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Ideal for learners who want a practical path from basics to intermediate topics used in ML/data-science workflows.\n\n## Quick links (open these files)\n\n- Phase notebooks\n  - [phase_1.ipynb](phase_1.ipynb) — basics: array creation, properties, reshaping, views (`arr`, `arr_1d`, `reshaped`)\n  - [phase_2.ipynb](phase_2.ipynb) — indexing, slicing, filtering (`numbers`, `mask`, `np.where`)\n- Core topics\n  - [Ufuncs.ipynb](Ufuncs.ipynb) — elementwise ops, broadcasting, `np.exp`, `np.sin`, etc.\n  - [broadcasting.ipynb](broadcasting.ipynb) — broadcasting rules and examples\n  - [Aggregation.ipynb](Aggregation.ipynb) — reductions (`np.sum`, `np.mean`, `np.argmax`, axis usage)\n  - [Adv_indexing.ipynb](Adv_indexing.ipynb) — fancy \u0026 boolean indexing\n  - [Linear_Algebra.ipynb](Linear_Algebra.ipynb) — matrix ops, `np.linalg` examples\n  - [view.ipynb](view.ipynb) — copy vs view semantics\n- File handling\n  - [file_handling/file_io.ipynb](file_handling/file_io.ipynb) — Python file read/write basics\n  - [file_handling/numpy_file_io.ipynb](file_handling/numpy_file_io.ipynb) — `np.save`, `np.load`, `np.savetxt`, `np.loadtxt` (`data_from_csv`)\n  - [file_handling/bonus_pythonic_iteration.ipynb](file_handling/bonus_pythonic_iteration.ipynb) — idiomatic file iteration\n  - Data files: [file_handling/sample_data.csv](file_handling/sample_data.csv), [file_handling/processed_output.txt](file_handling/processed_output.txt), [file_handling/my_data_file.npy](file_handling/my_data_file.npy), [file_handling/multiple_array.npz](file_handling/multiple_array.npz)\n\n## What you'll learn (high level)\n\n- Creating and inspecting arrays, shapes, dtypes, and basic sequences ([phase_1.ipynb](phase_1.ipynb))\n- Indexing: basic, fancy, boolean masks and `np.where` ([phase_2.ipynb](phase_2.ipynb), [Adv_indexing.ipynb](Adv_indexing.ipynb))\n- Elementwise ops / ufuncs and broadcasting ([Ufuncs.ipynb](Ufuncs.ipynb), [broadcasting.ipynb](broadcasting.ipynb))\n- Aggregations and axis semantics ([Aggregation.ipynb](Aggregation.ipynb))\n- Linear algebra primitives and verification ([Linear_Algebra.ipynb](Linear_Algebra.ipynb))\n- Views vs copies and memory implications ([view.ipynb](view.ipynb))\n- Saving/loading data with NumPy and text I/O examples (`np.save`, `np.load`, `np.savetxt`) — see [file_handling/numpy_file_io.ipynb](file_handling/numpy_file_io.ipynb) (`data_from_csv` example)\n\n## Quick start\n\n1. Clone the repo and create/activate a virtualenv.\n2. Install minimal deps:\n   ```sh\n   pip install numpy jupyter\n   ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhaabhijeet864%2Fnumpy_for_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjhaabhijeet864%2Fnumpy_for_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhaabhijeet864%2Fnumpy_for_machine_learning/lists"}