Artificial Intelligence
+70% demand
AI/Machine Learning Engineer
Design intelligent systems and train machine learning models to automate decision-making processes.
6-24 months
4.9/5 rating
9 Phases
Start Learning Path
+70%
Python
TensorFlow
PyTorch
Keras
Scikit-learn
Skills & Technologies
Python
TensorFlow
PyTorch
Keras
Scikit-learn
Prompt Engineering
Reinforcement Learning
MLOps
Transformers
NLP
AI/Machine Learning Engineer Roadmap
Phase 1: Programming & Math Foundations
1.5 months
Topics Covered:
- Python basics and libraries (NumPy, Pandas, Matplotlib)
- Linear Algebra (vectors, matrices, eigenvalues)
- Calculus (derivatives, gradients)
- Probability & Statistics
- Basic data preprocessing and cleaning
Phase 2: Machine Learning Fundamentals
2 months
Topics Covered:
- Supervised vs Unsupervised Learning
- Regression, Classification, Clustering
- Evaluation metrics (Accuracy, Precision, Recall, F1)
- Scikit-learn workflows
- Model validation, bias-variance tradeoff
Hands-on Projects:
- Build a house price prediction model using Scikit-learn
Phase 3: Deep Learning Essentials
2 months
Topics Covered:
- Neural Networks & Backpropagation
- Activation functions and optimizers
- Keras and TensorFlow basics
- Convolutional Neural Networks (CNNs)
- Hyperparameter tuning and regularization
Hands-on Projects:
- Image classification with CNN (e.g., CIFAR-10 or MNIST)
Phase 4: PyTorch & Advanced Neural Architectures
1.5 months
Topics Covered:
- PyTorch fundamentals and custom models
- Transfer Learning (ResNet, VGG, etc.)
- Recurrent Neural Networks (RNNs)
- LSTM/GRU for sequential data
- GANs (Generative Adversarial Networks)
Hands-on Projects:
- Train an image generator using GANs
Phase 5: Natural Language Processing (NLP)
1.5 months
Topics Covered:
- Text preprocessing (Tokenization, Stemming, Lemmatization)
- TF-IDF and Word Embeddings (Word2Vec, GloVe)
- Text Classification and Sentiment Analysis
- Named Entity Recognition (NER)
- Hugging Face Transformers (BERT, GPT)
Hands-on Projects:
- Build a sentiment analysis or chatbot app
Phase 6: Transformers & Prompt Engineering
1 month
Topics Covered:
- Transformer architecture explained
- Using pre-trained models (BERT, GPT, T5)
- Fine-tuning transformers on custom data
- Intro to Prompt Engineering
- Zero-shot and Few-shot learning
Hands-on Projects:
- Build a question-answering or summarization tool with HuggingFace
Phase 7: Reinforcement Learning (RL)
1.5 months
Topics Covered:
- Markov Decision Process (MDP)
- Exploration vs Exploitation
- Q-Learning and Deep Q Networks (DQN)
- Policy Gradients
- OpenAI Gym
Hands-on Projects:
- Train an agent to play a simple game using RL
Phase 8: MLOps & Model Deployment
1.5 months
Topics Covered:
- Version Control for ML (DVC, Git)
- Model Tracking (MLflow, Weights & Biases)
- Containerization with Docker
- APIs with FastAPI/Flask
- CI/CD for ML pipelines (Airflow, Kubeflow basics)
Hands-on Projects:
- Deploy an ML model using FastAPI + Docker
Phase 9: Capstone AI/ML Project
2 months
Topics Covered:
Hands-on Projects:
- End-to-end AI project (data pipeline → model → deployment)
- Use advanced NLP or CV models
- Track metrics, visualize training
- Deploy on cloud (GCP, AWS, or Hugging Face Spaces)
- Document the project and share on GitHub
Tools & Resources
Python
TensorFlow
PyTorch
Keras
Scikit-learn
Hugging Face Transformers
OpenAI Gym
MLflow
Weights & Biases
FastAPI
Docker
Google Colab
VS Code
