AI Expert Roadmap

AI Learning Roadmap

AI Expert Roadmap

1. Mathematical Foundations

1.1 Linear Algebra

  • Vectors and Matrices: Vector operations, dot products, matrix operations, special matrices.
  • Matrix Decompositions: Eigenvalues, eigenvectors, SVD, PCA.
  • Vector Spaces: Basis, dimension, linear transformations, norms, metrics.

1.2 Calculus

  • Differentiation: Partial derivatives, gradient, Jacobian, Hessian, chain rule.
  • Optimization: Gradient descent, constrained optimization, Lagrange multipliers.
  • Integration: Multiple integrals, line and surface integrals.

1.3 Probability and Statistics

  • Probability Theory: Random variables, probability distributions, expectation, variance.
  • Statistical Inference: MLE, Bayesian inference, hypothesis testing.
  • Statistical Models: Regression models, time series analysis, causal inference.

2. Machine Learning Fundamentals

2.1 Supervised Learning

  • Regression Algorithms: Linear regression, polynomial regression, regularization techniques.
  • Classification Algorithms: Logistic regression, SVM, decision trees, random forests.
  • Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curves, AUC, cross-validation.

2.2 Unsupervised Learning

  • Clustering: K-means, hierarchical clustering, DBSCAN.
  • Dimensionality Reduction: PCA, t-SNE, UMAP.
  • Anomaly Detection: Isolation forests, One-class SVM, autoencoders.

3. Deep Learning

3.1 Neural Networks Basics

  • Feedforward Networks: Neurons, activation functions, backpropagation, weight initialization.
  • Training Techniques: Optimization algorithms (SGD, Adam), regularization, learning rate schedules.
  • Frameworks: PyTorch, TensorFlow/Keras, GPU acceleration.

4. Natural Language Processing

4.1 Text Processing

  • Preprocessing Techniques: Tokenization, stemming, lemmatization, stop word removal.
  • Text Representation: Bag of words, TF-IDF, Word2Vec, GloVe.
  • Advanced Techniques: Named entity recognition, part-of-speech tagging, dependency parsing.

5. Computer Vision

5.1 Image Processing

  • Basic Operations: Filtering, convolution, edge detection, morphological operations.
  • Feature Extraction: SIFT, SURF, HOG features.
  • Traditional Approaches: Template matching, RANSAC, Hough transform.

6. AI Engineering and MLOps

6.1 Data Engineering

  • Data Collection: Web scraping, API integration, data labeling.
  • Data Processing: ETL pipelines, feature engineering, data augmentation.
  • Data Quality: Handling missing values, outlier detection, dataset balancing.

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