AI & Machine Learning Engineer

From Math Foundations to Production AI

A complete journey from mathematical foundations through deep learning frameworks to building and deploying production AI applications — covering linear algebra, neural networks, NLP, and real-world AI systems.

8Series
128Articles
80Hours
All Learning Paths
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0 / 128 articles
Math for AI
AI & Data Science
Neural Networks
PyTorch Mastery
TensorFlow Mastery
Natural Language Processing
AI Application Development
AI in the Wild
Step 1 — 20-Part Series

Math for AI

Linear algebra, calculus, probability, statistics, and optimization for machine learning.

Step 2 — 11-Part Series

AI & Data Science

Python setup, NumPy, Pandas, Matplotlib, scikit-learn, and data science workflows.

Step 3 — 9-Part Series

Neural Networks

Perceptrons, backpropagation, CNNs, RNNs, attention mechanisms, and architectures.

Step 4 — 14-Part Series

PyTorch Mastery

Tensors, autograd, nn.Module, training loops, distributed training, and deployment.

Step 5 — 14-Part Series

TensorFlow Mastery

Keras API, custom training, TF Serving, TFLite, and production pipelines.

Step 6 — 16-Part Series

Natural Language Processing

Tokenization, word embeddings, transformers, BERT, GPT, and text generation.

Step 7 — 20-Part Series

AI Application Development

Building AI-powered applications — RAG, agents, prompt engineering, and MLOps.

Step 8 — 24-Part Series

AI in the Wild

Real-world AI deployments — case studies, ethics, bias, regulation, and societal impact.