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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. Mathematics for AI

  • 2.1 Linear Algebra
    • Vectors
    • Matrices
    • Eigenvalues and eigenvectors
    • Matrix operations
  • 2.2 Calculus
    • Limits
    • Differentiation
    • Integration
    • Multivariable calculus
    • Vector calculus
  • 2.3 Probability and Statistics
    • Probability theory
    • Random variables
    • Probability distributions
    • Statistical inference
    • Bayesian statistics
  • 2.4 Optimization Techniques
    • Gradient Descent
    • Cost Functions

Resources:

Why learn this?

Mathematics is essential to understand how AI models learn and optimize themselves. These topics will help you understand the inner workings of learning algorithms.

3. Programming for AI

  • 3.1 Python
    • Basic syntax & Variables
    • Data structures
    • Control structures
    • Functions and modules
    • Object-oriented programming
  • 3.1 AI-related libraries
    • NumPy
    • Pandas
    • Data Visualization (Matplotlib, Seaborn)
    • TensorFlow
    • PyTorch
    • Jupyter Notebooks & Google Colab

Resources:

Why learn this?

Python is the most widely used programming language in AI. You need it to implement algorithms and work with data.

4. Machine Learning (ML)

  • 4.1 Supervised Learning
    • Linear Regression
    • Logistic Regression
    • Support vector machines
    • Decision Trees
    • Random forests
    • Gradient boosting machines
  • 4.2 Unsupervised Learning
    • Clustering (K-means, DBSCAN)
    • Dimensionality reduction (PCA, t-SNE)
    • Anomaly detection
  • 4.3 Reinforcement learning
    • Markov decision processes
    • Q-learning
    • Deep Q-networks
    • Policy gradients
    • Actor-critic methods
  • 4.2 Evaluation and validation
    • Training, validation, and test sets
    • Cross-validation
    • Model selection and hyperparameter tuning
    • Performance metrics

Why learn this?

This is where AI begins — enabling machines to learn patterns and make predictions.

Tools:

  • Scikit-learn,
  • Google Colab,
  • Real Datasets from Kaggle and UCI ML Repository

Resources:

Projects:

5. Deep Learning(DL)

  • 5.1 Neural networks
    • Multilayer perceptrons
    • Activation functions
    • Backpropagation
    • Optimization algorithms
  • 5.2 Convolutional neural networks
    • Convolutional layers
    • Pooling layers
    • Architectures (LeNet, AlexNet, VGG, ResNet)
  • 5.3 Recurrent neural networks (RNNs)
    • Long short-term memory (LSTM)
    • Gated recurrent units (GRU)
    • Sequence-to-sequence models
  • 5.4 Generative models
    • Variational autoencoders (VAE)
    • Generative adversarial networks (GAN)
    • Transformer models (BERT, GPT-2, T5)

Resources:

Projects:

6. Natural Language Processing (NLP)

  • 6.1 Text preprocessing
    • Tokenization
    • Stemming and lemmatization
    • Stopword removal
    • Part-of-speech tagging
  • 6.2 Feature extraction
    • Bag of words
    • TF-IDF
    • Word embeddings (Word2Vec, GloVe,FastText)
  • 6.3 Text classification
    • Sentiment analysis
    • Topic modeling
  • 6.4 Sequence Modeling (RNNs, LSTMs)
    • Named entity recognition
    • Text summarization
    • Machine translation
  • 6.5 Transformers (BERT, GPT, T5)
  • 6.6 Chatbots and Language Generation

Tools:

  • NLTK, spaCy, Hugging Face Transformers, TensorFlow, PyTorch

Projects:

  • Build a News Classifier using BERT
  • Chatbot with custom FAQ data using Transformers
  • Twitter Sentiment Classifier using Hugging Face pipeline

7. Computer Vision

  • 7.1 Image Preprocessing Techniques
    • Filtering techniques
    • Edge detection
    • Feature extraction
  • 7.2 CNN Architectures (VGG, ResNet, EfficientNet)
  • 7.3 Object Detection (YOLO, SSD, Faster R-CNN)
    • Sliding window approach
    • Region-based CNN (R-CNN)
    • YOLO (You Only Look Once)
  • 7.4 Image Segmentation (U-Net, Mask R-CNN)
    • Semantic segmentation
    • Instance segmentation
  • 7.5 Face Recognition, OCR
  • 7.6 Pose estimation
    • 2D pose estimation
    • 3D pose estimation

Tools:

  • OpenCV, TensorFlow, PyTorch, Keras, PIL

Projects:

  • Object detection in webcam feed
  • OCR number plate reader
  • Real-time face mask detection

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)

Tools:

  • Apache Airflow, Apache Kafka, DVC, Great Expectations, Spark (PySpark)

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

Projects:

  • Deploy a sentiment analysis model with FastAPI + Docker
  • Track model experiments with MLflow

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

Projects:

  • Deploy model to Vertex AI endpoint
  • Run image classifier on Jetson Nano using TensorFlow Lite

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

Platforms to Practice:

13. AI Books

14. Courses

  • 14.1 Machine Learning by Andrew Ng on Coursera
  • 14.2 Deep Learning Specialization by Andrew Ng on Coursera
  • 14.3 Applied Data Science with Python Specialization on Coursera
  • 14.4 Introduction to Artificial Intelligence with Python on edX

15. Articles

By completing this roadmap, you will not only understand how artificial intelligence works — but also build, deploy, and scale real-world AI applications confidently.