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Machine Learning Roadmap 2025 | Machine Learning Engineer Roadmap For Beginners | Simplilearn
Month 1: Foundations – Programming
Variables, Data types (like numbers, strings, lists),
Loops and Functions
Learn Python Basics
Month 1: Foundations – Programming
Numpy, Pandas, Scikit-Learn
Explore Key Libraries
Month 2 : Version Control and Data Structures
Committing changes, Branching, Merging
Version Control with Git
Month 2 : Version Control and Data Structures
Arrays and Lists, Stacks and Queues, Sorting and
Searching Algorithms
Data Structures and Algorithms
Month 3 : Access Data with SQL
SELECT and WHERE, JOIN, GROUP BY and Aggregate
Functions
SQL
Month 4: Mathematics
Linear Algebra, Calculus, Probability & Statistics
Math
Month 5: Data Handling and Visualization
Removing Missing Values, Transforming Variables,
Encoding Categorical Data
Data Manipulation
Month 5: Data Handling and Visualization
Matplotlib and Seaborn
Data Visualization
Month 6: Machine Learning Fundamentals
Linear Regression, Decision Trees, Support Vector
Machines
Supervised Learning
Month 6: Machine Learning Fundamentals
Clustering, Dimensionality Reduction
Unsupervised Learning
Month 7: Building and Training Models with
Advanced Libraries
Tensorflow and Pytorch, Model Training and Evaluation
Advanced Tools
Month 8: Advanced Machine Learning
Bagging (e.g., Random Forests, Boosting (e.g.,
AdaBoost, XGBoost)
Ensemble Learning
Month 8: Advanced Machine Learning
Neural Network Basics, Backpropagation and
Gradient Descent
Deep Learning
Month 9: Specialized Topics
Neural Network Basics, Backpropagation and
Gradient Descent
NLP and Computer Vision
Month 10: Model Deployment
Flask or Django, Docker
Deployment
Month 11: Cloud and Production
Cloud Platforms (AWS, Google Cloud, or Azure),
Monitoring and Maintenance
Deploy Models on Cloud

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Machine Learning Roadmap 2025 | Machine Learning Engineer Roadmap For Beginners | Simplilearn

  • 2. Month 1: Foundations – Programming Variables, Data types (like numbers, strings, lists), Loops and Functions Learn Python Basics
  • 3. Month 1: Foundations – Programming Numpy, Pandas, Scikit-Learn Explore Key Libraries
  • 4. Month 2 : Version Control and Data Structures Committing changes, Branching, Merging Version Control with Git
  • 5. Month 2 : Version Control and Data Structures Arrays and Lists, Stacks and Queues, Sorting and Searching Algorithms Data Structures and Algorithms
  • 6. Month 3 : Access Data with SQL SELECT and WHERE, JOIN, GROUP BY and Aggregate Functions SQL
  • 7. Month 4: Mathematics Linear Algebra, Calculus, Probability & Statistics Math
  • 8. Month 5: Data Handling and Visualization Removing Missing Values, Transforming Variables, Encoding Categorical Data Data Manipulation
  • 9. Month 5: Data Handling and Visualization Matplotlib and Seaborn Data Visualization
  • 10. Month 6: Machine Learning Fundamentals Linear Regression, Decision Trees, Support Vector Machines Supervised Learning
  • 11. Month 6: Machine Learning Fundamentals Clustering, Dimensionality Reduction Unsupervised Learning
  • 12. Month 7: Building and Training Models with Advanced Libraries Tensorflow and Pytorch, Model Training and Evaluation Advanced Tools
  • 13. Month 8: Advanced Machine Learning Bagging (e.g., Random Forests, Boosting (e.g., AdaBoost, XGBoost) Ensemble Learning
  • 14. Month 8: Advanced Machine Learning Neural Network Basics, Backpropagation and Gradient Descent Deep Learning
  • 15. Month 9: Specialized Topics Neural Network Basics, Backpropagation and Gradient Descent NLP and Computer Vision
  • 16. Month 10: Model Deployment Flask or Django, Docker Deployment
  • 17. Month 11: Cloud and Production Cloud Platforms (AWS, Google Cloud, or Azure), Monitoring and Maintenance Deploy Models on Cloud