Back to RoadmapsAI/ML Engineer
Comprehensive path to becoming a Machine Learning Engineer
10 milestones in this roadmap
Step 1beginner6-8 weeks
Python & Mathematics Foundations
Master Python programming and essential mathematics for ML including linear algebra, calculus, probability, and statistics
Curriculum
- 1Linear Algebra & Matrix Operations
- 2Multivariable Calculus & Gradients
- 3Probability Theory & Distributions
- 4Inferential Statistics & Hypothesis Testing
- 5Python Programming & Scientific Computing
Tools & Platforms
Python 3NumPySciPySymPyJupyter Notebookmatplotlib
Step 1beginner6-8 weeks
Python & Mathematics Foundations
Master Python programming and essential mathematics for ML including linear algebra, calculus, probability, and statistics
Curriculum
- 1Linear Algebra & Matrix Operations
- 2Multivariable Calculus & Gradients
- 3Probability Theory & Distributions
- 4Inferential Statistics & Hypothesis Testing
- 5Python Programming & Scientific Computing
Step 2beginner4-5 weeks
Data Manipulation & Analysis
Learn data wrangling, cleaning, and exploratory analysis with Pandas and NumPy
Curriculum
- 1Pandas DataFrames & Series Operations
- 2Data Cleaning & Missing Value Imputation
- 3Exploratory Data Analysis Techniques
- 4Statistical Visualization & Plotting
- 5
Step 3intermediate6-8 weeks
Machine Learning Fundamentals
Master supervised and unsupervised learning algorithms with rigorous model evaluation
Curriculum
- 1Supervised Learning: Regression & Classification
- 2Unsupervised Learning: Clustering & Dimensionality Reduction
- 3Cross-Validation & Model Selection
- 4Bias-Variance Tradeoff & Regularization
- 5
Step 4intermediate5-6 weeks
Advanced ML Algorithms
Master ensemble methods, gradient boosting frameworks, and SVMs for production-grade ML
Curriculum
- 1Ensemble Methods: Bagging & Boosting
- 2Gradient Boosting: XGBoost, LightGBM, CatBoost
- 3Support Vector Machines & Kernel Methods
- 4Model Interpretability & SHAP Values
- 5
Step 5intermediate6-8 weeks
Deep Learning Foundations
Build and train neural networks including CNNs and RNNs with PyTorch and TensorFlow
Curriculum
- 1Neural Network Architecture & Backpropagation
- 2Convolutional Neural Networks (CNNs)
- 3Recurrent Neural Networks, LSTMs & GRUs
- 4Optimization: SGD, Adam, Learning Rate Schedules
- 5
Step 6intermediate6-8 weeks
Natural Language Processing
Master NLP from tokenization and embeddings through transformers and BERT/GPT architectures
Curriculum
- 1Text Preprocessing & Tokenization Strategies
- 2Word Embeddings: Word2Vec, GloVe, FastText
- 3Transformer Architecture & Self-Attention
- 4BERT: Pretraining & Fine-tuning
- 5
Step 7advanced6-8 weeks
Computer Vision
Master image classification, object detection, and segmentation with modern architectures
Curriculum
- 1Image Classification & Transfer Learning
- 2Object Detection: YOLO, Faster R-CNN, DETR
- 3Semantic & Instance Segmentation
- 4Data Augmentation & Limited Label Strategies
- 5
Step 8advanced5-7 weeks
MLOps & Model Deployment
Deploy, monitor, and maintain ML models in production with robust MLOps practices
Curriculum
- 1Model Serialization & Serving Patterns
- 2A/B Testing & Shadow Deployments
- 3Data Drift & Concept Drift Monitoring
- 4ML-Specific CI/CD Pipelines
- 5
Step 9advanced6-8 weeks
Large Language Models & Generative AI
Build production LLM applications with RAG, fine-tuning, and prompt engineering
Curriculum
- 1Prompt Engineering: Chain-of-Thought, Few-Shot, ReAct
- 2Retrieval-Augmented Generation (RAG) Pipelines
- 3Fine-Tuning: LoRA, QLoRA, PEFT Methods
- 4RLHF, DPO & Alignment Techniques
- 5
Step 10advanced6-8 weeks
Research & Production Systems
Scale ML systems with distributed training, model optimization, and production architecture
Curriculum
- 1Experiment Tracking & Reproducibility
- 2Distributed Training: Data, Model & Pipeline Parallelism
- 3Model Optimization: Quantization, Pruning, Distillation
- 4ONNX Export & TensorRT Inference Optimization
- 5
Ready to start this journey?
Browse our courses and books to begin your learning path.
Python 3NumPySciPySymPyJupyter Notebookmatplotlib
Feature Scaling & Normalization
Tools & Platforms
PandasNumPyMatplotlibSeabornPlotlyJupyter Lab
Step 2beginner4-5 weeks
Data Manipulation & Analysis
Learn data wrangling, cleaning, and exploratory analysis with Pandas and NumPy
Curriculum
- 1Pandas DataFrames & Series Operations
- 2Data Cleaning & Missing Value Imputation
- 3Exploratory Data Analysis Techniques
- 4Statistical Visualization & Plotting
- 5Feature Scaling & Normalization
Tools & Platforms
PandasNumPyMatplotlibSeabornPlotlyJupyter Lab
Evaluation Metrics & Confusion Matrices
Tools & Platforms
scikit-learnNumPyPandasMatplotlibJupyter Notebook
Step 3intermediate6-8 weeks
Machine Learning Fundamentals
Master supervised and unsupervised learning algorithms with rigorous model evaluation
Curriculum
- 1Supervised Learning: Regression & Classification
- 2Unsupervised Learning: Clustering & Dimensionality Reduction
- 3Cross-Validation & Model Selection
- 4Bias-Variance Tradeoff & Regularization
- 5Evaluation Metrics & Confusion Matrices
Tools & Platforms
scikit-learnNumPyPandasMatplotlibJupyter Notebook
Bayesian Hyperparameter Optimization
Tools & Platforms
XGBoostLightGBMCatBoostscikit-learnSHAPOptuna
Step 4intermediate5-6 weeks
Advanced ML Algorithms
Master ensemble methods, gradient boosting frameworks, and SVMs for production-grade ML
Curriculum
- 1Ensemble Methods: Bagging & Boosting
- 2Gradient Boosting: XGBoost, LightGBM, CatBoost
- 3Support Vector Machines & Kernel Methods
- 4Model Interpretability & SHAP Values
- 5Bayesian Hyperparameter Optimization
Tools & Platforms
XGBoostLightGBMCatBoostscikit-learnSHAPOptuna
Regularization: Dropout, Batch Normalization, Weight Decay
Tools & Platforms
PyTorchTensorFlowKerastorchvisionTensorBoardCUDA
Step 5intermediate6-8 weeks
Deep Learning Foundations
Build and train neural networks including CNNs and RNNs with PyTorch and TensorFlow
Curriculum
- 1Neural Network Architecture & Backpropagation
- 2Convolutional Neural Networks (CNNs)
- 3Recurrent Neural Networks, LSTMs & GRUs
- 4Optimization: SGD, Adam, Learning Rate Schedules
- 5Regularization: Dropout, Batch Normalization, Weight Decay
Tools & Platforms
PyTorchTensorFlowKerastorchvisionTensorBoardCUDA
GPT Architecture & Autoregressive Language Models
Tools & Platforms
Hugging Face TransformersspaCyNLTKtokenizersPyTorchSentence-Transformers
Step 6intermediate6-8 weeks
Natural Language Processing
Master NLP from tokenization and embeddings through transformers and BERT/GPT architectures
Curriculum
- 1Text Preprocessing & Tokenization Strategies
- 2Word Embeddings: Word2Vec, GloVe, FastText
- 3Transformer Architecture & Self-Attention
- 4BERT: Pretraining & Fine-tuning
- 5GPT Architecture & Autoregressive Language Models
Tools & Platforms
Hugging Face TransformersspaCyNLTKtokenizersPyTorchSentence-Transformers
Vision Transformers & Modern Architectures
Tools & Platforms
PyTorchtorchvisionOpenCVUltralytics YOLOv8AlbumentationsRoboflow
Step 7advanced6-8 weeks
Computer Vision
Master image classification, object detection, and segmentation with modern architectures
Curriculum
- 1Image Classification & Transfer Learning
- 2Object Detection: YOLO, Faster R-CNN, DETR
- 3Semantic & Instance Segmentation
- 4Data Augmentation & Limited Label Strategies
- 5Vision Transformers & Modern Architectures
Tools & Platforms
PyTorchtorchvisionOpenCVUltralytics YOLOv8AlbumentationsRoboflow
Model Registry & Version Management
Tools & Platforms
MLflowKubeflowDockerFastAPISeldon CoreBentoMLEvidently
Step 8advanced5-7 weeks
MLOps & Model Deployment
Deploy, monitor, and maintain ML models in production with robust MLOps practices
Curriculum
- 1Model Serialization & Serving Patterns
- 2A/B Testing & Shadow Deployments
- 3Data Drift & Concept Drift Monitoring
- 4ML-Specific CI/CD Pipelines
- 5Model Registry & Version Management
Tools & Platforms
MLflowKubeflowDockerFastAPISeldon CoreBentoMLEvidently
Vector Databases & Embedding Strategies
Tools & Platforms
OpenAI APILangChainLlamaIndexPineconeChromaDBHugging Face PEFTvLLM
Step 9advanced6-8 weeks
Large Language Models & Generative AI
Build production LLM applications with RAG, fine-tuning, and prompt engineering
Curriculum
- 1Prompt Engineering: Chain-of-Thought, Few-Shot, ReAct
- 2Retrieval-Augmented Generation (RAG) Pipelines
- 3Fine-Tuning: LoRA, QLoRA, PEFT Methods
- 4RLHF, DPO & Alignment Techniques
- 5Vector Databases & Embedding Strategies
Tools & Platforms
OpenAI APILangChainLlamaIndexPineconeChromaDBHugging Face PEFTvLLM
End-to-End ML System Design & Architecture
Tools & Platforms
Weights & BiasesDeepSpeedONNX RuntimeTensorRTRayHorovodNVIDIA Triton
Step 10advanced6-8 weeks
Research & Production Systems
Scale ML systems with distributed training, model optimization, and production architecture
Curriculum
- 1Experiment Tracking & Reproducibility
- 2Distributed Training: Data, Model & Pipeline Parallelism
- 3Model Optimization: Quantization, Pruning, Distillation
- 4ONNX Export & TensorRT Inference Optimization
- 5End-to-End ML System Design & Architecture
Tools & Platforms
Weights & BiasesDeepSpeedONNX RuntimeTensorRTRayHorovodNVIDIA Triton