AI Projects

Agentic 100

Hands on Coding Exercises on AI Agents

What you'll learn:

Tool-using agents with planning and memory
Multi-agent workflows and orchestration
Retrieval-augmented agents over your own data
Evaluation harnesses for agent reliability

PyTorch 100

Hands on Coding Exercises on PyTorch

PyTorch 100

Hands on Coding Exercises on PyTorch

What you'll learn:

Tensors, autograd and custom training loops
CNNs, RNNs and Transformers from scratch
Mixed-precision and distributed training
Exporting and serving trained models

LLM 100

Hands on Coding Exercises on Large Language Models

LLM 100

Hands on Coding Exercises on Large Language Models

What you'll learn:

Tokenizers and attention from first principles
Fine-tuning with LoRA and QLoRA
RAG pipelines and prompt engineering
Guardrails and evaluation for LLM apps

Vision 100

Hands on Coding Exercises on Computer Vision

Vision 100

Hands on Coding Exercises on Computer Vision

What you'll learn:

Image classification and detection pipelines
Segmentation and keypoint models
Vision transformers and fine-tuning
Deploying vision models to the edge

Agentic 100

Hands on Coding Exercises on AI Agents

Agentic 100

Hands on Coding Exercises on AI Agents

What you'll learn:

Tool-using agents with planning and memory
Multi-agent workflows and orchestration
Retrieval-augmented agents over your own data
Evaluation harnesses for agent reliability

PyTorch 100

Hands on Coding Exercises on PyTorch

PyTorch 100

Hands on Coding Exercises on PyTorch

What you'll learn:

Tensors, autograd and custom training loops
CNNs, RNNs and Transformers from scratch
Mixed-precision and distributed training
Exporting and serving trained models

LLM 100

Hands on Coding Exercises on Large Language Models

LLM 100

Hands on Coding Exercises on Large Language Models

What you'll learn:

Tokenizers and attention from first principles
Fine-tuning with LoRA and QLoRA
RAG pipelines and prompt engineering
Guardrails and evaluation for LLM apps

Vision 100

Hands on Coding Exercises on Computer Vision

Vision 100

Hands on Coding Exercises on Computer Vision

Explore all: AI-100 Projects

What you'll learn:

Image classification and detection pipelines
Segmentation and keypoint models
Vision transformers and fine-tuning
Deploying vision models to the edge

Hands-On AI Projects

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Explore all: Hands-On AI Projects

Face Recognition from scratch

Build a face-recognition pipeline end to end with embeddings and similarity search.

What you'll learn:

Detect and align faces in images
Train an embedding model with triplet loss
Build a nearest-neighbour matcher

Time-series Forecasting for Markets

Forecast financial time series with classical and deep-learning models.

What you'll learn:

Feature engineering for time series
ARIMA and Prophet baselines
Sequence models with LSTMs

Makeup Style Transfer

Transfer makeup styles between portraits with generative models.

What you'll learn:

Facial parsing and region masks
Style-transfer GAN architecture
Training and evaluating the model

Semantic Segmentation for Driving

Segment road scenes for autonomous driving with deep networks.

4.8(174 ratings)

What you'll learn:

Encoder-decoder segmentation models
Working with driving datasets
Measuring IoU and mAP

Face Recognition from scratch

Build a face-recognition pipeline end to end with embeddings and similarity search.

What you'll learn:

Detect and align faces in images
Train an embedding model with triplet loss
Build a nearest-neighbour matcher

Time-series Forecasting for Markets

Forecast financial time series with classical and deep-learning models.

What you'll learn:

Feature engineering for time series
ARIMA and Prophet baselines
Sequence models with LSTMs

Makeup Style Transfer

Transfer makeup styles between portraits with generative models.

Explore all: Hands-On AI Projects

What you'll learn:

Facial parsing and region masks
Style-transfer GAN architecture
Training and evaluating the model

Semantic Segmentation for Driving

Segment road scenes for autonomous driving with deep networks.

4.8(174 ratings)

What you'll learn:

Encoder-decoder segmentation models
Working with driving datasets
Measuring IoU and mAP

Explore by tasks:

Recommendation Systems

Object Detection

Recommendation Systems

Speech Recognition

Image Classification

Recommendation Systems

Text Generation

Community Favorite

Curious what's trending? Explore our most popular AI projects and AI-100 Series—proven favorites that deliver real results and help you master in-demand skills.

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Agentic AI-100

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8 Chapters

100 Exercises

4 Quizzes

All Levels

4.8 (245 ratings)

Community Favorite

Get a Certification

Start your interview preparation today with this AI-Project:

Face makeup applier (Computer Vision)

4.5

8 Chapters

4 Notebooks

4 Quizzes

Beginner

Community Favorite

Get a Certification

Agentic AI-100

Lorem ipsum dolor sit amet consectetur. Viverra fusce purus. Lorem ipsum dolor sit amet consectetur.

8 Chapters

100 Exercises

4 Quizzes

All Levels

4.8 (245 ratings)

Community Favorite

Get a Certification

Start your interview preparation today with this AI-Project:

Face makeup applier (Computer Vision)

4.5

8 Chapters

4 Notebooks

4 Quizzes

Beginner

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Build and train a neural network from scratch on a real dataset.

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Predict price movements and backtest a trading strategy.

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PyTorch 100

Hands on Coding Exercises on PyTorch

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Hands on Coding Exercises on PyTorch

What you'll learn:

Custom training loops
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Serving trained models

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Transfer makeup styles between portraits with generative models.

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Training and evaluation

LLM 100

Hands on Coding Exercises on Large Language Models

LLM 100

Hands on Coding Exercises on Large Language Models

What you'll learn:

Fine-tuning with LoRA
RAG pipelines
Guardrails and eval

Neural Networks, End to End, Neural Networks, End to End

Build and train a neural network from scratch on a real dataset.

What you'll learn:

Forward and backprop by hand
Optimizers and regularization
Evaluating your model

Algorithmic Trading with ML

Predict price movements and backtest a trading strategy.

What you'll learn:

Market feature engineering
Model training and validation
Backtesting a strategy

PyTorch 100

Hands on Coding Exercises on PyTorch

PyTorch 100

Hands on Coding Exercises on PyTorch

What you'll learn:

Custom training loops
Transformers from scratch
Serving trained models

Makeup Style Transfer

Transfer makeup styles between portraits with generative models.

What you'll learn:

Facial parsing and masks
Style-transfer GANs
Training and evaluation

LLM 100

Hands on Coding Exercises on Large Language Models

LLM 100

Hands on Coding Exercises on Large Language Models