Machine Learning

Learn Machine Learning from Fundamentals to Production

Machine Learning is the foundation of many modern AI systems.

It enables computers to learn patterns from data and use those patterns to make predictions, classifications, recommendations, and decisions without explicitly programming every rule.

At GeekyCodes, this Machine Learning learning path takes you from ML fundamentals and classical algorithms to deep learning, NLP, Transformers, MLOps, and ML system design.

The focus is on understanding how algorithms work, when to use them, how to evaluate them, and how to build machine learning systems that work in real-world environments.

[Start Learning Machine Learning →]

What Is Machine Learning?

Machine Learning is a branch of artificial intelligence where algorithms learn patterns from data and use those patterns to make predictions or decisions.

A typical machine learning workflow looks like:

Data
 ↓
Data Cleaning
 ↓
Exploratory Data Analysis
 ↓
Feature Engineering
 ↓
Model Selection
 ↓
Model Training
 ↓
Model Evaluation
 ↓
Hyperparameter Tuning
 ↓
Deployment
 ↓
Monitoring

Understanding this complete lifecycle is just as important as understanding individual algorithms.

Machine Learning Learning Path

Follow the topics in this order if you’re learning Machine Learning from the beginning:

Machine Learning Fundamentals
          ↓
Statistics & Probability
          ↓
Data Preprocessing
          ↓
Feature Engineering
          ↓
Supervised Learning
          ↓
Unsupervised Learning
          ↓
Model Evaluation
          ↓
Ensemble Learning
          ↓
Deep Learning
          ↓
Natural Language Processing
          ↓
Transformers
          ↓
Time Series
          ↓
MLOps
          ↓
ML System Design

1. Machine Learning Fundamentals

Start with the concepts that form the foundation of Machine Learning.

Topics

• What is Machine Learning?
• AI vs ML vs Deep Learning
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning
• Training Data vs Test Data
• Features and Labels
• Parameters vs Hyperparameters
• Bias and Variance
• Overfitting and Underfitting
• Model Generalization

[Explore Machine Learning Fundamentals →]

2. Statistics & Probability for Machine Learning

Machine Learning relies heavily on statistics and probability.

Learn the mathematical concepts needed to understand and build ML models.

Topics

• Mean, Median and Mode
• Variance and Standard Deviation
• Probability
• Conditional Probability
• Bayes’ Theorem
• Probability Distributions
• Normal Distribution
• Central Limit Theorem
• Sampling
• Hypothesis Testing
• Confidence Intervals
• Correlation
• Covariance

[Explore Statistics for Machine Learning →]

3. Data Preprocessing

Real-world datasets are rarely ready to use directly.

Learn how to transform raw data into a format suitable for machine learning models.

Topics

• Handling Missing Values
• Handling Outliers
• Encoding Categorical Variables
• Feature Scaling
• Normalization
• Standardization
• Train-Test Split
• Data Leakage
• Imbalanced Datasets
• Feature Selection

[Explore Data Preprocessing →]

4. Feature Engineering

Good features can have a significant impact on model performance.

Learn how to transform raw data into meaningful features that help machine learning models identify useful patterns.

Topics

• What is Feature Engineering?
• Numerical Features
• Categorical Features
• Date and Time Features
• Text Features
• Aggregation Features
• Feature Selection
• Dimensionality Reduction
• PCA
• Feature Transformation
• Feature Interaction

[Explore Feature Engineering →]

5. Supervised Learning

Supervised learning uses labeled data to learn a relationship between inputs and outputs.

Regression

Learn how to predict continuous values.

• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net
• Regression Evaluation

Classification

Learn how to predict categories.

• Logistic Regression
• K-Nearest Neighbors
• Naive Bayes
• Decision Trees
• Support Vector Machines

[Explore Supervised Learning →]

6. Tree-Based & Ensemble Models

Ensemble learning combines multiple models to improve prediction performance and robustness.

Topics

• Decision Trees
• Random Forest
• Bagging
• Boosting
• AdaBoost
• Gradient Boosting
• XGBoost
• LightGBM
• CatBoost
• Random Forest vs Gradient Boosting
• XGBoost vs Random Forest

[Explore Ensemble Learning →]

7. Unsupervised Learning

Unsupervised learning discovers patterns and structures in data without labeled target variables.

Clustering

• K-Means
• Hierarchical Clustering
• DBSCAN
• Gaussian Mixture Models

Dimensionality Reduction

• PCA
• t-SNE
• UMAP

Other Techniques

• Anomaly Detection
• Association Rules
• Customer Segmentation

[Explore Unsupervised Learning →]

8. Model Evaluation

A model isn’t useful simply because it produces predictions.

You need reliable evaluation techniques to understand whether the model generalizes to unseen data.

Classification Metrics

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• PR-AUC
• Confusion Matrix

Regression Metrics

• MAE
• MSE
• RMSE
• R²
• MAPE

Model Validation

• Cross-Validation
• Stratified Cross-Validation
• Hyperparameter Tuning
• Grid Search
• Random Search
• Model Selection

[Explore Model Evaluation →]

9. Explainable AI

Machine learning models can make powerful predictions, but understanding why a model made a prediction is often equally important.

Learn about:

• Model Interpretability
• Feature Importance
• SHAP
• LIME
• Partial Dependence
• Global vs Local Explanations
• Explainability for Tree Models
• Explainability for Neural Networks

[Explore Explainable AI →]

10. Deep Learning

Deep Learning uses neural networks with multiple layers to learn increasingly complex representations from data.

Topics

• What Are Neural Networks?
• Perceptrons
• Activation Functions
• Forward Propagation
• Backpropagation
• Gradient Descent
• Loss Functions
• Optimizers
• Batch Normalization
• Dropout
• Regularization
• CNNs
• RNNs
• LSTMs
• GRUs

[Explore Deep Learning →]

11. Natural Language Processing

Natural Language Processing enables machines to understand and process human language.

Learn the evolution from traditional NLP techniques to modern neural language models.

Topics

• Text Preprocessing
• Tokenization
• Stop Words
• Stemming
• Lemmatization
• Bag of Words
• TF-IDF
• Word Embeddings
• Word2Vec
• GloVe
• Sequence Models
• RNNs
• LSTMs
• Attention

[Explore NLP →]

12. Transformers

Transformers fundamentally changed modern NLP and became the foundation for today’s large language models.

Learn:

• What Are Transformers?
• Self-Attention
• Query, Key and Value
• Multi-Head Attention
• Positional Encoding
• Encoder and Decoder
• Transformer Architecture
• BERT
• GPT
• T5
• Vision Transformers
• Transformers vs RNNs
• Transformers vs LSTMs

[Explore Transformers →]

13. Time Series Forecasting

Time series problems require models that understand temporal patterns, trends, seasonality, and dependencies.

Topics

• Time Series Fundamentals
• Trend
• Seasonality
• Stationarity
• Autocorrelation
• ACF and PACF
• ARIMA
• SARIMA
• Exponential Smoothing
• Prophet
• LSTM for Time Series
• Transformer-Based Forecasting
• Time Series Evaluation

[Explore Time Series →]

14. Model Deployment & MLOps

Training a model is only one part of the machine learning lifecycle.

Production systems need reliable deployment, monitoring, versioning, and retraining.

Topics

• Model Serialization
• REST APIs
• FastAPI
• Docker
• Model Versioning
• Experiment Tracking
• MLflow
• CI/CD
• Model Monitoring
• Data Drift
• Concept Drift
• Model Retraining
• Feature Stores
• Kubernetes
• Cloud ML Deployment

[Explore MLOps →]

15. Machine Learning System Design

Building a model in a notebook is different from designing a machine learning system that serves millions of predictions.

Learn how to design systems for:

• Recommendation Systems
• Fraud Detection
• Customer Churn Prediction
• Search Ranking
• Ad Click Prediction
• Demand Forecasting
• Real-Time Prediction
• Batch Prediction
• Feature Engineering Pipelines
• Model Serving
• Monitoring
• ML Infrastructure

A typical production ML system looks like:

                 Raw Data
                    │
                    ▼
              Data Pipeline
                    │
                    ▼
             Feature Pipeline
                    │
                    ▼
               Feature Store
                    │
                    ▼
              Model Training
                    │
                    ▼
             Model Registry
                    │
                    ▼
              Model Serving
                    │
                    ▼
               Predictions
                    │
          ┌─────────┴─────────┐
          ▼                   ▼
      Monitoring          Feedback
                              │
                              ▼
                         Retraining

[Explore ML System Design →]

🛠️ Machine Learning Projects

The best way to understand Machine Learning is to build complete projects.

Beginner Projects

• House Price Prediction
• Customer Churn Prediction
• Spam Classification
• Customer Segmentation
• Sales Prediction

Intermediate Projects

• Credit Risk Prediction
• Fraud Detection
• Recommendation System
• Demand Forecasting
• Sentiment Analysis

Advanced Projects

• Real-Time Fraud Detection
• Production Recommendation System
• End-to-End ML Platform
• NLP Classification Pipeline
• Time Series Forecasting System
• ML Monitoring Platform

[Explore Machine Learning Projects →]

🎯 Machine Learning Interview Preparation

Preparing for a Data Scientist, Machine Learning Engineer, AI Engineer, or Data Science interview?

Explore questions covering:

• Machine Learning Fundamentals
• Statistics
• Probability
• Feature Engineering
• Model Evaluation
• Classical ML Algorithms
• Deep Learning
• NLP
• Transformers
• Time Series
• MLOps
• ML System Design
• Python
• SQL

Popular Interview Resources

[Machine Learning Interview Questions →]

[Statistics Interview Questions →]

[Deep Learning Interview Questions →]

[ML System Design Questions →]

📚 Machine Learning Roadmap

If you’re starting from the beginning, follow this path:

                    Python
                       │
                       ▼
              Statistics & Probability
                       │
                       ▼
              Machine Learning Basics
                       │
                       ▼
              Data Preprocessing
                       │
                       ▼
              Feature Engineering
                       │
              ┌────────┴────────┐
              ▼                 ▼
        Supervised         Unsupervised
        Learning             Learning
              │                 │
              └────────┬────────┘
                       ▼
                Model Evaluation
                       │
                       ▼
                Ensemble Models
                       │
                       ▼
                 Deep Learning
                       │
              ┌────────┴────────┐
              ▼                 ▼
             NLP          Computer Vision
              │
              ▼
         Transformers
              │
              ▼
          Time Series
              │
              ▼
             MLOps
              │
              ▼
       ML System Design

[Follow the Complete Machine Learning Roadmap →]

🔗 Explore Related Learning Paths

🤖 AI Engineering

Learn about LLMs, RAG, embeddings, AI agents, LangGraph, fine-tuning, and production AI.

[Explore AI Engineering →]

🏗️ Data Engineering

Learn Python, SQL, Apache Spark, Delta Lake, Databricks, Airflow, Snowflake, and modern data platforms.

[Explore Data Engineering →]

💼 Interview Preparation

Prepare for Data Scientist, Data Engineer, ML Engineer, and AI Engineer interviews.

[Explore Interview Preparation →]

🛠️ Projects

Build complete machine learning, AI, and data engineering systems.

[Explore Projects →]

Start Learning Machine Learning

Machine Learning is much more than memorizing algorithms.

The goal is to understand why an algorithm works, when to use it, how to evaluate it, and how to turn a trained model into a reliable production system.

Follow the GeekyCodes Machine Learning learning path to progress from ML fundamentals → classical algorithms → deep learning → NLP → Transformers → MLOps → ML system design.

Learn the algorithm. Understand the intuition. Build the system.

[Start with Machine Learning Fundamentals →]