Weeks 1–2: Python, Core Data Science Libraries & ML
Basics
● Topics:
● Python programming (data types, modules, functions, classes, error
handling).
● Essential data libraries: pandas, numpy, matplotlib, seaborn.
● Scikit-learn basics: Supervised vs unsupervised learning, regression,
classification.
● Quick math brush-up: Linear algebra and stats for ML intuition.
● Projects:
● Analyze a public dataset (e.g., movies, weather).
● Data visualization gallery: Create compelling, relaxing visuals.
● Build & evaluate a simple classifier or recommender (e.g., predict movie
genre).
● Resources: Interactive courses on DataCamp, Udemy, or GeeksforGeeks; “Python
for Data Analysis” by Wes McKinney; Corey Schafer & Keith Galli YouTube series.
Weeks 3–5: Classic Machine Learning Projects, API
Deployment, React Native Integration
● Topics:
● ML workflow: EDA, preprocessing, model selection, feature engineering.
● Popular algorithms: Logistic regression, decision trees, random forest,
k-means, SVM.
● Model evaluation: Cross-validation, confusion matrix, ROC curves.
● Model deployment: Build REST API with FastAPI or Flask.
● Integration: Connect ML APIs to your React Native mobile frontend
projects.
● Projects:
● Text sentiment analyzer for app reviews (API + frontend).
● Recommendation engine (e.g., personalized news or products).
● API deployment: Host an ML model and consume from your app.
● Resources: “API Development: Build a Predictive Machine Learning Site With
React and Python”; mini-courses on ML pipeline deployment; YouTube project
walkthroughs.
