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Machine Learning Engineer Skills Checklist, Schemes and Mind Maps of Machine Learning

Kurukshetra UniversityMachine Learning

A comprehensive checklist for aspiring machine learning engineers, covering essential skills and knowledge across various phases, from python basics and data analysis to deep learning, mlops, and deployment strategies. It includes key topics such as data structures and algorithms, machine learning algorithms, deep learning architectures, and deployment tools like flask, docker, and cloud platforms. The checklist also suggests final ml project ideas and deployment stack recommendations, making it a valuable resource for structuring learning and career preparation. It also provides guidance on resume and interview preparation, including project demos, key skills, and relevant resources.

Typology: Schemes and Mind Maps

2023/2024

Available from 08/12/2025

vanshu-sharma
vanshu-sharma 🇮🇳

4 documents

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Final ML Engineer Checklist
Phase 1: Python Basics
- Variables, Data Types, Operators, Conditional Statements
- Loops, Functions, Recursion
- Lists, Tuples, Dictionary, Sets
- File Handling, Exception Handling, pip & modules
- OOPs, Lambda, Map/Filter/Reduce
- List Comprehensions, Virtual Environments
Phase 2: DSA + Problem Solving
- Arrays, Strings, Hashing, Two pointers
- Stacks, Queues, Linked Lists
- Trees, Graphs, DFS/BFS, Hash Maps, Tries
- Recursion, Backtracking, DP, Sorting
- Complexity Analysis, Sliding Windows
- Practice: LeetCode, GeeksForGeeks (300+ Problems)
Phase 3: Data Analysis with Python
- NumPy arrays, indexing, broadcasting, math ops
- pandas for DataFrames, cleaning
- Matplotlib & Seaborn for data visualization
Phase 4: Machine Learning
- ML basics: Supervised/Unsupervised, ML pipelines
- Algorithms: Linear/Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, Random Forest
- Clustering: KMeans, DBSCAN, PCA (Dimensionality Reduction)
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Download Machine Learning Engineer Skills Checklist and more Schemes and Mind Maps Machine Learning in PDF only on Docsity!

Phase 1: Python Basics

  • Variables, Data Types, Operators, Conditional Statements
  • Loops, Functions, Recursion
  • Lists, Tuples, Dictionary, Sets
  • File Handling, Exception Handling, pip & modules
  • OOPs, Lambda, Map/Filter/Reduce
  • List Comprehensions, Virtual Environments

Phase 2: DSA + Problem Solving

  • Arrays, Strings, Hashing, Two pointers
  • Stacks, Queues, Linked Lists
  • Trees, Graphs, DFS/BFS, Hash Maps, Tries
  • Recursion, Backtracking, DP, Sorting
  • Complexity Analysis, Sliding Windows
  • Practice: LeetCode, GeeksForGeeks (300+ Problems)

Phase 3: Data Analysis with Python

  • NumPy arrays, indexing, broadcasting, math ops
  • pandas for DataFrames, cleaning
  • Matplotlib & Seaborn for data visualization

Phase 4: Machine Learning

  • ML basics: Supervised/Unsupervised, ML pipelines
  • Algorithms: Linear/Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, Random Forest
  • Clustering: KMeans, DBSCAN, PCA (Dimensionality Reduction)
  • Evaluation Metrics: RMSE, Accuracy, Recall, Precision, F1-Score
  • Cross-validation, Train-Test Split, GridSearchCV
  • Math Foundations: Probability, Statistics, Linear Algebra, Calculus, Optimization
  • Additional: Feature Engineering, Model Interpretability (SHAP, LIME), XGBoost/LightGBM/CatBoost

Phase 5: Deep Learning + NLP

  • PyTorch basics, ANN, CNN, RNN, LSTM
  • Activation functions, Loss Functions
  • Projects: MNIST, CIFAR10 Image Classification
  • NLP: Tokenization, Word Embeddings, Attention
  • Transformer, BERT basics, GPT basics, GAN
  • Time Series Forecasting
  • Libraries: HuggingFace Transformers, Streamlit

Phase 6: MLOps & Deployment

  • Model Deployment: Flask, FastAPI, Docker
  • UI: Streamlit, Gradio
  • CI/CD: GitHub Actions, MLflow for tracking
  • Model Monitoring: Drift detection, Prometheus + Grafana
  • Model Registry: DVC, MLflow
  • Cloud: AWS, GCP, Azure basics for model hosting

Phase 7: Databases & Big Data

  • SQL: Joins, Aggregation, Subqueries
  • NoSQL: MongoDB basics
  • Hadoop, Spark (basics, optional for ML Engineers)