Hour 1 – AI Research & Coding (Python Libraries)

• By Engr. Waqar Qayyoom Khokhar
• August 20, 2025August 20, 2025
• 30 Days CEO Mastery Program, Artificial Intelligence, Content Writing, Deep Learning, Hour 1 – Morning Mindset & Energy, Libraries, Machine Learning

🔹 Expanded into 7 Subtasks Each (with AI Prompts)

1. Setup environment

1. Install Python (Anaconda/Miniconda).
2. Create virtual environment.
3. Configure Jupyter Notebook / VS Code.
4. Setup Git for version control.
5. Verify GPU/CPU availability.
6. Prompt: “Write a bash script to automatically create a Python virtual environment with common ML libraries.”
7. Prompt: “How do I check CUDA compatibility with PyTorch in my system?”

2. Install core libraries

1. Install numpy, pandas, matplotlib, scikit-learn.
2. Install PyTorch or TensorFlow.
3. Install OpenCV for image tasks.
4. Install HuggingFace Transformers.
5. Test imports with sample code.
6. Prompt: “Suggest lightweight Python libraries for ML suitable for low-resource laptops.”
7. Prompt: “Generate a requirements.txt file with the best versions for stability in ML projects.”

3. Data preprocessing pipeline

1. Load dataset into pandas.
2. Handle missing values (mean, median, drop).
3. Encode categorical variables.
4. Scale/normalize numerical features.
5. Save cleaned dataset.
6. Prompt: “Write sklearn pipeline code that includes imputation, encoding, and scaling.”
7. Prompt: “Which preprocessing steps improve performance for tree-based models like XGBoost?”

4. Exploratory Data Analysis (EDA)

1. Generate descriptive statistics.
2. Plot histograms & boxplots.
3. Create correlation heatmap.
4. Identify outliers.
5. Write a summary report.
6. Prompt: “Generate Python code to auto-generate an EDA report using pandas-profiling.”
7. Prompt: “How can I detect hidden patterns or seasonality in time-series data?”

5. Train baseline ML model

1. Split dataset into train/test.
2. Train Logistic Regression model.
3. Evaluate accuracy/precision/recall.
4. Compare with dummy classifier.
5. Save baseline metrics.
6. Prompt: “Generate baseline ML models for both classification and regression datasets.”
7. Prompt: “How do I decide if my baseline accuracy is meaningful compared to random chance?”

6. Image classification demo

1. Load CIFAR-10 or MNIST dataset.
2. Build CNN with Keras/PyTorch.
3. Train for 5 epochs.
4. Plot accuracy/loss curves.
5. Test on sample images.
6. Prompt: “Write PyTorch code for transfer learning on ResNet50 for CIFAR-10.”
7. Prompt: “Suggest ways to improve CNN accuracy without making it too heavy.”

7. NLP basics

1. Tokenize sample text.
2. Remove stopwords/punctuation.
3. Perform lemmatization/stemming.
4. Create word frequency visualization.
5. Save preprocessed dataset.
6. Prompt: “Write Python code for a TF-IDF vectorizer with n-grams.”
7. Prompt: “Explain the difference between stemming and lemmatization with examples.”

8. Sentiment analysis model

1. Collect IMDB/Twitter dataset.
2. Preprocess text (cleaning/tokenizing).
3. Train Naive Bayes / LSTM.
4. Evaluate with F1 score.
5. Test on custom sentences.
6. Prompt: “Generate PyTorch LSTM code for sentiment classification.”
7. Prompt: “How can I handle sarcasm in sentiment analysis datasets?”

9. HuggingFace Transformers

1. Install transformers library.
2. Load pre-trained BERT model.
3. Tokenize dataset with tokenizer.
4. Fine-tune for classification.
5. Save model + tokenizer.
6. Prompt: “Write HuggingFace pipeline code for zero-shot text classification.”
7. Prompt: “Explain how to reduce GPU memory usage when training BERT.”

10. Fine-tune text classification

1. Select dataset (AG News / Yelp).
2. Preprocess text & labels.
3. Fine-tune DistilBERT.
4. Evaluate accuracy on test set.
5. Save & deploy model.
6. Prompt: “Write HuggingFace Trainer code for fine-tuning DistilBERT.”
7. Prompt: “Which hyperparameters affect Transformer fine-tuning the most?”

11. Recommendation engine basics

1. Load user-item dataset.
2. Create user-item interaction matrix.
3. Implement popularity-based recommender.
4. Implement collaborative filtering.
5. Evaluate with precision@k.
6. Prompt: “Generate Python code for a content-based recommender using cosine similarity.”
7. Prompt: “What are the differences between collaborative filtering and hybrid recommenders?”

12. Collaborative filtering demo

1. Load MovieLens dataset.
2. Create user similarity matrix.
3. Implement KNN-based recommender.
4. Test recommendations for 1 user.
5. Save results.
6. Prompt: “Write matrix factorization code using SVD for recommendations.”
7. Prompt: “How can I deal with the cold-start problem in recommenders?”

13. OpenCV image processing

1. Load sample image.
2. Convert to grayscale.
3. Apply edge detection (Canny).
4. Perform image transformations (resize, rotate).
5. Display results in grid.
6. Prompt: “Generate Python code for face detection using OpenCV Haar cascades.”
7. Prompt: “What are modern alternatives to OpenCV for image processing tasks?”

14. Object detection (YOLOv8)

1. Install Ultralytics YOLO.
2. Load pre-trained YOLO model.
3. Run inference on sample image.
4. Fine-tune with custom dataset.
5. Save trained model.
6. Prompt: “Generate YOLOv8 training config for a custom dataset of 3 classes.”
7. Prompt: “What are best practices for annotating datasets for object detection?”

15. Speech recognition demo

1. Install SpeechRecognition library.
2. Load audio file.
3. Convert speech → text.
4. Evaluate accuracy.
5. Test with live microphone input.
6. Prompt: “Write Python code for speech recognition using Google Speech API.”
7. Prompt: “How can I improve accuracy of speech recognition in noisy environments?”

16. Chatbot with Rasa

1. Install Rasa framework.
2. Create intents & entities.
3. Train dialogue model.
4. Test in shell mode.
5. Deploy simple bot locally.
6. Prompt: “Generate sample nlu.yml and stories.yml files for a customer service bot.”
7. Prompt: “How to connect a Rasa bot with WhatsApp or Telegram?”

17. TensorFlow Lite model

1. Train small TF model.
2. Convert model to TFLite format.
3. Optimize with quantization.
4. Test inference on mobile emulator.
5. Compare size vs accuracy.
6. Prompt: “Write Python code to quantize a CNN to INT8 for TFLite.”
7. Prompt: “What are limitations of TensorFlow Lite compared to full TensorFlow?”

18. Flask API deployment

1. Setup Flask project.
2. Create /predict route.
3. Load trained ML model.
4. Send JSON input & return prediction.
5. Test with Postman.
6. Prompt: “Generate Flask REST API for a scikit-learn regression model.”
7. Prompt: “What security best practices should I follow for Flask APIs?”

19. FastAPI deployment

1. Setup FastAPI project.
2. Create /predict endpoint.
3. Load trained model.
4. Add Swagger UI documentation.
5. Deploy with Uvicorn.
6. Prompt: “Generate FastAPI code for async inference with a PyTorch model.”
7. Prompt: “How does FastAPI compare with Flask for ML model deployment?”

20. Dockerize ML model

1. Write Dockerfile.
2. Install dependencies inside container.
3. Copy model into container.
4. Expose API port.
5. Run container locally.
6. Prompt: “Generate a Dockerfile for FastAPI with GPU-enabled PyTorch model.”
7. Prompt: “What’s the difference between Docker and Kubernetes for ML deployment?”

21. Edge AI deployment

1. Select lightweight model (MobileNet).
2. Optimize model for edge.
3. Convert to ONNX/TFLite.
4. Deploy on Raspberry Pi/Jetson.
5. Test inference speed.
6. Prompt: “Generate Python code to run ONNX models on Jetson Nano.”
7. Prompt: “What are the hardware limitations of deploying AI on Raspberry Pi?”

22. Monitoring ML models

1. Setup logging for predictions.
2. Track model accuracy drift.
3. Monitor API latency.
4. Setup alert system.
5. Generate monthly reports.
6. Prompt: “Write Python code for monitoring ML drift using EvidentlyAI.”
7. Prompt: “What are the best open-source tools for ML observability?”

23. AutoML (PyCaret)

1. Install PyCaret.
2. Load dataset into PyCaret.
3. Run setup() for classification.
4. Compare multiple models.
5. Deploy best model.
6. Prompt: “Generate PyCaret code for regression AutoML.”
7. Prompt: “What are the limitations of AutoML vs manually tuned ML models?”

24. AI ethics & bias check

1. Load dataset & check fairness.
2. Calculate demographic parity.
3. Run bias detection tools.
4. Suggest bias mitigation strategies.
5. Document ethical risks.
6. Prompt: “Generate Python code to check bias using AIF360 toolkit.”
7. Prompt: “What are real-world cases where biased AI models failed businesses?”

25. AI + Business integration demo

1. Select business case (e-commerce, health, finance).
2. Map AI task to ROI benefit.
3. Create workflow diagram.
4. Build small prototype.
5. Present demo report.
6. Prompt: “Suggest AI use cases for e-commerce marketplaces to increase revenue.”
7. Prompt: “How can AI adoption improve customer trust and reduce fraud?”

• Share
• Share
• Pin