40 Practical Python Scripts for AI Beginners

Welcome back to your favorite section on https://aizonex.blogspot.com. Today, we’re bringing you 40 Practical Python Scripts for AI Beginners that you can start using in your work. They’re simple examples for now, but this is just the beginning. Of course, in just one month, this site will be fully packed with projects you can completely rely on for your work. And don’t forget to follow our Facebook page at https://www.facebook.com/profile.php?id=61579052772784 because what’s coming next is truly amazing!

1. Print Current Time


from datetime import datetime
print(datetime.now())

Explanation: Useful for logging when your AI program runs.

2. Create a Sequential Array


import numpy as np
arr = np.arange(10)
print(arr)

Explanation: Quickly generates test data for AI projects.

3. Calculate Standard Deviation


import numpy as np
data = [1,2,3,4,5]
print(np.std(data))

Explanation: Measures data spread, important for preprocessing.

4. Generate Identity Matrix


import numpy as np
print(np.eye(4))

Explanation: Common in linear algebra for AI algorithms.

5. Load Iris Dataset


from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data[:5])

Explanation: Classic dataset for learning classification.

6. Plot Iris Dataset


import matplotlib.pyplot as plt
plt.scatter(iris.data[:,0], iris.data[:,1])
plt.show()

Explanation: Visualizes feature relationships.

7. Decision Tree Classifier


from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(iris.data, iris.target)

Explanation: Train a basic decision tree classifier.

8. Predict with Decision Tree


print(model.predict([[5.1,3.5,1.4,0.2]]))

Explanation: Test the trained model on new input.

9. View Decision Tree Structure


from sklearn import tree
print(tree.export_text(model))

Explanation: Understand how your model makes decisions.

10. Confusion Matrix


from sklearn.metrics import confusion_matrix
print(confusion_matrix(iris.target, model.predict(iris.data)))

Explanation: Evaluate classification accuracy.

11. Confusion Matrix Heatmap


import seaborn as sns
sns.heatmap(confusion_matrix(iris.target, model.predict(iris.data)), annot=True)
plt.show()

Explanation: Easier-to-read evaluation results.

12. PCA for Dimensionality Reduction


from sklearn.decomposition import PCA
pca = PCA(n_components=2)
reduced = pca.fit_transform(iris.data)
print(reduced[:5])

Explanation: Speeds up training by reducing features.

13. Plot PCA Results


plt.scatter(reduced[:,0], reduced[:,1], c=iris.target)
plt.show()

Explanation: Shows data clusters in fewer dimensions.

14. Load MNIST Dataset


from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
print(mnist.data.shape)

Explanation: Famous dataset for digit recognition.

15. Train SVM on MNIST


from sklearn.svm import SVC
clf = SVC()
clf.fit(mnist.data[:1000], mnist.target[:1000])

Explanation: Classify images with a powerful algorithm.

16. Test SVM Model


print(clf.predict([mnist.data[1001]]))

Explanation: Predict on unseen handwritten digit.

17. One-Hot Encoding


import pandas as pd
df = pd.DataFrame({'color': ['red','blue','green']})
print(pd.get_dummies(df))

Explanation: Convert text categories into numbers.

18. Normalize Data


from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaled = scaler.fit_transform([[10],[20],[30]])
print(scaled)

Explanation: Scale values between 0 and 1.

19. Standardize Data


from sklearn.preprocessing import StandardScaler
std_scaled = StandardScaler().fit_transform([[10],[20],[30]])
print(std_scaled)

Explanation: Makes data have mean 0 and variance 1.

20. Histogram Plot


plt.hist(iris.data[:,0], bins=10)
plt.show()

Explanation: See how values are distributed.

21. Simple Neural Network


import tensorflow as tf
model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

Explanation: Foundation for deep learning projects.

22. Compile TensorFlow Model


model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Explanation: Prepares the model for training.

23. Train Neural Network


model.fit(mnist.data[:5000], mnist.target[:5000], epochs=5)

Explanation: Improves accuracy with training cycles.

24. Evaluate Neural Network


print(model.evaluate(mnist.data[5000:6000], mnist.target[5000:6000]))

Explanation: Tests model on unseen data.

25. Add Dropout Layer


model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
])

Explanation: Prevents overfitting during training.

26. Load & Preprocess Image


import tensorflow as tf
import numpy as np
img = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(28,28), color_mode='grayscale')
arr = np.array(img)/255.0
print(arr.shape)

Explanation: Prepare images for neural networks.

27. Early Stopping


callback = tf.keras.callbacks.EarlyStopping(patience=3)

Explanation: Stops training when performance stops improving.

28. Save Best Model Only


checkpoint = tf.keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True)

Explanation: Keeps only the top-performing model.

29. Convert Array to DataFrame


df = pd.DataFrame(np.random.rand(5,3), columns=list('ABC'))
print(df)

Explanation: Organizes raw data into table format.

30. Group Data


print(df.groupby('A').mean())

Explanation: Summarizes data by key feature.

31. Covariance Matrix


print(np.cov(np.random.rand(3,5)))

Explanation: Understand variable relationships.

32. Boxplot


import seaborn as sns
sns.boxplot(x=iris.data[:,0])
plt.show()

Explanation: Detects outliers in data.

33. Hyperparameter Tuning (GridSearchCV)


from sklearn.model_selection import GridSearchCV
param_grid = {'max_depth': [3,5,7]}
grid = GridSearchCV(DecisionTreeClassifier(), param_grid)
grid.fit(iris.data, iris.target)
print(grid.best_params_)

Explanation: Finds the best model parameters.

34. Cross-Validation


from sklearn.model_selection import cross_val_score
print(cross_val_score(DecisionTreeClassifier(), iris.data, iris.target, cv=5))

Explanation: Reliable model performance testing.

35. ROC Curve


from sklearn.metrics import roc_curve
fpr, tpr, _ = roc_curve([0,0,1,1],[0.1,0.4,0.35,0.8])
plt.plot(fpr, tpr)
plt.show()

Explanation: Evaluates binary classifiers.

36. AUC Score


from sklearn.metrics import roc_auc_score
print(roc_auc_score([0,0,1,1],[0.1,0.4,0.35,0.8]))

Explanation: Single score for classifier quality.

37. Heatmap of Large Data


sns.heatmap(np.random.rand(10,10), cmap='coolwarm')
plt.show()

Explanation: Visualizes data correlations.

38. Random Forest Classifier


from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(iris.data, iris.target)

Explanation: Strong ensemble classifier.

39. Feature Importance


print(rf.feature_importances_)

Explanation: Shows which features matter most.

40. Save Model as Pickle


import pickle
with open('model.pkl','wb') as f:
    pickle.dump(rf,f)

Explanation: Save and reload models easily.