AI Engineer Lab Exercises

🔰 Basic Level (30+ Exercises)

 

Goal: Build foundational understanding of AI, Python programming, and core ML concepts.

 

Python for AI

  • Write Python programs using loops, lists, and dictionaries.

  • Develop functions and classes in Python.

  • Handle exceptions and file operations.

  • Use NumPy for array manipulation.

  • Use Pandas for data loading and analysis.

Data Visualization

  • Plot data using Matplotlib.

  • Create interactive plots with Plotly.

  • Build dashboards using Streamlit or Dash.

  • Visualize correlations using Seaborn heatmaps.

  • Display time-series trends and histograms.

Mathematics for AI

  • Implement linear algebra operations using NumPy.

  • Solve optimization problems with gradient descent.

  • Plot sigmoid, tanh, and ReLU functions.

  • Perform statistical analysis (mean, variance, std).

  • Build a basic matrix multiplication engine.

Intro to Machine Learning

  • Load datasets (Iris, Titanic) using Scikit-learn.

  • Split data into training/test sets.

  • Apply Linear Regression to predict values.

  • Train a classification model with Decision Trees.

  • Evaluate model accuracy using confusion matrix.

 

🚀 Intermediate Level (40+ Exercises)

 

Goal: Apply machine learning algorithms, work with real datasets, and start building intelligent systems.

 

Supervised Learning

  • Implement Logistic Regression for binary classification.

  • Train SVM on spam detection.

  • Apply Random Forest for customer churn prediction.

  • Use K-Nearest Neighbors for image classification.

  • Optimize models using GridSearchCV.

Unsupervised Learning

  • Apply K-Means clustering to customer segmentation.

  • Use PCA for dimensionality reduction.

  • Implement hierarchical clustering.

  • Detect anomalies using Isolation Forest.

  • Visualize clusters in 2D using T-SNE.

Natural Language Processing (NLP)

  • Tokenize and clean text using NLTK.

  • Build a sentiment analysis model using TF-IDF and Naive Bayes.

  • Use spaCy for named entity recognition (NER).

  • Create a chatbot with rule-based logic.

  • Generate word clouds from review data.

Deep Learning Foundations

  • Build neural networks using Keras or TensorFlow.

  • Implement forward and backward propagation.

  • Train a model for handwritten digit recognition (MNIST).

  • Use dropout to prevent overfitting.

  • Visualize training loss and accuracy.

 

🧠 Advanced Level (40+ Exercises)

 

Goal: Engineer intelligent AI systems using advanced tools, models, and deployment techniques.

 

Advanced Neural Networks

  • Implement CNN for image classification (CIFAR-10).

  • Build LSTM models for time-series forecasting.

  • Design a transformer model from scratch.

  • Apply attention mechanisms for translation tasks.

  • Use autoencoders for anomaly detection.

Generative AI

  • Build a GAN to generate handwritten digits.

  • Train a StyleGAN for facial synthesis.

  • Use DALL·E/Stable Diffusion APIs for text-to-image generation.

  • Generate music with RNN-based models.

  • Create prompts for ChatGPT and fine-tune a GPT model.

Reinforcement Learning

  • Build a Q-learning agent to play a simple grid game.

  • Train an agent with Deep Q Networks (DQN).

  • Simulate environments using OpenAI Gym.

  • Use policy gradient methods.

  • Analyze convergence and reward trends.

AI in Production

  • Save and load ML models using joblib or pickle.

  • Build REST APIs for model inference using FastAPI.

  • Containerize AI models with Docker.

  • Monitor AI models using Prometheus and Grafana.

  • Set up CI/CD pipelines for MLOps.

Capstone Projects

  • AI-powered chatbot with voice interface.

  • Recommendation system for movies or products.

  • Fraud detection system using supervised ML.

  • Face recognition attendance system.

  • AI assistant using NLP + voice commands.

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