Machine Learning Journey Starts Here

From “What’s a Model?” to “Let’s Deploy It!”

Your Machine Learning Journey Starts Here

Machine Learning can feel overwhelming at first—algorithms, math, tools, deployment.

This structured learning roadmap breaks ML into clear, progressive stages, guiding you from fundamentals to real-world deployment with confidence.

Beginner Level — Build the Foundation

What is Machine Learning?

Understanding the core idea and real-world use cases

Types of Machine Learning

Supervised, Unsupervised & Reinforcement Learning

Key ML Terminologies

Features, labels, training, testing, inference

Data Preprocessing Essentials

Handling missing values, encoding categorical data, feature scaling

Train–Test Split & Cross-Validation

Building reliable and unbiased models

Model Evaluation Metrics

Accuracy, precision, recall, F1-score

Overfitting vs Underfitting

Bias–variance tradeoff and model generalization

Intermediate Level — Learn the Core Algorithms

Linear & Logistic Regression

Decision Trees & Random Forests

K-Nearest Neighbors (KNN)

Support Vector Machines (SVM)

Naive Bayes Algorithm

Clustering Techniques

K-Means, Hierarchical clustering

Dimensionality Reduction

PCA, t-SNE

Advanced Level — Real-World & Production ML

Ensemble Learning

Bagging, Boosting, XGBoost

Deep Learning Fundamentals

ANN, CNN, RNN basics

Natural Language Processing (NLP)

Text preprocessing, TF-IDF, word embeddings

Time Series Forecasting

ARIMA, LSTM

Model Deployment

Flask, Streamlit, FastAPI

Hyperparameter Tuning

GridSearch, RandomizedSearch

Real-World ML Projects

End-to-end problem solving

Top Datasets for Practice

Hands-on learning with real data

Common ML Mistakes & Best Practices

Lessons learned from real implementations

Free & Essential Tools

Google Colab, Scikit-learn, TensorFlow, PyTorch