This is a learning roadmap for anyone who want to be a ML Engineer. I created it from perspective of full-stack developer with no experience with ML and Python. Goals I had in mind when I created this roadmap ## Level 0: Python & math essentials _<-- I am here_ **Goal:** Learn just enough Python and math to understand and write ML code. ### Topics - [Python basics (variables, functions, loops, classes, data structures, testing)](https://github.com/Frodigo/garage/blob/main/Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/python-tutorial-notes.ipynb) ✅ - [NumPy](https://github.com/Frodigo/garage/blob/main/Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/numpy/numpy-essentials.ipynb) ✅ - pandas - Matplotlib & Seaborn for visualization - Basic linear algebra, probability, statistics ### Mini-projects - [Basic Python Programs](./level-0-python-and-math-essentials/basic-python-programs/README.md) - Text Analyzer — show list comprehensions, lambda functions, text operations - Task Manager CLI app — mix object-oriented and procedural styles - Backup Automation Script — work with files and directories - Calculator for stats — demonstrate NumPy and descriptive statistics - Weather App — connect to a weather API and show JSON parsing - CSV/PDF Report Generator — automate report formatting - Note Manager GUI — use Tkinter for building a visual tool - Random Test Data Generator — generate data for testing other projects ### Resources - [Python Tutorial](https://docs.python.org/3/tutorial/index.html) ✅🏁💯 - [A Byte of Python" free online book](https://python.swaroopch.com) - [Obey the Testing Goat!" free online book](https://www.obeythetestinggoat.com/pages/book.html#toc) - [Crafting Test-Driven Software with Python: Write test suites that scale with your applications' needs and complexity using Python and PyTest](https://www.amazon.com/Crafting-Test-Driven-Software-Python-applications-ebook/dp/B08PP4FT2B) --- ## Level 1: your first ML project **Goal:** Build a full beginner ML pipeline using a structured dataset. ### Topics - What is ML? Types of learning - ML pipeline: data → model → prediction - Scikit-learn: train/test split, fit, predict - Evaluation: accuracy, confusion matrix ### Mini-projects - Predict flower species (Iris) - Classify Titanic survivors - Build a spam email detector - Automate markdown summaries from email text (custom dataset) - Use CLI tool to classify text (combine ML + CLI app) --- ## Level 2: Data Understanding & Feature Engineering **Goal:** Learn to explore and clean datasets, and create useful features. ### Topics - Exploratory Data Analysis (EDA) - Feature types, missing values, outliers - Encoding categorical variables - Normalization, standardization ### Mini-projects - Clean and analyze a housing dataset - Feature engineer from scraped product prices - Analyze financial data with pandas and visualize trends - Web scraper for products — collect training data yourself --- ## Level 3: Algorithms and models **Goal:** Understand how different ML algorithms work and when to use them. ### Topics - Supervised: Logistic regression, Decision Trees, k-NN, SVM - Unsupervised: Clustering (k-means), Dimensionality Reduction - Model selection, bias/variance tradeoff ### Mini-projects - Compare models (logistic vs. tree vs. SVM) - Tune a Decision Tree and explain feature splits - Cluster product prices and show in 2D plot - Create a template engine with test case generator to explore logic programming --- ## Level 4: Model tuning & evaluation **Goal:** Learn how to optimize your models and avoid overfitting. ### Topics - Cross-validation - Grid search and random search - ROC curves, AUC, F1 score - Overfitting and underfitting detection ### Mini-projects - Tune spam detector with grid search - Build a multithreaded file downloader that logs performance - Apply window functions to time series of sensor or stock data - Create a basic unit test framework to wrap your pipelines --- ## Level 5: Build your own ML tool **Goal:** Apply ML to a real problem with a full end-to-end solution. ### Topics - Problem scoping and framing - Data collection and cleaning - Deployment basics (e.g. Flask, Streamlit) - Communicating results and business value ### Mini-projects - Streamlit app: sentiment analyzer or price predictor - Flask API for ML model serving - Django mini blog with prediction plugin - AI-powered chatbot with basic NLP features --- ## Level 6: Deep Learning Foundations **Goal:** Dive into neural networks and build intuition for deep models. ### Topics - Perceptron, activation functions, backpropagation - Keras/TensorFlow for building neural networks - CNNs and RNNs intro ### Mini-projects - Digit classifier with MNIST - Image classifier for product categories - RNN-based sentiment analysis - Intro chatbot with sequence modeling - Experiment with simple AI modules in games --- ### Level 7: Real-World ML & next steps **Goal:** Expand your skills and apply ML to domains of interest. ### Topics - Transfer learning, pre-trained models - ML in production (monitoring, updating models) - Ethics and bias in ML - Optional: NLP, recommender systems, time series ### Mini-projects - Reuse pretrained models from Hugging Face or TensorFlow Hub - Build a monitoring system for API predictions - Explore transformer summarization on email thread dataset - Create a lightweight dashboard that tracks ML metrics --- #AI #AIgeneratedContent