms (Linear Regression, Polynomial Regression)

Learn More

Classification Algorithms (Logistic Regression, Decision Trees, Random Forest, SVM)

Learn More

Evaluation Metrics: MSE, RMSE, Accuracy, Precision, Recall, F1 Score

Learn More

Phase 5: Unsupervised Learning

Clustering (K-Means, Hierarchical Clustering, DBSCAN)

Learn More

Dimensionality Reduction (PCA, t-SNE)

Learn More

Applications of Unsupervised Learning

Learn More

Phase 6: Advanced Machine Learning Techniques

Ensemble Learning (Bagging, Boosting, Stacking)

Learn More

Gradient Boosting Algorithms (XGBoost, LightGBM, CatBoost)

Learn More

Model Selection and Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)

Learn More

Phase 7: Deep Learning Basics

Understanding Neural Networks (Perceptron, Activation Functions)

Learn More

Building Neural Networks with TensorFlow and PyTorch

Learn More

Introduction to Backpropagation and Optimization Techniques

Learn More

Phase 8: Reinforcement Learning

What is Reinforcement Learning?

Learn More

Key Concepts (Agent, Environment, Rewards, Policy)

Learn More

Algorithms (Q-Learning, Deep Q-Networks)

Learn More

Applications of Reinforcement Learning

Learn More

Phase 9: Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stop Words)

Learn More

Sentiment Analysis and Text Classification

Learn More

Transformer Models (BERT, GPT)

Learn More

Phase 10: Computer Vision

Image Processing Basics

Learn More

Convolutional Neural Networks (CNNs)

Learn More

Object Detection and Recognition

Learn More

Advanced Topics (GANs, Style Transfer)

Learn More

Phase 11: Big Data and Scalability in ML

Working with Large Datasets (Hadoop, Spark)

Learn More

Distributed Training of ML Models

Learn More

Cloud Platforms for ML (AWS SageMaker, Google AI Platform)

Learn More

Phase 12: Model Deployment and Monitoring

Deployment Tools (Flask, FastAPI, Streamlit)

Learn More

Continuous Monitoring of Models

Learn More

Version Control and Model Updates

Learn More

Do You Have Questions ?

We’ll help you to grow your career and growth.

Do you need help with
anything?

Receive updates, hot deals, tutorials, discounts sent straignt in your inbox every month

Machine Learning Roadmap

The Complete Machine Learning Roadmap

Basic overview

What is Machine Learning?

Why Learn Machine Learning?

Estimated time commitment for the roadmap.

Phases of the Machine Learning Roadmap

Phase 1: Fundamentals of Machine Learning

Introduction to Machine Learning

Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

Understanding the ML Workflow (Data Collection, Preprocessing, Model Training, Evaluation, Deployment)

Phase 2: Prerequisites

Programming:

Mathematics:

Phase 3: Data Preprocessing and Cleaning

Understanding Data Types

Handling Missing and Erroneous Data

Feature Scaling and Encoding

Exploratory Data Analysis (EDA) Techniques

Phase 4: Supervised Learning

Regression Algorithms (Linear Regression, Polynomial Regression)

Classification Algorithms (Logistic Regression, Decision Trees, Random Forest, SVM)

Evaluation Metrics: MSE, RMSE, Accuracy, Precision, Recall, F1 Score

Phase 5: Unsupervised Learning

Clustering (K-Means, Hierarchical Clustering, DBSCAN)

Dimensionality Reduction (PCA, t-SNE)

Applications of Unsupervised Learning

Phase 6: Advanced Machine Learning Techniques

Ensemble Learning (Bagging, Boosting, Stacking)

Gradient Boosting Algorithms (XGBoost, LightGBM, CatBoost)

Model Selection and Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)

Phase 7: Deep Learning Basics

Understanding Neural Networks (Perceptron, Activation Functions)

Building Neural Networks with TensorFlow and PyTorch

Introduction to Backpropagation and Optimization Techniques

Phase 8: Reinforcement Learning

What is Reinforcement Learning?

Key Concepts (Agent, Environment, Rewards, Policy)

Algorithms (Q-Learning, Deep Q-Networks)

Applications of Reinforcement Learning

Phase 9: Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stop Words)

Sentiment Analysis and Text Classification

Transformer Models (BERT, GPT)

Phase 10: Computer Vision

Image Processing Basics

Convolutional Neural Networks (CNNs)

Object Detection and Recognition

Advanced Topics (GANs, Style Transfer)

Phase 11: Big Data and Scalability in ML

Working with Large Datasets (Hadoop, Spark)

Distributed Training of ML Models

Cloud Platforms for ML (AWS SageMaker, Google AI Platform)

Phase 12: Model Deployment and Monitoring

Deployment Tools (Flask, FastAPI, Streamlit)

Continuous Monitoring of Models

Version Control and Model Updates

Do You Have Questions ?

Courses List

Helpful Articles

Quick Links