Skip to content

AI Engineer 2025: Learning Roadmap

1. Foundations of AI

  • 1.1 What is Artificial Intelligence?
  • 1.2 AI vs ML vs DL: Understanding the Differences
  • 1.3 Applications of AI in Industry
  • 1.4 History and Evolution of AI

2. Essential Math for AI

  • 2.1 Linear Algebra (Vectors, Matrices, Operations)
  • 2.2 Calculus (Derivatives, Gradients)
  • 2.3 Probability & Statistics (Distributions, Bayes’ Theorem)
  • 2.4 Optimization Techniques (Gradient Descent, Cost Functions)

3. Programming for AI

  • 3.1 Python Programming Basics
  • 3.2 Object-Oriented Programming
  • 3.3 Working with NumPy and Pandas
  • 3.4 Data Visualization (Matplotlib, Seaborn)
  • 3.5 Jupyter Notebooks & Google Colab

4. Machine Learning (ML)

  • 4.1 Supervised Learning (Regression, Classification)
  • 4.2 Unsupervised Learning (Clustering, Dimensionality Reduction)
  • 4.3 Ensemble Methods (Random Forests, Gradient Boosting)
  • 4.4 Model Evaluation (Confusion Matrix, ROC, F1 Score)
  • 4.5 Cross-validation & Hyperparameter Tuning
  • 4.6 Feature Engineering & Selection

5. Deep Learning

  • 5.1 Neural Networks Basics (Perceptron, MLP)
  • 5.2 Activation Functions
  • 5.3 Backpropagation and Gradient Descent
  • 5.4 Convolutional Neural Networks (CNNs)
  • 5.5 Recurrent Neural Networks (RNNs, LSTMs)
  • 5.6 Transformers and Attention Mechanism
  • 5.7 Generative Models (GANs, VAEs)

6. Natural Language Processing (NLP)

  • 6.1 Text Preprocessing (Tokenization, Lemmatization)
  • 6.2 Word Embeddings (Word2Vec, GloVe, FastText)
  • 6.3 Sequence Modeling (RNNs, LSTMs)
  • 6.4 Transformers (BERT, GPT, T5)
  • 6.5 Text Classification, Named Entity Recognition (NER)
  • 6.6 Chatbots and Language Generation

7. Computer Vision

  • 7.1 Image Preprocessing Techniques
  • 7.2 CNN Architectures (VGG, ResNet, EfficientNet)
  • 7.3 Object Detection (YOLO, SSD, Faster R-CNN)
  • 7.4 Image Segmentation (U-Net, Mask R-CNN)
  • 7.5 Face Recognition, OCR

8. Data Engineering for AI

  • 8.1 Data Collection & Pipelines
  • 8.2 Data Cleaning and Imputation
  • 8.3 Feature Stores and Data Versioning
  • 8.4 Big Data Tools (Spark, Hadoop basics)

9. Model Deployment & MLOps

  • 9.1 Model Serialization (Pickle, ONNX, TorchScript)
  • 9.2 REST APIs for ML Models (Flask, FastAPI)
  • 9.3 Model Serving (TensorFlow Serving, TorchServe)
  • 9.4 Docker for AI Applications
  • 9.5 CI/CD for ML (GitHub Actions, Jenkins)
  • 9.6 MLflow & Weights & Biases for Experiment Tracking
  • 9.7 Monitoring and Scaling ML Systems

10. Cloud & Edge AI

  • 10.1 AI on Cloud (AWS SageMaker, GCP Vertex AI, Azure ML)
  • 10.2 Using GPUs and TPUs
  • 10.3 Edge AI (TinyML, TensorFlow Lite, NVIDIA Jetson)
  • 10.4 Serverless AI Architectures

11. Responsible AI & Ethics

  • 11.1 Fairness, Accountability, and Transparency
  • 11.2 Bias in Data and Models
  • 11.3 Privacy and Security in AI
  • 11.4 AI Regulation and Governance

12. Real-World Projects

  • 12.1 Predictive Analytics (Time Series Forecasting)
  • 12.2 Image Classification & Detection
  • 12.3 NLP Chatbot
  • 12.4 AI for Healthcare or Finance
  • 12.5 Recommender System
  • 12.6 Custom AI SaaS Product