python-for-ai-ml

🐍 Python for AI/ML — AIOps Edition

A structured, hands-on learning repository for mastering Python fundamentals and the core data science stack — purpose-built for AIOps practitioners who want to apply ML/AI techniques to IT operations, monitoring, anomaly detection, and automation workflows.

📌 Why This Repository?

AIOps demands more than just scripting knowledge. This repository bridges the gap between Python basics and production-ready data science skills, using real-world AIOps scenarios like:

Log parsing and event correlation
Time-series analysis of infrastructure metrics
Anomaly detection in server and application data
Automating operational insights with ML

Whether you’re an SRE, DevOps engineer, or platform engineer stepping into AI/ML — this is your guided path.

📁 Repository Structure

python-for-ai-ml/
│
├── 📂 1-python-foundation/
│   ├── 1-python-basics-conditionals-loops.ipynb
│   ├── 2.1-lists-loops-methods-continue-break.ipynb
│   ├── 2.2-lists-methods-sorting-slicing.ipynb
│   ├── 3-tuple-concatenation-lists.ipynb
│   ├── 4-functions.ipynb
│   ├── 5-oops-itserverclass.ipynb
│   ├── 6-stringmethods-logparsing.ipynb
│   ├── 7.1-dictionaries-loops-functions.ipynb
│   ├── 7.2-dictonaries-methods.ipynb
│   ├── 8-list-comprehension.ipynb
│   ├── 9-lists-functions-dictionaries-combine.ipynb
│   ├── 10-file-Handling.ipynb
│   ├── 11-exception-handling.ipynb
│   └── 12-modules.ipynb
│
├── 📂 2-numpy/
│   ├── 1-numpy-arrays.ipynb
│   ├── 2-numpy-indexing-slicing.ipynb
│   ├── 3-numpy-mathoperations-aggregations.ipynb
│   ├── 4-numpy-booleanlogic-filtering.ipynb
│   ├── 5-numpy-reshaping-dimensions.ipynb
│   ├── 6-numpy-broadcasting.ipynb
│   ├── 7-numpy-statistics.ipynb
│   ├── 8-numpy-synthetic-it-metrics.ipynb
│   └── 9-numpy-anomaly-threshold-logic.ipynb
│
├── 📂 3-pandas/
│   ├── 1-pandas-series-dataframe.ipynb
│   ├── 2-pandas-loading-inspection.ipynb
│   ├── 3-pandas-filtering-selection.ipynb
│   ├── 4-pandas-cleaning-missing.ipynb
│   ├── 5-groupby-aggregation.ipynb
│   ├── 6-pandas-merge-join.ipynb
│   ├── app_logs.csv
│   ├── incidents.csv
│   └── server_metrics.csv
│
└── 📂 4-scikit-learn/              # 🚧 Coming Soon
    └── ...

📚 Module Breakdown

🔹 1. Python Foundation

Build a solid Python base with patterns commonly used in AIOps tooling, scripting, and automation — all grounded in IT operations contexts.

Notebook	Topics Covered	AIOps Context
`1-python-basics-conditionals-loops`	Variables, conditionals, loops	Alert routing and threshold checks
`2.1-lists-loops-methods-continue-break`	List traversal, loop control	Iterating over log entries
`2.2-lists-methods-sorting-slicing`	Sorting, slicing, list methods	Ranking incidents by severity
`3-tuple-concatenation-lists`	Tuples, immutability, combining collections	Immutable config records
`4-functions`	Function definition, scope, return values	Reusable monitoring utilities
`5-oops-itserverclass`	Classes, objects, inheritance	Modeling servers and services as objects
`6-stringmethods-logparsing`	String ops, regex, formatting	Parsing application and system logs
`7.1-dictionaries-loops-functions`	Dict iteration, functional patterns	Processing structured event data
`7.2-dictonaries-methods`	Dict methods, nested dicts	Managing config maps and metadata
`8-list-comprehension`	Comprehensions, filtering, transforms	Compact log filtering pipelines
`9-lists-functions-dictionaries-combine`	Combining data structures	Building incident summary reports
`10-file-Handling`	Reading/writing files, CSV, JSON	Loading log files and config dumps
`11-exception-handling`	Try/except, custom exceptions	Resilient pipelines that don’t crash in prod
`12-modules`	Importing, `__init__`, packaging	Structuring reusable AIOps toolkits

🔹 2. NumPy

Efficient numerical computing applied to raw telemetry, server metrics, and time-series operational data.

Notebook	Topics Covered	AIOps Context
`1-numpy-arrays`	Array creation, dtypes, attributes	Storing metric snapshots as arrays
`2-numpy-indexing-slicing`	Indexing, slicing, fancy indexing	Extracting CPU/RAM time windows
`3-numpy-mathoperations-aggregations`	Arithmetic, sum, mean, min, max	Computing utilization statistics
`4-numpy-booleanlogic-filtering`	Boolean masks, dynamic thresholds	Anomaly detection on server metrics
`5-numpy-reshaping-dimensions`	Reshape, transpose, dimensions	Structuring multi-server metric matrices
`6-numpy-broadcasting`	Broadcasting, baselines, z-scores	Normalizing and comparing metrics per server
`7-numpy-statistics`	Moving averages, sliding correlations	CPU trend analysis and pattern detection
`8-numpy-synthetic-it-metrics`	Simulating CPU, RAM, disk, network data	Generating realistic test datasets
`9-numpy-anomaly-threshold-logic`	Static vs dynamic thresholds, consensus methods	Multi-method anomaly detection logic

🔹 3. Pandas

Data wrangling with real-world operational datasets — application logs, server metrics, and incident records.

Datasets used in this module:

File	Description
`app_logs.csv`	Application log events with timestamps, levels, and services
`server_metrics.csv`	CPU, memory, disk metrics with duplicates and missing values
`incidents.csv`	Incident records for cross-dataset correlation and reporting

Notebook	Topics Covered	AIOps Context
`1-pandas-series-dataframe`	Series, DataFrames, indexing	Core structures for tabular ops data
`2-pandas-loading-inspection`	read_csv, dtypes, info, describe	Loading and profiling log exports
`3-pandas-filtering-selection`	Boolean filters, loc, iloc, query	Isolating critical-severity events
`4-pandas-cleaning-missing`	Nulls, IQR outlier removal, category normalization	Cleaning inconsistent metric records
`5-groupby-aggregation`	groupby, agg, error code frequency	Finding frequent errors, excluding noise
`6-pandas-merge-join`	merge, join, full cross-dataset pipeline	Building a complete server reliability report

🔹 4. Scikit-learn (Coming Soon)

The next module will cover supervised and unsupervised ML for AIOps, including:

Anomaly detection with IsolationForest and LocalOutlierFactor
Alert classification with decision trees and random forests
Clustering services for topology grouping
Root cause analysis with feature importance
Model evaluation against operational SLAs

⭐ Star this repo to get notified when the Scikit-learn module drops!

🧭 Learning Path

1-python-foundation  ──▶  2-numpy  ──▶  3-pandas  ──▶  4-scikit-learn (🚧)
         │                    │               │                  │
   Scripting &            Numerical       Wrangling           ML Models
   Automation             Computing       Ops Data            for AIOps

Each module is grounded in real AIOps datasets and scenarios — no toy examples.

🚀 Getting Started

Prerequisites

Python 3.10 or higher
Jupyter Notebook or JupyterLab
pip or conda

Installation

# Clone the repository
git clone https://github.com/raviadapa-ai/python-for-ai-ml.git
cd python-for-ai-ml

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate        # Linux/macOS
venv\Scripts\activate           # Windows

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter lab

`requirements.txt`

numpy>=1.26
pandas>=2.0
matplotlib>=3.7
seaborn>=0.12
jupyterlab>=4.0
scikit-learn>=1.4    # for upcoming module

🚫 Contributions

This repository is currently a personal learning and portfolio project.

External contributions are not being accepted at this time.

However, you’re welcome to:

Fork the repository
Use it for your own learning
Adapt ideas for your own projects

For questions or discussions, feel free to open an issue.

📄 License

This project is licensed under the MIT License.

🙌 Acknowledgements

Inspired by the growing need for ML literacy in the SRE and platform engineering communities. Built for practitioners who live in terminals, dashboards, and incident bridges — and want AI to work for them.

Made with ❤️ for the AIOps community