1-Month Machine Learning Learning Plan
Week 1 – NumPy (Foundation of ML)
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Install Python, Jupyter Notebook / VS Code.
Learn NumPy arrays: creation, shapes, indexing, slicing.
Understand broadcasting and vectorized operations.
Practice computing mean, variance, dot products, etc.
Mini-project: Analyze random exam scores using NumPy.
Week 2 – Pandas (Data Handling)
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Import pandas and explore Series, DataFrames.
Load data with read_csv(), read_excel().
Filter, sort, and summarize data with .describe() and .info().
Handle missing data using .isnull(), .fillna(), .dropna().
Mini-project: Clean a CSV dataset and summarize insights.
Week 3 – Matplotlib (Visualization)
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Learn basic plots: line, bar, scatter, and histograms.
Add labels, titles, legends, and customize styles.
Visualize DataFrames using df.plot().
Optional: Learn Seaborn for advanced visualizations.
Mini-project: Plot feature relationships (e.g., age vs income).
Week 4 – Scikit-learn (ML Basics)
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Load datasets using sklearn.datasets.
Split data into training/testing sets using train_test_split().
Train simple models: LinearRegression, KNeighborsClassifier.
Evaluate models with accuracy_score and confusion_matrix.
Final project: Build a full ML pipeline with data cleaning, visualization, and prediction.
Bonus Tips
1 Practice small coding exercises daily.
2 Use free datasets (Kaggle, UCI ML Repository).
3 Document your progress in a learning log or Notion page.
4 Experiment in Jupyter Notebooks regularly.