This course takes you from Python fundamentals to AI Agent development, covering core Python, NumPy, Pandas, SQL, Flask, FastAPI, LLMs, and open-source models via HuggingFace.
The author describes building a personal, open-source computational engine using Python libraries SymPy, NumPy, pandas, SciPy, statsmodels, Pingouin, Matplotlib, and Seaborn, effectively replicating the functionality of Wolfram Mathematica at no cost.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
>>> from sklearn.neighbors import NearestCentroid
>>> import numpy as np
>>> X = np.array( [-1, -1 » , -2, -1 » , -3, -2 » , 1, 1 » , 2, 1 » , 3, 2 » ])
>>> y = np.array( 1, 1, 1, 2, 2, 2 » )
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict( [-0.8, -1 » ]))
1 »
>>> import numpy as np
>>> X = np.array( [-1, -1 » , -2, -1 » , -3, -2 » , 1, 1 » , 2, 1 » , 3, 2 » ])
>>> y = np.array( 1, 1, 1, 2, 2, 2 » )
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict( [-0.8, -1 » ]))
1 »
