Complete Machine Learning Roadmap

Deepshika

Complete Machine Learning Roadmap👇

1. Introduction to Machine Learning

    • Definition
    • Purpose
    • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

    2. Mathematics for Machine Learning

      • Linear Algebra
      • Calculus
      • Statistics and Probability

      3. Programming Languages for ML

        • Python and Libraries (NumPy, Pandas, Matplotlib)
        • R

        4. Data Preprocessing

          • Handling Missing Data
          • Feature Scaling
          • Data Transformation

          5. Exploratory Data Analysis (EDA)

            • Data Visualization
            • Descriptive Statistics

            6. Supervised Learning

              • Regression
              • Classification
              • Model Evaluation

              7. Unsupervised Learning

                • Clustering (K-Means, Hierarchical)
                • Dimensionality Reduction (PCA)

                8. Model Selection and Evaluation

                  • Cross-Validation
                  • Hyperparameter Tuning
                  • Evaluation Metrics (Precision, Recall, F1 Score)

                  9. Ensemble Learning

                    • Random Forest
                    • Gradient Boosting

                    10. Neural Networks and Deep Learning

                    • Introduction to Neural Networks
                    • Building and Training Neural Networks
                    • Convolutional Neural Networks (CNN)
                    • Recurrent Neural Networks (RNN)

                    11. Natural Language Processing (NLP)

                    • Text Preprocessing
                    • Sentiment Analysis
                    • Named Entity Recognition (NER)

                    12. Reinforcement Learning

                    • Basics
                    • Markov Decision Processes
                    • Q-Learning

                    13. Machine Learning Frameworks

                    • TensorFlow
                    • PyTorch
                    • Scikit-Learn

                    14. Deployment of ML Models

                    • Flask for Web Deployment
                    • Docker and Kubernetes

                    15. Ethical and Responsible AI

                    • Bias and Fairness
                    • Ethical Considerations

                    16. Machine Learning in Production

                    • Model Monitoring
                    • Continuous Integration/Continuous Deployment (CI/CD)

                    17. Real-world Projects and Case Studies

                    18. Machine Learning Resources