Free Course
AI Engineer
Learn to build, train and deploy AI models. From fundamentals to production-ready systems.
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> Curriculum
What You Will Learn
01
Introduction to AI & Machine Learning
01
Understand the fundamentals of artificial intelligence, the types of machine learning (supervised, unsupervised, reinforcement), and how AI is transforming industries. You'll learn the mathematical foundations including linear algebra, probability, and statistics that power modern AI systems.
Supervised LearningUnsupervised LearningReinforcement LearningLinear AlgebraProbability & StatisticsPython for ML
02
Neural Networks & Deep Learning
02
Dive deep into neural network architecture, from perceptrons to complex deep learning models. Learn about activation functions, backpropagation, gradient descent, and how to design networks for specific tasks. Build and train models using TensorFlow and PyTorch.
PerceptronsCNNsRNNsTransformersBackpropagationTensorFlowPyTorch
03
Natural Language Processing (NLP)
03
Explore how machines understand and generate human language. Cover tokenization, word embeddings, sequence models, attention mechanisms, and the transformer architecture that powers ChatGPT and modern language models.
TokenizationWord EmbeddingsAttention MechanismTransformersBERTGPT ArchitectureFine-tuning LLMs
04
Computer Vision
04
Learn how AI systems interpret visual data from the world. Cover image classification, object detection, image segmentation, and generative models. Understand architectures like ResNet, YOLO, and Stable Diffusion.
Image ClassificationObject DetectionSegmentationGANsResNetYOLOStable Diffusion
05
MLOps & Model Deployment
05
Bridge the gap between model development and production. Learn about model versioning, CI/CD for ML, containerization with Docker, serving models via APIs, monitoring model performance, and scaling with cloud infrastructure.
DockerModel ServingREST APIsCI/CD for MLModel MonitoringAWS/GCPKubernetes
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> What you will build
collect.py
import pandas as pd
df = pd.read_csv("data/reviews.csv")
print(df.shape, df.columns.tolist())ML Pipeline Architecture
The AI Engineer workflow from data to production
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[#build]
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[#artificial]
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[#intelligence]
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[#learn]
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[#build]
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[#artificial]
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[#intelligence]
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[#learn]
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[#build]