Machine Learning Roadmap 2025 | Machine Learning Engineer Roadmap For Beginners | Simplilearn
0 likes287 views
The document outlines a comprehensive 11-month curriculum focused on programming and machine learning. It includes foundational topics like Python and data structures, progressing through mathematics, data handling, machine learning fundamentals, and advanced concepts. The program culminates in model deployment and cloud computing strategies.
More Related Content
Similar to Machine Learning Roadmap 2025 | Machine Learning Engineer Roadmap For Beginners | Simplilearn (20)
More from Simplilearn (20)
![[Medicalstudyzone.com] 1. AIIMS NOV EMBER 2015 SOLVED PAPER.pdf](https://cdn.slidesharecdn.com/ss_thumbnails/medicalstudyzone-250904212000-ef4cbf83-thumbnail.jpg?width=560&fit=bounds)
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